esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Resonator Design
### Design Summary  
The butterfly-shaped coil topology was selected for the given NFC interface design, for the reason as described in Section 3.1. A primary and secondary data coil were redesigned for \(r_{\text{b},1}=15\) mm and \(r_{\text{b},2}=5\) mm to match the external dimensions of the energy coils, resulting in the overall coil system of Figure 6. Using a 13.56 MHz carrier and requiring a 6.78 MHz symbol rate, we request the transient response to settle within half of a carrier cycle (\(\
u=0.5\)), which implies a quality factor of the data coil of \(\omega_{0}L_{\text{c}}/R_{\text{c}}=\pi\), as given by (19). To assure that the optimal load resistance \(R_{\text{L,par},i}\) lies in the range from 50 to 200 \(\Omega\), which is feasible to be matched to a receiving amplifier, the coils should have a self-inductance \(L_{i}\) in the range from 170 to 680 nH according to (21), which can be realized with three turns each (recall the self-inductance from (7)). The coils' pitch \(p_{i}\) was arbitrarily chosen to be 0.3 mm for the primary and 0.15 mm for the secondary coil, while the trace width \(w_{i}\) was dimensioned with the resistance model from [28] to obtain an ohmic coil resistance resistance well below 1/10 of the required optimal series resistance \(R_{\text{c,opt}}\), which is then defined by an additional lumped resistor on the circuit board of the coil. The final design parameters of the resonators are compiled in Table 1.  
Figure 6: Setup of the overall coil system: A wireless power transfer link is realized by two planar spiral coils, while the data link consists of two additional butterfly-shaped coils.
# Advances in Finite Element Modeling of Fatigue Crack Propagation
## 3 Advanced Techniques in Finite Element Modeling for Fatigue Crack Propagation
### Dual Boundary Element Method (DBEM)  
DBEM combines the advantages of boundary element methods with finite elements to model crack propagation in complex geometries. This method is particularly effective for problems with infinite or semi-infinite domains. The DBEM has emerged as a powerful alternative approach for fatigue crack growth analysis, offering several advantages over conventional FEM. The key features of the DBEM for fatigue crack growth simulation include accurate stress intensity factor calculations, efficient crack growth modeling, handling of complex crack geometries, Adaptive Mesh Refinement capabilities, and flexibility in coupling with other methods [145; 146]. The DBEM formulation inherently captures the crack tip stress singularity, allowing for highly accurate computation of the SIFs governing crack growth [147; 148]. The boundary-only discretization of DBEM reduces the dimensionality of the problem, leading to fewer degrees of freedom compared to domain-based FEM. This results in lower computational costs for modeling crack initiation, propagation, and coalescence [149; 150]. The DBEM can effectively handle components with multiple cracks, holes, or other complex geometrical features, which are common in engineering structures. The boundary-only discretization simplifies the modeling of these intricate crack configurations [151; 152]. The DBEM can be coupled with Adaptive Mesh Refinement techniques to focus computational resources on the evolving crack front, further enhancing the efficiency of fatigue crack growth simulations [153; 154; 155]. This method can also be seamlessly integrated with other numerical techniques, such as the FEM or the XFEM, to leverage their respective strengths and overcome their individual limitations.
## 4 Network Node Bandwidth Allocation Method  
In this section, we introduced the bandwidth allocation algorithm on network nodes and the bandwidth allocation algorithm for subflows when facing multipath transmission.
## 5 Squashing Operation of Specialised Slicing
### Slicing Algorithm for Special Features  
In the manufacturing process, the slicing algorithm is the most important operation to maintain accuracy of the AM process. The algorithm described here as shown in Algorithm 1 with notations on Table 1 uses a squashing operation to reduce the amount of gaps caused by the fluctuation of accumulated approximated sliced layers. This algorithm is able to keep fixed tolerance allowance and manage the empty space defined by the designer. Example of the algorithm applied on a single slice is described on Figure 10 and the overall specific procedure of the algorithm are shown in Figure 11.  
Gradient interpolation is conducted during the slicing process. It does not use the approximated representation data format in the design phase but performs last minute modification to maximise accuracy in machine tool performance. The algorithm for building a squashing operation consists of five stages: (1) First, definition of the slicing plane which, with the layer thickness defines the slicing block. (2) Next, intersection using a Boolean operation between the slicing block and the desired object to calculate the actual slicing geometry of the layer. (3) Projection of all edges in the calculated slice along the slicing direction of each layer. Each projected edge becomes a portion of the slice boundary. The projected edges are then combined and the interior edges are removed to make the final profile. (4) Trimming of the profiles to remove dangling edges and ensure that the ge  
Figure 10: Procedure of squashing operation on single slice: (**a**) select a shape to be squashed in the slice layer, (**b**) project all edges of a shape on the slice layer, (**c**) merge the edges and time the internal handling edges, and (**d**) extrude merged edges into a shell.  
