esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Virtual Surgical Planning Protocol Between Surgeons and Bioengineers
### Fabrication of Surgical Guides  
Surgical guide fabrication is the final stage of virtual surgical planning and is critical to the accurate execution of the plan during the actual surgery. Surgical guides can be broadly categorized into mandibular resection guides and reconstruction guides. This process can be performed by the bioengineer under the guidance of the surgeon.  
For the mandibular resection guide, the fitting area is determined considering the lesion characteristics and approach method. An extraoral approach can take advantage of the mandibular inferior border curvature, while an intraoral approach can benefit from alveolar bone adaptation. The Resection Guides can also be used to position the mandible by connecting the proximal and distal portions, and screw holes can be preset for accurate fitting of custom plates.  
Reconstruction guides are fabricated with donor site characteristics in mind. Osteotomy lines for partial bone cuts and screw holes for accurate custom plate fitting are pre-set to ensure plate positioning according to the surgical plan.  
Surgeon's Role: Define the surgical approach and determine the overall shape of the surgical guide.  
Bioengineer's role: Design and 3D print patient-specific surgical guides.
# SCM-IoT: An Approach for Internet of Things Services Integration and Coordination
## 2 Related Work
### Coloured Petri Nets (CPN)  
A CPN is a directed bipartite graph with annotations on its elements [78]. In a CPN, the nodes are of two distinct types, known as _places_ and _transitions_, and visualized by ellipses and rectangles. CPN tools [79], a widely used modeling environment, adopts this form of visualization. At any moment, a place contains a possibly empty multiset of structured data called _tokens_. The distribution of the tokens among the places is known as _marking_. Place marking is visualized as a collection of tokens inside that place. The _arcs_ between the nodes must satisfy the structural constraint that they only can connect nodes of different types, either from places to transitions or from transitions to places. The places and their respective arcs incident to a transition are known as the _input places_ and _input arcs_ of the transition, respectively. Likewise, the places and their respective arcs incident on them, starting from a transition, are known as the _output places_ and _output arcs_ of the transition. _Annotations_ are written on the elements of a CPN using the functional programming language SML as in CPN Tools, according to well-defined syntactic and semantic rules. Annotations on the input arcs impose constraints on the selection of the resources, whereas the annotations on the output arcs establish their transformation rules. The scope of the variables used in the annotations is centered around each transition. An input arc annotation is an expression that uses variables to designate either a token or one of its components within the token structure. In concurrent systems, places correspond to passive resource storage sites, while transitions are actions intended to move resources from input places to output places, possibly transforming them according to a rule of transformation.  
In CPN, the basic operations of movement and transformation of resources, grounded on variable substitution and annotation instantiation under a substitution, are centered on transitions. Substitution is an assignment of values to the variables occurring in at least one annotation of the input arcs for any transition. A _consistent substitution_ produces instances of the input arc annotations that can be identified as tokens in the marking of the corresponding input places. A transition is _enabled_ if a consistent substitution satisfies the transition condition (guarding). An enabled transition is eventually fired as long as it remains enabled. _Firing_ an enabled transition changes the distribution in the marking of the input and output places of the transition.
# Towards Tamper-Proof Trust Evaluation of Internet of Things Nodes Leveraging IOTA Ledger
## 5 Results and Discussion
### \(\lambda\) Sensitivity Analysis  
The proposed scheme uses a trust threshold called \(\lambda\) to determine the trustworthiness of a node. This threshold provides a trade-off between number of trustworthy nodes and privacy. Figure 2 shows that there is an inverse relationship between \(\lambda\) values and the number of trustworthy nodes. Therefore, when the information is more sensitive, a higher threshold value is selected in order to only select the nodes with highest final trust score. However, when the information is not very sensitive, lower values of \(\lambda\) return many trustworthy nodes, contributing to network efficiency.  
It has been noted that \(\lambda\) values between 0.25 and 0.50 offer an optimal number of trustworthy nodes for a network of 10-100 nodes. The wide range of \(\lambda\) values (0.05 to 1.00) provides flexibility in adjusting the strictness of trust evaluation. This allows for fine-tuning based on specific network requirements or security needs.  
Our trust evaluation method demonstrates a nuanced approach to assessing node trustworthiness. By adjusting the \(\lambda\) parameter, we can control the balance between network security and inclusivity. The method appears to be adaptable to different network sizes, which is crucial for real-world applications where network scale can vary significantly. These conclusions highlight the flexibility and effectiveness of our trust evaluation method in various scenarios.
