esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of the Influence of Segmented Rollers on Slab Bulge Deformation
## 4 Comparisons of Slab Bulge Deformations with Different Segment Rollers
### Analysis of Segmented Roller's Stiffness  
The stiffness of the casting roller was an important factor affecting slab bulge deformation. In the bulging models, the vertical displacement of the elastic casting roller had occurred, which was selected to measure the stiffness of the casting roller. The results are shown in Figure 9. The vertical displacement of the elastic solid roller was a reverse parabola distribution. The maximum value of 0.14 mm appeared in the center of the solid roller. The rigid roller was not deformed, so the vertical displacement was 0 mm. The vertical displacement of the two-segment roller was evenly distributed around 0.004 mm. The displacement of the three-segment roller in the vertical direction was generally distributed around 0.0015 mm. The average vertical displacement of the roller was selected for comparison to intuitively analyze the stiffness of the casting rollers. The average vertical displacement of the elastic solid roller was 0.07 mm, the average vertical displacement of the two-segment roller was 0.004 mm, and that of the three-segment roller was 0.0015 mm. The stiffness of the two-segment roller was 17.5 times greater than that of the solid roller, and the stiffness of the three-segment roller was 46.7 times greater than that of the solid roller. As the number of sections increased, the deflection of the segment roller during the continuous casting process was smaller. This indicated that the stiffness of the segmented roller was effectively improved. Combined with the previous analysis of casting slab deformation, the conclusion could be drawn that the stiffness of the segmented roller could be better improved than that of the solid casting roller, and the bulge deformation of the slab could also be excellently reduced.  
Figure 8: Bulge deformation of the slab on the narrow side with different casting rollers.
## 3 Data Processing
### Defect-to-Background Ratio  
The defect-to-background ratio \(DBR\) is introduced in order to quantify the increase in amplitude at the defect relative to the surrounding damage-free material. The \(DBR\) is calculated at each frequency \(f\):  
\[DBR^{*}(f)=\frac{\Omega_{healthy}}{\Omega_{defect}}\ \frac{\sum\limits_{i=1}^{n_{ defect}}V_{Z}^{*}(x_{i},y_{i,}f)}{\sum\limits_{i=1}^{n_{healthy}}V_{Z}^{*}(x_{i},y_{i,}f)} \tag{5}\]  
where \(\Omega_{defect}\) is the known defected area that contains \(n_{defect}\) measurement points and \(\Omega_{healthy}\) is the surrounding healthy area with \(n_{healthy}\) measurement points. \(V_{Z}^{*}(x_{i},y_{i},f)\) is the frequency-specific velocity amplitude at location \((x_{i},y_{i})\), which may correspond to any of the filtered components discussed in the previous section, for instance the second harmonic component \(V_{Z}^{*}=V_{Z}^{HH2}\). Thus, the \(DBR\) equals the average amplitude of the (filtered) vibration velocity at the defect's location compared to the average amplitude of the (filtered) vibration velocity at the remainder of the coupon.
# AUV Trajectory Planning for Optimized Sensor Data Collection in Internet of Underwater Things
## 2 Materials and Methods
### System Model
#### 2.1.1 Network Architecture  
The UASN architecture used in this study is illustrated in Figure 1[1]. In such a network, \(U\) sensor nodes, labeled as \(j=1,2,\ldots,U\), are randomly distributed within a 3D underwater area. These sensor nodes collect environmental data and forward it via acoustic signals to neighboring nodes or directly to the AUV [1]. The sensor nodes are generally assumed to be stationary, with their positions known using underwater localization techniques [1; 9; 37]. Additionally, the seafloor on which the nodes are deployed is uneven, leading to variations in their individual depth values.  
To facilitate communication, a clustering method is applied to group the sensor nodes into \(M\) clusters. One node in each cluster is designated as a cluster head (CH), forming a set of \(M\) CHs labelled as \(i=1,2,\ldots,M\)[5]. The set of CHs can be reselected in each data collection round to balance energy consumption among nodes, using methods such as those presented in [6; 7; 13; 38; 39]. Each CH collects data from the sensor nodes within its cluster and transmits it to the AUV. Given the focus on AUV trajectory planning, this paper considers only CH-AUV communication and assumes that data transmission from sensor nodes to CHs has already been completed.  
