esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
This paper proposes IDEANE, a multipath forwarding strategy that makes use of identity identifiers to distinguish content requests with the combination of network coding and edge computing. IDEANE differentiates content requests through identity information and request sequence numbers, which can reduce the aggregation of multiple interest messages, improve the efficiency of interest message transmission, and promote the multipath forwarding of content requests. Encoding and decoding at the edge nodes can reduce the computing pressure of linear network coding on the intermediate nodes and reduce the transmission latency. Simultaneously, linear network coding and identity authentication can also improve the security of edge computing. At the same time, this paper proposes a multiple edge node cooperation scheme to solve the mobile problem of content requesters based on IDEANE and reduce the time taken for users to obtain the required content during the movement process. The simulation results on ndnSIM show that IDEANE can improve the cache hit rate, reduce the redundancy of coding blocks, shorten the cache hit distance, and reduce the request waiting time. In addition, IDEANE can solve the problem of link congestion caused by queuing.  
In future work, we will investigate how to intelligently schedule content request tasks to all edge nodes, taking into account the network bandwidth and remaining cache resources of edge nodes to complete load balancing and realize multi-access multipath forwarding and resource acquisition. In addition, taking advantage of network coding, coded computing [39], and edge computing [12], we will investigate how to better utilize identifiers for network forwarding in 6G networks to solve the communication bottleneck and improve the multipath forwarding performance and network transmission efficiency.  
Conceptualization, H.L. and Y.W.; methodology, Y.W.; software, Y.W.; validation, Y.W.; formal analysis, H.L.; investigation, Y.W.; resources, H.L.; data curation, Y.W.; writing--original draft preparation, Y.W.; writing--review and editing, H.L.; visualization, Y.W.; supervision, H.L.; project administration, H.L.; funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was partially supported by the 2022 High-Level Research Startup Fund of Liuzhou Vocational Technical College (Grant No. 2022 GCQD07), Guangdong R&D Key Program (2019B010137001), Shenzhen Research Program (GXWD20201231165807007-20200807164903001, JCYJ20210324122013036, JCYJ20190808155607340), China Environment for Network Innovation (CENI) (GJFGW No.[2020]386, SZFGW [2019] 261), and National Keystone R&D Program of China (2017 YFB0803204).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflicts of Interest.
## 2 Case Presentation
### Computer-Assisted Surgical Planning  
For computer-assisted surgery, CT data of the patient's facial bone and ilium were collected. The collected CT data were stored as a dicom file and segmented into mandible and ilium using Mimics software (Materialize, Leuven, Belgium). Segmentation of the lesion site where bone invasion was observed was also performed. The osteotomy line was planned by considering the location of the lesion. The shape of the surgical guide to be used in the actual operation was constructed using Geomagic freeform software (3D Systems, Valencia, USA). The original shape of the mandible and the mirror image of the opposite site were used to shape the bone flap to be reconstructed. The bone flap model was compared with the segmented ilium to select the most suitable shape. The surgical guide for the bone flap was thus prepared (Figure 2).  
Figure 1: Initial examinations: (**a**) Intraoral photograph showing the granulomatous lesion on the right lower gingiva; (**b**) Panoramic view showing the impacted tooth and osteolytid lesion.  
A customized titanium implant was designed using the shape of the ilium bone flap. The titanium implant reproduces the shape of the ilium while the interior is porous to improve cell proliferation and mesenchymal stem cell differentiation. On the outer surface, a plate shape with a hole for fixation to the ilium was added.
# Self-Sustainable Biomedical Devices Powered by RF Energy: A Review
## 2 RF Energy Harvesting Principles
### Antenna Design and Characteristics for RF Energy Harvesting
#### 2.1.1 Wearable Antenna Design for RF Energy Harvesting
Conventional and stretchable metals are suitable radiation elements due to their inherent electromagnetic properties and high radiation efficiency. Thus, the work reported [81] proposed a laser stretchable wideband dipole antenna fabricated using conductive laser-induced graphene (LIG) patterns; the surface was selectively coated with a metal and mounted on an elastomeric substrate. The radiation performance of the stretchable antenna was evaluated depending on its tensile strain using a modified stretcher to reflect its combined mechanical-electromagnetic properties. In addition to stretching, the performance of the stretchable wideband dipole antenna is resistant to deformation modes such as twisting and bending. The safety of the wearable antenna must be taken into account during the design phase, in addition to ensuring that a wearable antenna can blend in with the body. The specific absorption rate (SAR) is used to evaluate the electromagnetic safety of wearable antennas. The International Commission on Non-Ionizing Radiation Protection and the Federal Communications Commission (FCC)/International Electrotechnical Commission (IEC) limits the maximum general exposure to SAR to 1.6 W/kg (SAR 1 g) or 2 W/kg (SAR 10 g), respectively. SAR is computed by averaging over a sample volume, which is typically 1 or 10 g (ICNIRP), respectively.