# Corrosion-Fatigue Evaluation of Uncoated Weathering Steel Bridges
## 6 Sensitivity Analysis on Parameters
### Influence of the Stress Ratio  
The stress ratio (\(R\)) varied from 0.01 to 0.5 in the analysis. The crack depth was plotted against time, as shown in Figure 12. According to the result, when the stress ratio ranges increased from 0.01 to 0.5, the whole C-F life was reduced by 64.9% in the HPS 70W and 95.6% in the 14 MnNbq. It is also worth noting that the C-F life in the 14 MnNbq became longer than that in the HPS 70W when the stress ratio was below 0.05, which would be discussed in the following.  
The influence of the stress range on the initiation life and the propagation life are also shown in Figure 13a,b, respectively. It could be found that with the increases in the stress ratio, the propagation  
Figure 11: Impact of stress range on the C-F life: (**a**) Initiation life and (**b**) propagation life.  
Figure 12: Crack development under various stress ratios: (**a**) HPS 70W and (**b**) 14 MnNbq.  
life steel was decreased by 42.7% in the HPS and 81.2% in the 14 MnNbq. Meanwhile, the initiation life was reduced by 68.3% in the HPS 70W and 97.6% in the 14 MnNbq. The result suggests that the C-F life in the HPS 70W was less sensitive to the variation in the stress ratio. In another word, the HPS 70W had better robustness under the change in the stress ratio as previously illustrated by the analysis on the critical depth. It is also worth noting that when the stress ratio was less than 0.05, both the initiation life and the propagation life in the HPS 70W were shorter than that in the 14 MnNbq. On the contrary, once the stress ratio increased beyond 0.05, a totally opposite trend could be observed. This phenomenon could explain the fact why the C-F life in the 14 MnNbq was higher under a stress ratio of 0.05, as shown in Figure 12. However, it only happened under some extremely rare situations that the stress ratio was under 0.05 in steel bridges. Thus, the HPS 70W could be said to have a much better C-F performance under different stress ratios.
# Improved Cryptanalysis of Some RSA Variants
## 2 Preliminaries
### Schemes with the Key Equation \(ed-k(p^{2}-1)(q^{2}-1)=1\)  
In 1995, [PERSON] et al. [7] introduced a cryptographic scheme utilizing a specific elliptic curve, defined by the equation  
\[y^{2}\equiv x^{3}+bx^{2}\pmod{N},\]  
where \(N=pq\) represents an RSA modulus. In this scheme, the public key is represented by the pair \((N,e)\), with \(e\) chosen to satisfy  
\[\gcd\Bigl{(}e,(p^{2}-1)(q^{2}-1)\Bigr{)}=1.\]  
The private key is \((N,d)\), where \(d\) is determined such that  
\[d\equiv\frac{1}{e}\pmod{(p^{2}-1)(q^{2}-1)}.\]  
The encryption of the message \((m_{x},m_{y})\) is performed by computing  
\[b\equiv\frac{m_{y}^{2}-m_{x}^{3}}{m_{x}^{2}}\pmod{N},\]  
after which the ciphertext \((c_{x},c_{y})\) is obtained by applying the encryption operation \(e\) to \((m_{x},m_{y})\) on the elliptic curve.  
Decryption is carried out by first computing  
\[b\equiv\frac{c_{y}^{2}-c_{x}^{3}}{c_{x}^{2}}\pmod{N},\]  
and then applying the decryption operation \(d\) to \((c_{x},c_{y})\) on the curve to recover the original plaintext \((m_{x},m_{y})\).  
In 2002, [PERSON] et al. [8] proposed a cryptosystem in which arithmetic operations are carried out over \(\mathbb{Z}[i]\), the ring of Gaussian integers, rather than the standard integers. In this scheme, the modulus is a standard \(N=pq\), and the public and private exponents are related by \(ed\equiv 1\pmod{(p^{2}-1)(q^{2}-1)}\). For encryption, the plaintext \(m\in\mathbb{Z}[i]\) is transformed into the ciphertext \(c\equiv m^{e}\pmod{N}\), and the decryption is performed using \(m\equiv c^{d}\pmod{N}\).  