The AUV navigates along a horizontal plane above the CHs to collect their aggregated data. Upon mission completion, the AUV offloads the collected data to a USV, which serves as the surface sink [1]. The USV then relays the data to a ground control station using radio communication. This study specifically focuses on the data transmission process from the underwater CHs to the USV [1].
## 2 Status and Literature Review
### Domestic and Overseas Design-for-Safety Review
#### 2.3.5 Korea--Design-for-Safety  
Domestic design-for-safety was introduced in 2017 through related laws and guidelines [9; 10]. This concept was introduced in accordance with the existing concept of design-for-safety, which extends the scope of the existing safety management measures to the design stage, but due to its hasty introduction, the utilization in actual construction projects is low [31]. One reason for this is that the distributed guidelines are ambiguous and do not directly replace the construction project's safety management work. For example, in overseas construction projects, they provide a risk assessment method, a hazard recognition method, and a hazard checklist for each disaster to provide a consistent design-for-safety review task. Thus, quantitative and qualitative evaluation of safety management elements that could not be reviewed in the existing design stage is achieved. Instead, the design-for-safety review in Korea provides instructions on the work based on the participant's occupation in the design stage and an evaluation system for risk assessment, but the content is not clear. As an example, in the case of the risk assessment coefficient table and the evaluation template provided for risk assessment, the evaluation system provides \(4\times 5\) and \(4\times 4\) matrices, but this is subject to change according to experts (contractors, supervisors, etc.), and is not clearly defined [9]. In the absence of items related to hazards, the missing factors are determined by the supervisor or expert.  
In addition, the domestic design-for-safety review requires a separate evaluation of the control measures, and the details of the evaluation criteria and methods for the control measures are left to the contractor and designer, so the benefit of the evaluation is low. These problems make it difficult to apply what is currently aimed at the domestic design-for-safety review manual in the field. As a result, only the evaluation of design alternatives occurs in the actual reviews, and the risk evaluations are omitted. Thus, the evaluation is different from the one intended in the design-for-safety review task manual. This is because there are too many items required in the risk assessment process of other design-for-safety reviews. In the case of Singapore and Hong Kong, the risk assessment items required in the design-for-safety review process only require clarification of hazards, risk ratings, control measures, reduced risk ratings, and related personnel. However, in the case of design-for-safety reviews in Korea, additional results (in addition to the above items) must be prepared for the risk assessment results such as human/physical damage, control measures by third parties, whether to review, and whether additional work such as reports for review are necessary. Further, evaluations and results for control measures are required. This means that there are more items to be prepared than design-for-safety reviews in other countries, and it also means that there are insufficient guidelines.
# Simulation of the Influence of Wing Angle Blades on the Performance of Counter-Rotating Axial Fan
## 2 Numerical Calculation Model and Calculation Method
### Experimental Verification  
Since the counter-rotating fan for mining was a press-in fan, the GB/T 1236-2000 Type B device was selected to collect data related to the total pressure Pt and efficiency \(\eta\) of the fan, and then the accuracy of the numerical calculation results was experimentally verified.  
Figure 2 is a test platform for a counter-rotating axial flow fan. The wind resistance of the fan can be changed by adjusting the distance between the cone-shaped restrictor in Figure 2b and the outlet of the test air tube to achieve the purpose of imposing different loads on the fan, and then it provided conditions for testing the fan under full flow conditions.  
As shown in Figure 3, the collected results were compared with the numerical calculation results. It can be seen from the figure that the trends of the full pressure \(Pt\) and efficiency \(\eta\) curves of the simulation and experiment were basically the same. Additionally, the average deviations of the total pressure Pt and efficiency \(\eta\) of simulation and experiment were 1.75% and 0.60%, respectively; the relative deviations at the rated flow point were about 1.44% and 0.01%, and both were within 5%. This showed that the reliability of modeling, meshing and calculation method settings was high, and the numerical calculation results could reflect the actual operation of the wind turbine.  
Figure 2: Test platform for contra-rotating axial fan. (**a**) Counter-rotating fan and test air duct; (**b**) tapered restrictor.