# Multi-Objective, Reliability-Based Design Optimization of a Steering Linkage
## 2 Optimization Design Problem Formulation
### Optimization Problems  
A general optimization problem can be stated as  
\[\text{Min}\,f_{i}(\mathbf{x})\;i=1,\ldots,M \tag{14}\]  
Subject to \(g_{i}(\mathbf{x})\leq 0\), \(i=1,\ldots,N\)  
\(\mathbf{x_{i}}\leq\mathbf{x}\leq\mathbf{x_{u}}\)  
where \(i=1\) for single-objective optimization, \(i>1\) for multi-objective optimization, \(\mathbf{x}\) is a vector of design variables, \(g_{i}\) are inequality constraints, \(N\) is the number of inequality constraints, \(\mathbf{x}_{i}\) is a vector of lower bound limits, and \(\mathbf{x_{u}}\) is a vector of upper bound limits.  
From the new steering error, the optimization problem for steering linkage design is posed as  
\[\text{Min}\;f=\text{max}\big{|}\delta s(\theta_{I})\big{|}\;;;\theta_{I}\in[0 ^{\circ},40^{\circ}] \tag{15}\]  
\[\text{Subject to}\quad g_{1} =\;\theta_{60}-\theta_{6,\text{max}}\;\leq\;40^{\circ} \tag{16}\] \[g_{2} =\;f\;\leq\;0.75\] \[g_{3} =\;L_{u}+L_{t}<L_{0}=\sqrt{H^{2}+(W_{t}/2-L_{r}/2)^{2}}\] \[g_{4} =\;L_{u}\;\geq\;L_{t}\;+\;L_{0}\] \[g_{5} =\;L_{t}\;\geq\;L_{u}\;+\;L_{0}\] \[g_{6} =\;\{0.1,0,0\}^{T}\;\leq\;\mathbf{x}\;\leq\;\{0.3,0.3,0.3\}^{T}\; \text{m}\]  
where the design variables are \(\mathbf{x}=\{L_{u},L_{t},H\}^{T}\). In turning right/left of the car in range \(\theta_{I}\in[0^{\circ},40^{\circ}]\), the computation of the steering error \(f\) should be less than or equal to 0.75. The value is defined follow in [33], which is in the interval [0,1]. The design constraints imposed in the problem (14) cause a narrow feasible region, as mentioned in [17; 33]. The extension causes the feasible design domain being wider. The second last constraint is assigned for avoiding the steering linkage from limiting position [17; 33]. All remaining constraints are assigned for the mechanism usability.
## 2 Fixed Right Shift Code (Related Works)  
This code is characterized by \(L\), \(P\), \(\lambda_{cross}\), and \(N_{u}\), that represent, respectively, code length, code weight, cross-correlation between any two code sequence, and number of simultaneous users.  
The relation between \(L\) and \(N_{u}\) can be given as [29]:  
\[L=N_{u}\ (P-2)+P \tag{1}\]  
The minimum value for \(P\) is 3 and the FRS code must obtain the condition of \((P-2)\) fixed right shift of unity. As its construction depends on selecting the values of \(P\) and \(N_{u}\), whether even or odd, that makes the FRS code construction vary. The basic matrix \(N_{u}\times L\) of the code contains first nonzero (\(1^{st}NZ\)) elements and last nonzero elements (\(LNZ\)) and can be expressed as [29]:  
\[B_{M}=\begin{bmatrix}1^{st}NZ_{1}&LNZ_{1}&0&\cdots&\cdots&\cdots&0\\ 0&1^{st}NZ_{2}&LNZ_{2}&0&\cdots&\cdots&0\\ 0&0&1^{st}NZ_{3}&LNZ_{3}&0&\cdots&\vdots\\ \vdots&\vdots&\vdots&\ddots&\ddots&\ddots&\vdots\\ 0&0&0&0&1^{st}NZ_{U}&LNZ_{U}\end{bmatrix} \tag{2}\]  
Thus, it is important to know the position of both \(1^{st}NZ\) and \(LNZ\) elements in the matrix. Let \(W\) be the number of rows that has values ranging from 1 to \(N_{u}\), so the \(1^{st}NZ\) can be placed at \((W,1+(W-1)(P-2))\). As for \(LNZ\), it can be placed at \((W,1+(W-1)(P-2))\). As for the other elements of the matrix, they can be filled as follows: fill \((P-2)\) with \"1 s\" after \(1^{st}NZ\) and \((P-2)\) with \"0 s\" before \(LNZ\).  