In [9; 15], alternative RSA variants were introduced, where the modulus is expressed as \(N=pq\), and the exponents \(e\) and \(d\) are interlinked by the key equation  
\[ed\equiv 1\pmod{(p^{2}-1)(q^{2}-1)}.\]
# Arginine-Mediated Self-Assembly of Porphyrin on Graphene: A Photocatalyst for Degradation of Dyes
## 3 Results
From these plots, the degradation rate constant of RhB and MO by GNPs@TCPP nanofibers as a photocatalyst is calculated to be 15.8 \(\times\) 10\({}^{-3}\) and 6.5 \(\times\) 10\({}^{-3}\) min\({}^{-1}\), respectively. Figure 5: XRD patterns of monomeric TCPP molecule and GNPs@TCPP nanofibers. It is well known that \(J\)-type porphyrin assemblies can be used as organic semiconductors to harvest light and generate electron-hole pairs under light irradiation [5; 42; 49; 50]. Moreover, previous studies demonstrated that graphene could be used as a support and promoter to improve charge separation, by suppressing the recombination of electron-hole pairs generated from irradiation of the porphyrins, increasing the lifetime of the charge carriers [23; 32]. Graphene also plays an important role in enhancing the efficiency of dye adsorption, as it facilitates the interfacial charge transfer to the adsorbed agent [23; 51]. Based on this literature, a possible mechanism for the enhanced photocatalytic activity of the GNPs@TCPP nanofibers photocatalyst toward dyes is proposed as illustrated in Figure 7. When a GNPs@TCPP nanofiber is irradiated with visible light, electrons from valence band (VB) of TCPP crystals cross the band gap to the conduction band (CB), resulting in the generation of \(\mathrm{e}^{-}/\mathrm{h}^{+}\) pairs [52]. The generated electron in the CB may move freely to the graphene sheets; consequently, the generated electron-hole pairs are effectively separation and recombination energy is significantly minimized. Dye molecules are oxidized to degraded products on the surface of TCPP nanofibers by active species such as 'OH formed by reaction between photogenerated holes with H\({}_{2}\)O or OH\({}^{-}\)[24]. On the other side, oxygen in H\({}_{2}\)O is reduced on the graphene sheet by the electrons generated from TCPP nanofibers under visible irradiation to form O\({}_{3}^{-}\) radicals [53]. Figure 6: (**A**,**C**) Photocatalytic performance and (**B**,**D**) kinetic simulation curve for RhB and methyl orange degradation, respectively, of (**a**) control without catalyst, (**b**) GNPs, (**c**) free-standing TCPP nanobelts, (**d**) GNPs@TCPP nanofibers. Figure 7: The possible mechanism of the GNPs@TCPP nanofibers photocatalyst for dye degradation.
## 3 Autonomous Mapping and Navigation with Satellite Imagery and Cadastral Information
### First Stage: Development of the Autonomous Mapping System
#### 3.1.1 Acquisition Sub-Module  
This sub-module is in charge of obtaining the two images which are needed to create the map of the parcel from the current position of the autonomous system. Its description is detailed in Algorithm 1 where _CurrentPosition_ represents the current position of the robotic system, _Resolution_ is the resolution of the acquired imagery, and _Radius_ stands for the radius of the area.  
```
\(FullSatImage\) = ObtainSatImage{CurrentPosition,Resolution,Radius} \(Borders\) = ObtainBorders[CurrentPosition,Resolution,Radius] Return:\(FullSatImage\),Borders
```  
**Algorithm 1** ObtainImages{\(CurrentPosition,Resolution,Radius\)}  
The output generated in this sub-module includes the full remote image of the area, \(FullSatImage\), and the borders of the parcels, \(Borders\), which will be processed in the \"Map Creation module\".