## 3 DksA/ppGpp and TraR  
DksA is a global regulator of gene expression in many bacteria [47; 48]. Acting in concert with the alarmone ppGpp, DksA is involved in both positive and negative regulation of the expression of many genes during the stringent response [32; 49; 50]. DksA/ppGpp are best known for shutting down ribosomal transcription during starvation [16]. It has been demonstrated that DksA decreases the life-time of the open complex on the _rrnB_ P1 promoter in _E. coli_[16]. Alternatively, DksA/ppGpp can directly stimulate transcription of some genes, such as those required for _de novo_ amino acid biosynthesis [32]. To explain these opposing effects on transcription, it was proposed that DksA and ppGpp act synergistically by decreasing the energy barrier for open complex formation and simultaneously reducing open complex stability, which might stimulate or inhibit initiation, the outcome depending on intrinsic kinetic properties of a given promoter [32].  
An alternative model for DksA and ppGpp was proposed for _Pseudomonas aeruginosa_, where it was observed that DksA, in synergy with ppGpp, actually increases the binding of RNAP to ribosomal promoters _in vitro_, presumably forming inactive complexes [51]. Moreover, in many cases where DksA and ppGpp act together (either to induce or repress transcription) the absence of ppGpp can be compensated for by overproduction of DksA, suggesting that they could act independently [52]. In some cases DksA and ppGpp behave antagonistically, for example in the regulation of the _E. coli__fimB_ promoter [53]. Recently evidence has grown to suggest that DksA can also act as an elongation factor. In support of this hypothesis, a genome-wide localization study of DksA revealed that it was distributed along transcribed sequences, presumably being associated with elongating RNAP, rather than concentrated at the promoter regions [54]. The role of DksA/ppGpp in elongation will be discussed in more detail below.  
Conjugative plasmids often encode a DksA homolog, TraR, which regulates the extracytoplasmic stress response. Structural prediction of TraR indicates the presence of a single alpha helix, corresponding to half of the coiled-coil domain, which can protrude into the active center of RNAP. Unlike DksA, TraR does not require ppGpp for regulation of rRNA transcription, suggesting that it can mimic the combined activity of DksA and ppGpp on its own [17].
## 3 Results
### Quantitative Interpretation of LOC Classification Model  
To investigate how word-level information in the text contributes to the classification model, we used the LIME package, which provides insights into the keywords that are most influential for the prediction model. Figure 4 illustrates the impact of the vocabulary used in the data on the predicted group classifications. In the C2 (head injuries but no LOC) group, vocabulary related to head injury is particularly significant (Figure 4A). In contrast, the C3 (head injuries with LOC) group primarily focuses on words directly associated with LOC rather than head injury, resulting in relatively straightforward decisions compared to the C2 group (Figure 4B). The results of LIME on the original version of Korean are shown in Figure S2.  
Interestingly, while both the C2 and C3 groups share words related to LOC, they are classified as C2 when the context includes a denial of LOC (Figure 4). These findings suggest that the model is capable of making contextual judgments regarding head injuries and levels of consciousness.
## 4 Frozen Wall Elastic-Plastic Problem
### Discussion on Stability Evaluation of Horizontal Frozen Wall under Uneven Load  
Based on the abovementioned analysis of the influence of the lateral pressure coefficient \(\lambda\) and vertical external load \(p_{0}\) on the plastic zone contour and range of the tensile stress zone of the horizontal frozen wall, the following observations can be made:  
1. When \(\lambda\)<0.485, the tensile stress zone is clearly observable at the inner edge of the frozen wall in the vertical direction and its range is only related to \(\lambda\); in particular, it increases with a decrease in \(\lambda\), but is independent of the magnitude of \(p_{0}\). Furthermore, \(p_{0}\) only affects the magnitude of the tensile stress in the stress zone, but does not affect its range. Moreover, a reasonable vertical direction freezing reinforcement should be performed to increase the radius of the frozen wall and avoid tensile failure of the inner edge of the frozen wall.
2. When \(0.485<\lambda<0.610\), there is no tensile stress zone on the frozen wall. In such cases, the plastic failure of the horizontal inner edge under low load and inner edge in the direction 30-60\({}^{\circ}\) (the other three quadrants are similar in terms of the radius of the plastic zone) under high load should be avoided.
3. When \(\lambda>0.610\), there is no tensile stress zone as in the previous case, and the plastic zone in the \(0^{\circ}\) and \(180^{\circ}\) directions has the largest radius, indicating the weakest positions of the plastic wall of the frozen wall. Thus, the compressive plastic failure of the inner edge of the frozen wall in the horizontal direction should be avoided.  