As an example that is used in this study, we take \(P=3\), \(N_{u}=3\), so the value of \(L\) according to Equation (1) will be 6. Thus, the matrix will be 3 \(\times\) 6. Therefore, we have three rows and each row has \(1^{st}NZ\) and \(LNZ\) elements. As for the first row, \(W=1\), the place of \(1^{st}NZ\) and \(LNZ\), respectively, will be at (1,1) and (1,4), while for \(W=2\), \(1^{st}NZ\) is located at (2,2) while \(LNZ\) is located at (2,5). The last row has the position of \(1^{st}NZ\) at (3,3) and the position of \(LNZ\) at (3,6). As for the other elements of the matrix, we fill \"1 s\" \((P-2)=3-2=1\) after \(1^{st}NZ\) and \"0 s\" with \"0 s\" before LNZ. Then the 36 FRS code matrix will be expressed as [29]:  
\[M_{3\times 6}=\begin{bmatrix}1&1&0&1&0&0\\ 0&1&1&0&1&0\\ 0&0&1&1&0&1\end{bmatrix} \tag{3}\]
## 5 Tests and Results
### Algorithmic Setup  
The software has been fully implemented in Python with the support of the following third-party libraries: NetworkX [62] and LittleBallOfFur [63] for handling and processing graph data structures, Scikit-Learn [64] for machine learning routines and SciPy [65] for optimization routines.  
Other algorithm parameters have been configured as follows:  
* \(\alpha=0.9\) in the fitness function of the first genetic optimization (cf. Equation (8));
* \(\beta=0.9\) in the fitness function of the second genetic optimization (cf. Equation (12));
* Maximum number of 20 generations for both genetic optimizations;  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset Name** & **\# Graphs** & **Avg. \# Nodes** & **Avg. \# Edges** & **\# Classes** & **Balanced** & **Domain** & **Reference** \\ \hline AIDS & 2000 & 15.69 & 16.20 & 2 & No & Chemoinformatics & [49] \\ Letter-L & 2250 & 4.7 & 3.1 & 15 & Yes & Computer Vision & [49] \\ Letter-M & 2250 & 4.7 & 3.2 & 15 & Yes & Computer Vision & [49] \\ Letter-H & 2250 & 4.7 & 4.5 & 15 & Yes & Computer Vision & [49] \\ GREC & 1100 & 11.5 & 12.2 & 22 & Yes & Electronics & [52; 49] \\ Mutagenicity & 4337 & 30.3 & 30.8 & 2 & No & Chemoinformatics & [53; 49] \\ MUTAG & 188 & 17.93 & 19.79 & 2 & No & Chemoinformatics & [54; 55] \\ DD & 1178 & 284.32 & 715.66 & 2 & No & Bioinformatics & [56; 57] \\ NCII & 4110 & 29.87 & 32.30 & 2 & Yes & Chemoinformatics & [57; 58] \\ ENZYMES & 600 & 32.63 & 62.14 & 6 & Yes & Bioinformatics & [59; 60] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset statistics.  
* A total of 20 and 100 individuals in the population for the first and second genetic optimization, respectively;
* Maximum walk length \(o=5\);
* Candidate \(\theta\) values are \(\theta=\{0.5,0.25,0.125\}\);
* \(Q\in[1,Q_{\max}]\), where \(Q_{\max}=500\) for non-stratified Granulators and \(Q_{\max}=500/p\) for stratified Granulators [41];
* The user-defined bucket size \(W\) has been chosen according to a given percentage of the maximum number of paths or cliques that can be drawn from the training graphs. The percentages are chosen according to the following rule: the larger the dataset, the higher the subsampling rate. For the sake of shorthand, we omit any sensitivity analysis on the behavior of the Granulators as a function of \(W\). In this regard, we refer the interested reader to our previous works [41; 46; 66] The values for \(W\) for both clique-based and path-based Granulators are summarized in Table 2.