Figure 9: 2D operations of additive manufacturing.  
ometry is closed and manifold. Example of the non manifold cases are shown in Figure 12. (5) Extrusion of the projected profiles along the slicing direction.  
\begin{table}
\begin{tabular}{l l} \hline \(O\) & Original Shape \\ \(S\) & Solid plane \\ \(n\) & Number of Slice \\ \(V\) & Slicing Direction \\ \(s_{n}\) & Duplicate \(S\), \(n\) times by \(V\) direction \\ \(I_{n}\) & Intersection Part of \(O\) and \(s_{n}\) (\(O\cap s_{n}\)) \\ \(P_{n}\) & Projection result of \(I_{n}\) to \(S\) \\ \(E_{n}\) & Extrude \(P_{n}\) by direction \(V\) \\ \hline \end{tabular}
\end{table}
Table 1: Notations of the proposed squashing operation.  
Figure 11: Squashing Operation Process: (**a**) Original Shape and solid plane, (**b**) Duplicated solid planes by vertical direction, (**c**) Intersection part, (**d**) Projection, (**e**) Extrusion, (**f**) Final Shape.
###### Abstract  
In this paper, we investigate pulsed eddy current (PEC) testing based on a rectangular sensor for the purpose of defect shape mapping in electric vehicle lightweight alloy material. Different dimensional defects were machined on the 3003 aluminum alloy and detected using the A-scan technique and C-scan imaging in two scanning directions. The experiment results indicated that defect plane shape could be preliminarily obtained and length and width could be estimated based upon C-scan contour images. Consequently, the comparison of results between the two directions showed that the C-scan identification in the direction of magnetic flux was better than in the direction of the exciting current. Finally, subsurface defects and irregular defects were detected to verify the performance of shape mapping as a recommended approach. The conclusion drawn indicates that the proposed method, based on PEC rectangular sensors, is an effective approach in reconstructing a defect's shape.  
Keywords:nondestructive testing; pulsed eddy current; alloy material; C-scan imaging; defect shape mapping; rectangular coil +
Footnote †: journal: Applied Sciences
# Design of a Wideband Antenna for Wireless Network-On-Chip in Multimedia Applications
## 3 Antenna Characterization  
With reference to the antenna whose layout is depicted in Figure 1, Figure 3 shows the performance achieved in terms of radiation pattern along the X-Y plane. The relevant half-power beamwidth (HPBW) angle is also shown: the HPBW along the X-Y plane is 110 degrees. Instead, the HPBW along the X-Z plane is 65 degrees.  
It is to be noted that for WiNoC applications, where a processing core has to be connected with other processor cores or memory tiles, placed on the same plane, the radiated power of interest is that along the X-Y plane. The power radiated along the Z-axis would be just wasted. This is why the antenna  
Figure 2: Side view (in the case of a Co-planar Stripline (CPS) implementation) of the adopted single-chip multi-layer structure.  
is designed to be directional in its X-Y radiation pattern, featuring a large HPBW of 110 degrees in both directions of positive and negative values of the X axis. The only application where radiation along the \(Z\) axis would be useful is in the case of a WiNoC communication in a multi-core system-on-chip fabricated using 3D assembly technology. In such a case, the wireless link is also between cores aligned in the vertical direction, and not only between cores placed in the same plane. However, the use of WiNoC technology in the framework of 3D integration technology is out of the scope of this paper.  
At 60 GHz, the performance in terms of S-parameters has a maximum gain (from CST simulations) of about \(-7\) dBi and a return loss of \(-39.5\) dB. The percentage radiation efficiency \(\eta\) is about 20%. In the state-of-the-art, the antenna bandwidth capability is calculated as the frequency range where the return loss parameter is below \(-10\) dB [74]. Figure 4 shows the S11 antenna performance vs. frequency. From the results in Figure 4, the return loss is always below \(-10\) dB in the range of 51 GHz to 100 GHz, hence an antenna bandwidth capability from 51 GHz to 100 GHz is determined. Frequency values above 100 GHz are not taken into account in this work, since above 100 GHz the CMOS technology is not a suited technology for the integration of the transceivers. Indeed, above 100 GHz, the gain values of the low noise amplifier (LNA) and of the power amplifier (PA) will be too low, and the receiver noise figure (NF) will be too high. Figure 5 shows the 3D radiation pattern and the antenna gain as a function of frequency, at 50 GHz, 60 GHz and 100 GHz, considering a direction of 0 degrees in the X-Y plane in Figure 3.  