## 2 Results
### Nonlinear Responses and the Liquid-Solid Phase Transition
The sub-threshold pinning increases substantially as indicated by \(r_{d}=dV/dI\sim\)100 M\(\Omega\). As shown by previous studies [28; 34], \(\xi\) is estimated by balancing the total potential energy \(Ne\) and the pinning energy \(-(1/2)\kappa a\). \(\kappa=0.245e^{2}p^{3/2}/4\pi e_{0}e\) is the sheer modulus and \(N=p\xi^{2}\) is the particle numbers in the domain. For our case, this yields \(N\geq 10^{5}\) or \(\xi\geq 50\)\(\mu\)m. As clearly shown by the results below by the general nonlinear IVs up to 43 mK, \(\xi\) decreases with increasing \(V\). Therefore, the estimated \(\xi\) reflects a lower limit. The pinning effect depends on both \(T\) and the external bias. Thus, to probe a thermal melting, the external bias signal must be limited to the lowest levels. Figure 3c shows the \(r_{d}\) measured with \(I\leq\) 0.5 \(p\)A at several temperatures. \(r_{d}(T)\) exhibits a piecewise behavior, supporting a phase transition at \(T_{c}\sim\)30 mK where a sharp jump of nearly 60 M\(\Omega\) appears. The jump can be viewed as a potential energy loss if \(\xi\) is suddenly reduced. Because a pinned WC is characterized by the potential energy, \(r_{d}\) scales with \(\xi\). By limiting \(I\) below 0.5 \(p\)A, the nonlinear effect related to the level of excitation diminishes. Thus, this result implies a piecewise \(T\) dependence of \(\xi\) which is proportional to the energy within a WC domain. A recent Monte Carlo study indeed predicts a discontinuity in the free energy across the phase boundary between a WC and a quantum hexatic phase [51]. As a finite pinning [53] is retained above \(T_{c}\), this transition indeed ends up in an intermediate phase instead of a liquid. Melting through intermediate phases has been modeled for hexatics [51; 54; 55; 56; 57], stripes, bubbles, and microemulsions [58]. For the \(T\) dependence of \(r_{d}\) above \(T_{c}\) (or for \(1/T\leq\) 0.033 in Figure 3c), \(r_{d}\) falls exponentially with increasing \(T\), which is qualitatively consistent with the exponential \(T\) dependence of \(\xi\) predicted for the hexatic phases [52; 55; 56]. Figure 3: **(a)** Nonlinear and linear DC-IV for \(r_{s}=53\) at 32 and 43 mK within a current window up to 50 \(p\)A. Dashed line is a guide for the eye. (**b**) Threshold DC-IV obtained at 29 mK for the same \(r_{s}\) (**c**) Piecewise behavior of \(r_{d}(T)\) displayed in \(\log(r_{d})\) vs. \(1/T\).
# DenseZDD: A Compact and Fast Index for Families of Sets +
## 2 Preliminaries
### Succinct Data Structures for Trees  
An ordered tree is a rooted unlabeled tree such that children of each node have some order. A succinct data structure for an ordered tree with \(n\) nodes uses \(2n+o(n)\) bits of space and supports various operations on the tree such as finding the parent or the \(i\)-th child, computing the depth or the preorder of a node, and so on, in constant time ([PERSON] et al., 2018). An ordered tree with \(n\) nodes is represented by a string of length \(2n\), called a balanced parentheses sequence (BP), defined by a depth-first traversal of the tree in the following way: starting from the root, we write an open parenthesis '(' if we arrive at a node from above, and a close parenthesis ')' if we leave from a node upward. For example, imagine the complete binary tree that consists of three branching nodes and four leaves. When we traverse the complete binary tree in the depth-first manner, the sequence of the transition is \"down, down, up, down, up, down, down, up, down, up, up\". Then, we can encode the tree as \"\((,),(,),(,),(,),(,),(,),\)\" by replacing 'down' with '(' and 'up' with ')', respectively.  
In this paper, we use the following operations. Let \(P\) denote the BP sequence of a tree. A node in the tree is identified with the position of the open parenthesis in \(P\) representing the node:  
* \(depth(P,i)\): the depth of the node at position \(i\). (The depth of a root is 0.)
* \(preorder(P,i)\): the preorder of the node at position \(i\).
* \(level\_ancestor(P,i,d)\): the position of the ancestor with depth \(d\) of the node at position \(i\).
* \(parent(P,i)\): the position of the parent of the node at position \(i\) (identical to \(level\_ancestor(P,i,depth(P,i)-1)\)).
* \(degree(P,i)\): the number of children of the node at position \(i\).
* \(child(P,i,d)\): the \(d\)-th child of the node at position \(i\).  
The operations take constant time.  
A brief overview of the data structure is the following. The BP sequence is partitioned into equal-length blocks. The blocks are stored in leaves of a rooted tree called range min-max tree. In each leaf of the range min-max tree, we store the maximum and the minimum values of node depths in the corresponding block. In each internal node, we store the maximum and the minimum of values stored in children of the node. By using this range min-max tree, all tree operations are implemented efficiently.