For circular horizontal freezing engineering, in practice, \(\lambda\) should first be determined using an in situ stress test to judge whether tensile stress is present in the frozen wall, and the corresponding tensile failure of the tensile stress zone should be determined according to the tensile strength of the frozen soil obtained via the frozen soil mechanics test. By substituting the external load into the plastic contour equation of the frozen wall, the plastic zone can be determined; consequently, the maximum position of the plastic zone can be obtained. Finally, the freezing scheme can be improved based on the determined weak areas.
# Patient-Specific Dosimetry Evaluations in Theranostics Software for Internal Radiotherapy
## 1 Introduction
It can perform structure-based calculations (according to MIRD/RADAR schema, which is also adopted by OLINDA), and voxel-based dosimetry calculations. Velocity Theranostics adopts a novel absorbed dose calculation algorithm based on Linear Boltzmann transport equation. This approach can be considered as a gold standard method, since it incorporates high accuracy calculations equivalent to MC methods, but with faster computational speed (similar to the speed of kernel convolution methods). In this study, Velocity Theranostics performances were compared with OLINDA 2.0 [16]. Dose calculations were performed using images of phantoms and patients.
## 3 Results and Discussion
### Characterization Analysis and Mechanism
#### 3.3.2 XRD Analysis  
XRD patterns of milled samples before and after MC treatment are presented in Figure 4. The peaks corresponding to the crystal planes 003, 101, and 104 represent the layer structure of LiCoO\({}_{2}\). It is obvious that the intensity of these peaks weakened gradually with prolonged ball milling time, indicating the destruction of the crystal structure of LiCoO\({}_{2}\). Accordingly, the structure of Fe reflected by the diffraction peak of 110 crystal plane, which disappeared after milling, and the new peaks of 211 crystal plane corresponding to Fe\({}_{2}\)O\({}_{3}\) were observed, showing the oxidation of Fe(0) to Fe(III). Fe(0) acted as a reducing reagent to reduce BDE 209 during the MC process. The grain size of 003 and 110 peaks decreased from about 1000 nm to 453 and 243 nm after 240 min grinding, respectively, which also indicated the decrease of LiCoO\({}_{2}\) and Fe (Figure S4). Furthermore, the FWHM of the two main crystal planes, 003 and 110, broadened with the increase of MC reaction time (Figure S5). Results suggest that the crystal structure of LiCoO\({}_{2}\) and Fe was gradually converted to an amorphous state within the process. The disorder degree (Figure S5), representing the degree of amorphousness, became higher with MC treatment time from 0 to 240 min, very consistent with the results of FWHM.
# A New Method for Detecting Onset and Offset for Singing in Real-Time and Offline Environments
## 3 The Proposed Algorithm
### Stretching Pitch Contour  
Since humans' vocal pitch range is wide, generally from 77 to 900 Hz [39], calculating significant changes occurring on pitch contour has some difficulty. For example, the slope of the line when moving from the note \(E2\approx 82\) Hz to the note \(F2\approx 87\) Hz is much less than when it moves from the note \(E5\approx 659\) Hz to the note \(F5\approx 698\) Hz. Therefore, to counteract any adverse effect of this wide pitch frequency range on the slopes, the F0s are _stretched_ to be on the almost same pitch frequency range.  
Figure 2 plots two estimated pitch contours (panels a and b) and the stretched version of them (panels c and d, respectively). As depicted in Figure 2, although (a) and (b) are in different pitch frequency ranges, after stretching, the slopes between notes in both (c) and (d) are almost similar.  
The following formulas, Equations (2) and (3), are used to implement the stretch.  
\[\max=\left\{\begin{array}{ll}F0_{i},&F0_{i}>max\\ max,&otherwise\end{array}\right. \tag{2}\]  
\[F0_{i}=\frac{F0_{i}+Threshold}{max}, \tag{3}\]  
where the variable \(max\) holds the maximum \(F0\) estimated until index \(i-1\), and the constant value _Threshold_ holds the maximum possible \(F0\). Since the maximum pitch frequencies of the singers in both datasets mentioned above are less than 1000 Hz, for this study, 1000 Hz is considered as the _Threshold_. In Equation (2), if the current \(F0\), \(F0_{i}\), is more than the \(max\) variable, Equation (3) should be run for all the \(F0\) from index 0 to index \(i-1\).  