Figure 4: Return loss of the antenna as a function of frequency.  
Figure 3: Antenna radiation pattern at 60 GHz along the X–Y plane.
# Research on Springback Compensation Method of Roll Forming Based on Improved Fuzzy PID Control
## 2 System Modeling Based on Springback Compensation Strategy
### Electromechanical Control Model  
To effectuate the control of the motor rotation angle difference, an electromechanical control model for the circular rolling mechanism is established. The armature circuit of the circular roll forming mechanism is depicted in Figure 4.  
Let \(u\) denote the voltage, \(E\) the back electromotive force, \(L\) the inductance, \(R\) the resistance, \(J\) the moment of inertia of the mechanical rotating part of the motor (1), \(B\) the  
Figure 4: Control armature circuit.  
Figure 3: Horizontal adjustable diagram. 1—screw, 2—slide block.  
viscous friction coefficient of the rotating part of the motor (1), \(K_{a}\) the torque constant, and \(K_{b}\) the back electromotive force constant. The current \(i\), the rotational speed \(\omega\), and the rotation angle \(\theta\) of the motor (1) are selected as state variables:  
\[\left\{\begin{array}{l}x_{1}=i\\ x_{2}=\omega\\ x_{3}=\theta\end{array}\right. \tag{7}\]  
The derivatives with respect to time are as follows:  
\[\left\{\begin{array}{l}\frac{dx_{1}}{dt}=\frac{di}{dt}\\ \frac{dx_{2}}{dt}=\frac{d\omega}{dt}\\ \frac{dx_{3}}{dt}=\frac{d\theta}{dt}\end{array}\right. \tag{8}\]  
From the armature circuit equation, we have  
\[L\frac{di}{dt}+Ri+E=u \tag{9}\]  
From the dynamic equation, we have  
\[J\frac{d\omega}{dt}+B\omega=K_{a}i \tag{10}\]  
From the electromagnetic induction relationship, we have  
\[E=K_{b}\omega \tag{11}\]  
Sorting out the above formula, we obtain  
\[\left\{\begin{array}{l}\frac{di}{dt}=-\frac{R}{t}i-\frac{K_{b}}{t}\omega+ \frac{1}{t}u\\ \frac{d\omega}{dt}=\frac{K_{a}}{t}i-\frac{B}{t}\omega\\ \frac{d\theta}{dt}=\omega\end{array}\right. \tag{12}\]  
By substituting \(x_{1}=i\), \(x_{2}=\omega\), and \(x_{3}=\theta\) into the equation, we obtain  
\[\left(\begin{array}{l}\dot{x_{1}}\\ \dot{x_{2}}\\ \dot{x_{3}}\end{array}\right)=\left(\begin{array}{ccc}-\frac{R}{t}&-\frac{K _{b}}{t}&0\\ \frac{K_{a}}{t}&-\frac{B}{t}&0\\ 0&1&0\end{array}\right)\left(\begin{array}{l}x_{1}\\ x_{2}\\ x_{3}\end{array}\right)+\left(\begin{array}{l}\frac{1}{t}\\ 0\\ 0\end{array}\right)u \tag{13}\]  
The output equation is obtained:  
\[y=x_{3}=\left(\begin{matrix}0&0&1\end{matrix}\right)\left(\begin{array}{l}x _{1}\\ x_{2}\\ x_{3}\end{array}\right) \tag{14}\]  
Assume that the parameter values in the state equation are as shown in Table 1.