# Wildlife Monitoring Using a Multi-UAV System with Optimal Transport Theory
## 3 Method
### OT-Based Multi-UAV Exploration: Time-Invariant Case
#### 3.2.1 A Three-Stage Approach
The weight update process for the common weight \(n_{T}(y_{j})\) is provided as follows:  
\[n_{T}(y_{j})=\min(n_{T}^{k}(y_{j})),\qquad k=1,2,\ldots,n_{a} \tag{10}\]  
This common weight information is transmitted to all agents at each time step. By sharing the common weight information, each UAV can know which areas are already covered by other UAVs. Thus, the team of UAVs can explore the given spacious domain effectively.
# Longitudinal Wind Tunnel Tests of the PROSIB 19-Pax Airplane
## 2 Wind Tunnel Setup
### Aircraft Model Description  
The aircraft experimental model was developed by the authors from the design presented in [6] and was made in aluminum alloy, except for the battery pod, which was made with ABS in additive manufacturing. A three-view drawing is provided in Figure 3. The manufacturing and detailed design of the fittings were outsourced. The scale of the model was 1:15, with a wing planform area \(S=0.25\,\mathrm{m}^{2}\) and mean aerodynamic chord \(\overline{c}=0.171\,\mathrm{m}\). It allowed sufficient internal space to install the strain gauge balance within the fuselage, right below the wing-body fairing. The main connection was between the latter and the balance interface plate. This allowed an easy handling of the components, with the fuselage installed from below. The empennage group could be entirely removed from the fuselage. The airfoils used on the lifting surfaces were the NACA 23018 for the wing root and kink sections, the NACA 23015 for the wingtip section, and the NACA 0012 for the empennage sections.  
Two tail configurations were provided: a body-mounted horizontal tail and a T-tail. The latter was designed with the VeDSC method [26] to provide the same directional stability of the former, exploiting the end-plate effect [19]. Under this assumption, the T-tail configuration was sized with about 13% less vertical tail planform area. However, the horizontal tail was scaled down by 30% to be installed on the vertical tail tip. The high horizontal tail had a small backward shift of its aerodynamic center, but it was not sufficient to compensate the loss of planform area and, consequently, of the volumetric coefficient. The two configurations are compared in Figure 4 and their differences are detailed in Table 3. The aerodynamics of the two empennage groups are discussed in the next sections.  
Figure 4: Comparison of two tailplane configurations: (**a**) body-mounted; (**b**) T-tail. Units in mm.  
Figure 3: Aircraft model drawings. Units in mm.  
\begin{table}
\begin{tabular}{l l l} \hline
**Parameter** & **Body-Mounted** & **T-Tail** \\ \hline Root chord & 123 mm & 102 mm \\ Tip chord & 87 mm & 77 mm \\ Span & 469 mm & 422 mm \\ Sweep angle & 15\({}^{\circ}\) & 15\({}^{\circ}\) \\ Planform area & 49,245 mm\({}^{2}\) & 37,769 mm\({}^{2}\) \\ Aspect ratio & 4.47 & 4.72 \\ Taper ratio & 0.71 & 0.75 \\ Long. distance from ref. point & 560 mm & 571 mm \\ Volume tail coefficient & 0.645 & 0.505 \\ \hline \end{tabular}
\end{table}
Table 3: Comparison of the two horizontal tailplanes data.
# The Concept of Segmented Wind Turbine Blades: A Review
## 7 Mechanical Joints in Segmented Blades
### Experience from Blade Root Connections
#### 7.2.4 Stud/Insert Type  
The stud or insert root joint relies on longitudinal bolts attached to studs or inserts. Typically, the inserts are female threaded and made of steel, causing a thermal and flexural mismatch [107].  
Figure 6: An overview of blade root joints. (**a**) Flange root connection. (**b**) Hub-type root connection. (**c**) T-bolt root connection. (**d**) Stud root connection.  