Figure 1: The main steps to find onsets in the proposed algorithm.
# Autonomous Visual Navigation System Based on a Single Camera for Floor-Sweeping Robot
## 3 Key Technologies  
### Garbage Identification and Target Detection  
Different from the garbage in the street, ground garbage is mostly paper scraps, dust, and wrapping paper. The small size, inconspicuousness and scattered distribution of indoor  
Figure 3: Autonomous visual navigation method for sweeping robot based on superpixel segmentation.  
garbage make it more difficult to identify. Traditional target detection methods use manual extraction of image feature information, which cannot avoid incomplete feature information in this process, thus leading to poor recognition results. In contrast, neural networks and deep learning can solve these problems well. [PERSON] et al. [21] proposed a new system to identify illegal dumping in mobile vehicles by using video analysis, which is based on the Haar cascade classifier, background subtraction, and OpenALPR library to identify illegal dumping in a changing light environment. [PERSON] et al. [22] used target box dimensional clustering and classification network pre-training to improve the YOLOv2 network model for spam detection and ported it to an embedded module to achieve lightweight features. [PERSON] et al. [23] proposed a hierarchical deep-learning method for waste detection and classification in food trays. It takes advantage of Faster R-CNN to achieve support for waste classification tasks in high-resolution bounding boxes. [PERSON] et al. [24] used the SIFT algorithm to extract labels to recognize the organic and non-organic elements. This paper uses a target detection algorithm based on deep learning techniques. Several familiar image processing methods are shown in Table 1. The deep learning-based target detection algorithm has a significant advantage in accuracy and better generalization performance.  
In our research, the target detection SSD algorithm [20] was used to detect garbage and achieve the active localization of ground targets. SSD used a CNN for detection, which employed a multi-scale feature map. It used VGG16 [32] as the base model, eliminating the fully connected layers and adding multiple convolutional layers. The core of the SSD algorithm includes the use of multi-scale feature maps, convolutional neural networks, and setting prior frames to apply to the detection of small targets.
# Improving Decodability of Polar Codes by Adding Noise
## 4 Proposed Decoding Algorithms
### PDNE Decoding Algorithm
2: \(\mathrm{\hat{\alpha}}_{1}^{\mathrm{N}}\leftarrow\mathrm{Polar\_decoder}( \mathrm{y}^{\mathrm{N}})\)
3: if \(\mathrm{CRC}(\mathrm{\hat{\alpha}}_{1}^{\mathrm{N}})==\) success
4: break
5: else
6: while i \(\leq\mathrm{T}_{1}\) do
7: \(\mathrm{y}_{1}^{\mathrm{N}}=\mathrm{y}^{\mathrm{N}}+\mathrm{Q}_{\mathrm{P}} \cdot\mathrm{randn}(1,\mathrm{N})\)
8: \(\mathrm{\hat{\alpha}}_{1}^{\mathrm{N}}\leftarrow\mathrm{Polar\_decoder}( \mathrm{y}_{1}^{\mathrm{N}})\)
9: \(i\leftarrow i+1\)
10: if \(\mathrm{CRC}(\mathrm{\hat{\alpha}}_{1}^{\mathrm{N}})==\) success
11: break
12: end if
13: end if
14: while j \(\leq\mathrm{T}_{\mathrm{s}}\) do
15: Choose \(\mathrm{n}_{\mathrm{n}}^{(\mathrm{j})}\in\mathrm{S}_{\mathrm{n}}^{*}\)
16: \(\mathrm{y}_{2}^{\mathrm{N}}=\mathrm{y}^{\mathrm{N}}+\mathrm{n}_{\mathrm{n}}^{ (\mathrm{j})}\)
17: \(\mathrm{\hat{\alpha}}_{1}^{\mathrm{N}}\leftarrow\mathrm{Polar\_decoder}( \mathrm{y}_{2}^{\mathrm{N}})\)
18: \(\mathrm{j}\leftarrow j+1\)
19: if \(\mathrm{CRC}(\mathrm{\hat{\alpha}}_{1}^{\mathrm{N}})==\) success
20: break
21: end if
22: end while
23: end if
24: Return \(\mathrm{\hat{\alpha}}_{1}^{\mathrm{N}}\)
```  
**Algorithm 2**PDNE Decoding algorithm