## 6 Complexity Analysis
#### 6.1.2 Complexity of NLEs Based on Inverse Volterra Series Transfer Function (IVSTF)  
The procedure to calculate the number of FLOPs required by the IVSTF-based NLE is similar to that of DBP-based NLE since multiples time-to-frequency and frequency-to-time conversions are required. The number of operations, however, does not depend on the link spatial discretization but on the number of links. Looking at the blocking diagram of Figure 2, we can observe, that IVSTF-based NLE requires a linear compensation block and a non-linear equalization block per span, that is, \(N_{\text{span}}\), and therefore, the total number of FLOPs can be calculated as:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Link parameters**} & \multicolumn{2}{c}{**Signal parameters**} \\ \hline
**Symbol** & **Definition** & **Symbol** & **Definition** \\ \hline \(N_{span}\) & Number of spans & \(N_{SC}\) & Subcarrier number \\ \(L_{span}\) & Length per span & \(K\) & Oversampling factor \\ \(\Delta d\) & Spatial step & \(M\) & No. bits per subcarrier \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters employed in the calculation of complexity.  
\[N_{IVSTF}=N_{linear}+N_{span}\times N_{nonlinear} \tag{5}\]  
Here, \(N_{linear}\) is given by:  
\[N_{linear}=N_{FFT}+N_{prod}+N_{IFFT}, \tag{6}\]  
where \(N_{\rm prod}\) is the number of operations for linear equalization, which is given by \(6N_{\rm block}\) as it requires \(N_{\rm block}\) complex multiplications (6 FLOPs each). The number of FLOPs for the non-linear compensation block, on the other hand, is:  
\[N_{nonlinear}=N_{prod}+N_{IFFT}+N_{square}+2N_{prod}+N_{FFT}+N_{prod}. \tag{7}\]  
The square operation can be seen as an element-by-element multiplication of \(N_{\rm blocks}\) data and, consequently, also requires \(6N_{\rm blocks}\). Consequently, the total number of operations for the IVSTF-based NLE is given by:  
\[N_{IVSTF}=\big{(}N_{span}+1\big{)}8N_{block}\log_{2}N_{block}+\big{(}20N_{span }-6\big{)}N_{block}+16\big{(}N_{span}+1\big{)} \tag{8}\]
# Joint Uplink and Downlink Resource Allocation for D2D Communications System
## 1 Introduction
The issue of fair resource allocation in D2D underlaid cellular networks is studied [25] based on orthogonal frequency division multiple access (OFDMA). The joint of time scheduling and power control [26] is a non-convex optimization problem, which is transformed into a nonlinear fractional programming problem. Moreover, to achieve green communication, references [27; 28] resolve the energy efficiency (EE) maximization problem of downlink cellular communication system, in which the EE maximization problem for all D2D pairs is considered in reference [27], and the overall system EE maximization problem is considered in reference [28]. However, these methods in references [12; 13; 14; 15; 16; 17; 18; 19; 20; 21] reuse the uplink channel, and in references [21; 22; 23; 24; 25; 26; 27; 28] D2D users can share the downlink channel, therefore the total radio spectrum has not been fully utilized. To account for this problem, jointly uplink and downlink channel scheme [29; 30] are proposed in cellular networks. A new benefit-aware uplink-downlink (GAUD) resource allocation scheme [29] is proposed to maximize the total data rate of all D2D. The interference management algorithm is presented, which includes admission control, power allocation, and channel assignment in reference [30]. However, these two algorithms only maximize the total data rate of D2D, while not being optimal to the overall system data rate. In this paper, we consider the joint uplink and downlink resource allocation scheme to maximize the total system capacity. The quality-of-service (QoS) of all users of D2D and the co-channel CU should be satisfied by choosing the corresponding transmission power. Based on the above idea, an optimization problem with a binary variable is formulated, which is NP-hard in general. To make the problem better tractable, it can be decomposed into two sub-problems: Power allocation and channel assignment. It is first proved that the objective function of power allocation is convex, and the optimal transmission power is derived. Next, we develop the Hungarian algorithm for joint uplink and downlink channel assignment to maximize the total system capacity. Finally, the performance of the proposed scheme is evaluated through the numerical simulations. The rest of the paper is organized as follows. Section 2 introduces our system model and formulates the maximization problem of the system capacity. Then, in Section 3, we jointly develop a power allocation and channel assignment scheme. Furthermore, the formulated optimization problem is solved. After that, the simulation results are provided to demonstrate the superiority of the proposed schemes in Section 4. Finally, Section 5 briefly concludes this manuscript.