This is countered by tapering the studs on the out or inside and by using a thicker laminate [107]. [PERSON] [107] suggested to use a threaded insert made from a composite tube to improve compatibility to allow a reduced root wall thickness. Furthermore, to reduce the stress concentration at the tip of the inserts, [PERSON] [108] suggested using inserts of different lengths. Often, the studs are glued into the blade. In wood composite blades, the studs are placed in holes that are drilled, while in glass fiber blades, the holes are preferably formed during fabrication [41; 109]. Positioning of the stud is vital to the quality of the joint as a non-uniform adhesive thickness causes stress concentrations [110]. Typically, fixtures are used to position and bond the studs simultaneously. Often, the adhesive is injected into the hole around the insert by using a secondary hole or through the gap between the laminate and the stud. Alternatively, modified studs can allow the adhesive to flow through the center of the insert to form the bond through the stud [41]. Additionally, the joint quality can deteriorate because of macroscopic voids [41]. To avoid these voids, [PERSON] [110] suggests to improve the tru-stud bonding method to allow vacuum infusion by adding a second channel to the stud. Alternatively, the studs can be directly embedded during the lamination process. This requires less fabrication process steps, tooling and allows the root laminate to be much thinner, but increases the complexity of the lamination process [41; 111]. [PERSON] [112] suggested using a holder with spaced recesses to hold the bushings. As an alternative, [PERSON] [113] and [PERSON] [114] both suggested inserts that can easily be positioned and form a smooth outer and inner surface onto which the fiber mats of the root laminate can be applied. In general, to provide sufficient pull-out strength, inserts have to be long. Various improvements have been suggested to increase the pull-out strength, allowing shorter, lighter inserts. [PERSON] [115] suggested to include longitudinal grooves on the outside of the inserts to increase the contact area with the laminate. Further, in similarity with the Hutter root, [PERSON] [111] proposed to capture the inserts mechanically by looping fibers around it. Additionally, [PERSON] [116] suggested putting fibers in between the inserts for a better contact, whereas [PERSON] [117] suggested stitching together the fibers surrounding the bushings.
## 2 Transformer Symbolic Regression
### Improved PhySO Algorithm  
In the PhySO project, the authors used LSTM as a neural network architecture, and only the optimal 5% of candidate solutions were rewarded. However, LSTM has the problem of poor parallel performance, and each LSTM cell has four fully connected layers, if the LSTM network is very deep, the computational load will be large and time-consuming. And only the optimal 5% candidate solution is rewarded, when the channel mathematical model is relatively complex, such as when there are mixed operations of logarithm, exponent, fraction, and trigonometric function in mathematical expressions, there are problems of poor exploration performance and slow convergence. In addition, in the process of generating a channel model with PhySO, multilayer fractions, and multilayer exponents often appear, which are not common in the channel model. In order to solve these problems, the PhySO method is improved to make it more suitable for channel modeling tasks.  
In this paper, the LSTM architecture is changed to transformer architecture [16] to increase the parallel performance and feature extraction capability of the algorithm. The self-attention mechanism in the transformer architecture enables the calculation of each time step to only rely on the input vector, thus achieving completely parallel computation. Moreover, the self-attention mechanism can directly calculate the dependency relationship between any two positions in the sequence, making the model better able to capture long-distance dependencies. The structure of the transformer model is very flexible and can be adjusted according to the needs of specific tasks, such as increasing or decreasing the number of layers and adjusting the number of attention mechanism heads. Its architecture diagram, as shown in Figure 4, includes the input layer, transformer layer, and output layer. Both the input and output layers are linear layers, and the transformer architecture used contains only the Encoder part, which is used to extract the features of mathematical expressions.  
Figure 4: Network architecture diagram.  
In the process of exploration, the optimal 5% candidate solution is changed to the optimal candidate solution decreasing from 10-5% to increase the exploration effect of the model.  
According to the channel model expression, the symbol space is redefined as \(\left\{+,-,\times,/\exp,\log,\frac{1}{x},x^{2},\sqrt{x}\right\}\). By reducing unnecessary operators, search complexity can be reduced and convergence speed can be accelerated.  
Modify the config file of the PhySO project to reduce the occurrence probability of multiple fractions and exponents. Because in the common channel model, it is unusual to see the nested fraction and exponential function. By reducing the nesting of fractional, exponential, and logarithmic operations, the complexity of algorithm search can also be reduced, resulting in a faster search for the optimal expression.  
The logarithmic function used in the PhySO project is changed to the logarithmic form with base 10, which is more consistent with the dB definition in the channel model.