## 2 Materials and Methods  
### Materials  
The cells used in this study are the macrophage Raw264.7 (No. IIB-71) and fibroblast L929 (No. CCL-1) cells, both of which were obtained from the American Type Culture Collection (ATCC, Manassas, VA, USA). MSM was supplied by Sunny Rise Pharmachem Ltd. (Wanchai, Hong Kong). Dulbecco's modified Eagle's medium (DMEM; SH30243.01), fetal bovine serum (FBS; SF0005005), penicillin-streptomycin solution (SH40003.01), and trypsin (SH30042.02) were purchased from Hyclone Laboratories (Longan, UT, USA). Calcein-AM (C3100 MP) and Ethidium homodimer-1 (EthD-1, E1169) were purchased from Invitrogen (Carlsbad, CA, USA) Lipopolysaccharide (LPS, purified from Escherichia coli 0111: B4), Griess reagent (G4410), Cell Counting Kit-8 (CCK-8, G4410), ECL prime Western blotting detection reagent (RPN2232), Tween-20 (P9416) and 2\(\times\) Laemmli sample buffer (S3401) were obtained from Sigma-Aldrich (Merck KGaA, St. Louis, MO, USA). Skim milk (232100) was purchased from Becton, Dickinson and Company (Sparks, MD, USA). Power blotter (PB7320) was received from Thermo Fisher Scientific (Carlsbad, CA, USA). 10\(\times\) TBS buffer (TR2005-100-74) was obtained from Bioesang (Seongnam-si, Gyenggi-do, Republic of Korea), and 30% acrylamide (A0418-050) was acquired from GenDEPT (Baker, TX, USA). RIPA buffer (10\(\times\)) (#9806), PMSF (#8553), COX-2 (#12282), iNOS (#2982), NF\(\kappa\)B (#8242), IKK\(\kappa\) (#2682), IKK\(\beta\) (#8943), and IxB\(\alpha\) (#9242) were purchased from Cell Signaling Technology (Beverly, MA, USA), and anti-mouse IgG (#7076), anti-rabbit IgG (#7074), and \(\beta\)-actin (sc-47778) were purchased from Santa Cruz Biotechnology (Dallas, TX, USA). Dulbecco's phosphate-buffered saline (PBS; L0615) was purchased from Biowest (Nuaille, France).
## 2 Failure Recovery in SFC and Challenges
### Segment Routing  
Network traffic flows must traverse ordered segments in SFC, where a segment is a route from one SF to the next. Conventional routing approaches create a path between a single source and destination; on the contrary, SFC has multiple sources and destinations as the starting and ending SFs of each segment are treated as the source and destination. The Network Service Header (NSH) protocol is designed to route traffic flows through SFC [15], where packets are encapsulated by outer transport encapsulation. The Service Path Header (SPH) is the main part of the NSH, and it stores the SFC path. The SPH consists of a 24-bit Service Path Identifier (SPI) indicating which path in SFC is to be used and an eight-bit Service Index (SI) that defines the number of SFs traversed in the SPI. Together, the SPI and SI values define the current path and the SF the packet is traversing. A major drawback of the NSH is that the SPI is per path and not per SFC; this means that a new SPI must be created when part of the SFC path is changed. This increases the flow entries to distinguish new SPI and SI combinations and requires the update of both the SPI and SI for re-routing due to failure.  
Segment Routing (SR) resembles source routing where a complete path is added in the packet through an additional header at the ingress node. The path in the additional header is defined by an ordered list of labels representing network elements (i.e., SFs) to be traversed. Multi-Protocol Label Switching (MPLS) [16] is one of the protocols that can be used to implement SR. The MPLS header with the ordered labels is added to the packet by the ingress router of the network, and each label represents one segment in SFC. The core routers in the network use these labels to route the packets to appropriate destinations, and the label of a segment is popped out once it is traversed. Due to the limitations of the NSH, this paper defined SFC traffic routing and failure recovery mechanism through SR and implemented them in underlay network by using stacked labels in the MPLS header.
# Mapping Areal Precipitation with Fusion Data by ANN Machine Learning in Sparse Gauged Region
## 3 Results and Discussion
### Precision of Original TRMM Products
#### 3.1.2 Grids without a Gauge Station  
Most of the grids have no ground gauge so that we could not evaluate the precision by precipitation itself. Some other validation principle is introduced. Here, it is the consistency principle (CR), which is a relationship rule of rainfall and elevation retrieved from the gauged data. The situation of 3B42 RT and 3B43 precipitation falling into the PEM is shown in Figure 4.  
Figure 4: The gauged precipitation – elevation mask (PEM) and original TRMM precipitation filter from 2001 to 2016. (**a**) Grids with gauge station. (**b**) Grids without gauge station.  
Figure 3: The Original TRMM Precipitation Products Errors.  
It can be found that the CR value of original 3B42 RT and 3B43 precipitation on the grids with gauge is 60% and 68%, while on the grids without gauge it is only 27% and 32%, respectively. It means the TRMM precipitation on the gauged grids is more reliable than that on the ungauged grids.
## 8 Conclusions
### Academic and Practical Contributions  
This research contributes in three aspects. First, this study represents a literature review study undertaken in a systematic and reproducible manner, demonstrating a standardized research process. Compared to former studies [94], the same standardized research process and a more fine-grained analysis of the literature are here realized, and this approach even achieves a more detailed and in-depth analysis of the content of the literature. This can theoretically guide the classification and application of algorithms and models, and allows us to explore methods and techniques for the analysis of multiple types of text or data. Based on this, researchers can better cope with the emergence of big data in text classification and promote cutting-edge machine learning and deep learning techniques. Second, this study comprehensively shows the progress of algorithm and modeling applications in China, and can thus be used as a reference for further research in multiple fields. Unlike research focusing on specific fields [95; 96], this research addresses the application of models and algorithms in multiple fields, which can help to elucidate the overall progress made in text analysis research. Third, this study provides ideas and methods for selecting suitable models and algorithms, and also provides a reference for constructing an indicator system to assess the effectiveness of a model or algorithm. These indicators are also useful for reconfiguring and optimizing algorithms and models [97].
# Confirmation of Final Bolt Tightening via Deep Learning-Based Image Processing
## 2 Related Research  
Image-based bolt tightening judgment methods that have been developed so far have proposed methods that calculate from the features of the bolt region in the image, or methods that calculate the angle of the bolt using the extracted features [3,4]. These methods require the environment and shooting conditions at the bolt tightening point to be adjusted and are vulnerable to noise. In this study, we propose a deep learning-based method to determine bolt tightness.  
The problem of realizing the proposed method involves the following: classifying images showing bolt marking results according to whether the bolts were tightened correctly and extracting each bolt from the image of the bolt tightening location via object detection.  
For image classification, deep learning methods have achieved high accuracies in recent years. Among them, convolutional neural networks (CNNs) [5] are often used. In object detection for images, deep learning methods have achieved high accuracies in recent years. The R-CNN [6], which uses CNNs for object detection via image classification, achieved an accuracy 30% higher than that of existing methods, indicating the usefulness of deep learning methods for object detection. In this method, the CNN is used to classify images extracted from the input image as object detection candidates. Based on this method, a Fast R-CNN [7] and Faster R-CNN [8] were proposed as faster object detection methods. YOLO [9] and SSD [10] were also proposed as methods for faster object detection. These methods use videos as inputs and can detect objects almost in real time. Additionally, they use the entire image for model learning; thus, the models learn the object and its surrounding information, suppressing false detection of the background.
# Genomic Analysis of Response to Neoadjuvant Chemotherapy in Esophageal Adenocarcinoma
### Mutational Signature and Neoantigen Analysis  
Tumor Mutation Burden (TMB) in terms of per megabases was measured with 50 MB capture size for non-synonymous mutations. To compare the neoantigen load between the two groups, we used binding affinity for patient-specific class I human lymphocyte antigen (HLA) alleles, constituting potential candidate neoantigens by checking for the binding strength for peptides of length 9 using NeoPredPipe (version 1.1; Tampa, FL, USA, 2019) [35]. We then quantified the peptides that displayed high affinity (recognition potential > 1) binding in tumor, but no binding in the respective matched normal [36] as recognition potential prediction step implemented in NeoPredPipe to obtain recognition potential for each sample. We then compared the recognition potential between two groups by Wilcoxon rank-sum test. EAC mutational signatures were extracted using SigProfiler (version 1.1.0; San Diego, CA, USA, 2020) [35]. We processed the COSMIC solution to remove any artefactual signatures and signatures that contribute on average less than 1% of the mutations in the genome. A total of nine mutational signatures were identified, of which six were previously identified in EAC described by [PERSON] et al. [24]: SBS17A and SBS17B dominated by T > G substitutions in a CTT context and possibly associated with gastric acid reflux; SBS3, a complex pattern caused by defects in the _BRCA1/2_-led homologous recombination pathway; SBS2, C > T mutations in a TCA/TCT context, an APOBEC-driven hypermutated phenotype; SBS1, C > T in a \"CG context, associated with aging processes; an SBS18-like signature, C > A/T dominant in a GCA/TCT context, formerly described in neuroblastoma, breast and stomach cancers; SBS13 is usually found in the same samples as SBS2; SBS5, linked to tobacco exposure; and SBS41 with unknown etiology. Clustering of mutational signatures was performed with the NMF package (version 0.23.0 Cape Town, SA, USA, 2010) [37] set to three main clusters as previously observed in EAC.