esa-sceva/satcom-chunk-collection huggingface dataset:
# Random Violation Risk Degree Based Service Channel Routing Mechanism in Smart Grid
## 6 Conclusions  
On account of the problems in electric power communication service route planning, a probability model of service channel violation risk, named _SCVRD_ model, is proposed in this paper. Generally, the \(A_{SPA}\) is calculated by mean value and \(A_{APA}(t)\) is calculated based on some assumption, both of them cannot precisely track the violation risk change of service channel which is caused by the random failure under random \(TTR\) condition. To solve this problem, we deduce _SCVRD_ model from the service channel violation risk model which is usually denoted by \(A_{APA}(t)\) and denote _SCVRD_ model by the probability of service channel cumulative failure duration exceeding the prescribed duration. The deduction is proved and then _SCVRD_ model is simplified using mathematical method. Based on _SCVRD_, a service channel violation risk degree routing algorithm, named _SCVRD-R_ algorithm, is proposed to reduce the risk caused by random failure of transmission equipment and optical cable and improve the availability of electric power communication service. Finally, the simulation results show that the average service channel failure rate of AAR-OS algorithm and RAP algorithm are respectively reduced by 15% and 6%.  
In future work, we plan to further investigate how the failure rate convergence changes when optic cables reach the bandwidth limit. Furthermore, we intend to figure out the accurate numerical relationship among service intensity, service channel bandwidth and failure rate convergence to actually guide the service routing planning and optimization in electric power communication network. In many cases, the success of smart grid depends on their ability to support real-time decision-making. Therefore, we would make further efforts on the exploration of our proposed methodology in medium and short-term planning scenarios.  
Conceptualization, S.S. and Q.Z.; Methodology, X.Q.; Software, S.S. and Q.Z.; Validation, S.G. and X.Q.; Formal Analysis, S.S. and Q.Z.; Investigation, S.S. and Q.Z.; Resources, Q.Z.; Data Curation, S.S. and Q.Z.; Writing--Original Draft Preparation, S.S.; Writing--Review and Editing, Q.Z., S.G. and X.Q.; Visualization, S.S. and Q.Z.; Supervision, X.Q.; Project Administration, S.G.; Funding Acquisition, S.S., S.G. and X.Q.  
This research received no external funding.  
This research was supported by Independent Subject of State Key Laboratory of Networking and Switching Technology of Beijing University of Posts and Telecommunications Named Research on Fault Recovery Mechanism of Electric Power Internet of Things.  
The authors declare no conflict of interest.
## 4 Discussion
Overall, although it has been discussed that the QS approximation can be used for various applications by ignoring the capacitive effect for the cochlea simulation [37], the results of this study showed that the capacitive effect for the cochlea simulation should be considered after a certain frequency threshold (e.g., 10 MHz) to obtain more accurate results and improve the credibility of the simulations. Thus, the findings of this study may guide the clinicians for accurate electrode insertion to the ST when the EA was inserted based on various simulation frequency ranges. Therefore, the outcome can be used to design for configuring stimulation protocols in CI patients. Since different cochlear characteristics, such as variations in tissue composition and fluid dynamics, may affect how capacitive impacts manifest in individual patients, these findings highlight the need for more personalized cochlear implant programming to account for individual anatomical differences, especially at higher frequencies. Also, clinicians may need to adjust the frequency of the simulation electrode insertion based on the cochlear structure and patient-specific anatomy to optimize stimulation of both low and high-frequency regions. The findings suggested that careful consideration must be given to prevent damaging the delicate cochlear structures, especially if capacitive effects significantly alter signal propagation at a certain frequency level.
## 2 System Model
### Calculation of Signal to Noise Ratio (SNR)  
The received signal at \(D_{i}\) is given by  
\[y_{Di}^{FD-PDMA}=g_{i,k}\left(\sqrt{a_{1}P_{S}}x_{1}+\sqrt{a_{2}P_{S}}x_{2} \right)+f_{i}\sqrt{\varpi P_{Di}}x_{rDi}+w_{i}, \tag{1}\]  
where \(\varpi=1\) denotes user 1 working in FD, \(P_{Di}\) is transmit power of \(D_{i},i=1,2\) and \(w_{i}\) is the additive white Gaussian noise with zero mean and variance \(N_{0}\). We call \(x_{rDi}\) a signal related to self-interference at user \(i\), and \(f_{i}\) is the self-interference channel and follows \(f_{i}\sim\mathrm{CN}\left(0,\lambda_{f_{i}}\right)\).  
Then, the received signal-to-interference-plus-noise ratio (SINR) at user \(1\)\(D_{1}\) becomes  
\[y_{SD1,k}^{FD-PDMA}=\frac{a_{1}\rho\big{|}g_{1,k}\big{|}^{2}}{a_{2}\rho\big{|} g_{1,k}\big{|}^{2}+\varpi\rho\big{|}f_{1}\big{|}^{2}+1}, \tag{2}\]  
where \(\rho=P_{S}/N_{0}\) is the transmit signal-to-noise ratio (SNR) which was measured at the BS.  
In this scenario, \(D_{2}\) is so-called as SIC user, i.e., SIC is required to eliminate interference from signal of \(D_{1}\). Firstly, the received SINR at user 2 to detect user1's message \(x_{1}\) is given by  
\[\gamma_{SD1=2,k}^{FD-PDMA}=\frac{a_{1}\rho\big{|}g_{2,k}\big{|}^{2}}{a_{2}\rho \big{|}g_{2,k}\big{|}^{2}+\varpi\rho\big{|}f_{2}\big{|}^{2}+1}. \tag{3}\]  
Figure 1: System model of FD aware D2D transmission mode in Power Domain based Multiple Access with Multiple Antenna at the BS.  
Then SIC is activated to eliminate interference from \(D_{1}\), the received SINRs at the user 2 \(D_{2}\) is calculated to decode its own signal as  
\[\gamma_{SD2,k}^{FD-PDMA}=\frac{a_{2}\rho\big{|}g_{2,k}\big{|}^{2}}{\varpi\rho \big{|}f_{2}\big{|}^{2}+1}. \tag{4}\]
# Robust and Secure Data Transmission Using Artificial Intelligence Techniques in Ad-Hoc Networks
## 5 Experiments and Analysis
### Comparative Analysis
The enhancement is shown in Table 7. To know the percentage enhancement against existing work, the percentage decrease was calculated. \(\left(\frac{0.087-0.04}{0.087}\right)\times 100=54.02\%\) from [40] and \(\left(\frac{0.035-0.04}{0.035}\right)\times 100=14.29\%\) against [13] improvement in the proposed work was obtained.
# Joint Optimization of Data Transmission and Energy Harvesting in Relay Satellite Networks
## 5 Performance Analysis
### Minimum Capacity of Battery  
In RSN, the satellite should work at the best performance with the support of enough battery capacity. Thus, the minimum capacity of the battery is derived to ensure the performance of RSN by considering that the satellite cannot transmit data due to lack of energy, i.e., \(E_{i}(t)\leq E_{\max}^{c}\). For the LTO problem, it is in the case that the ISL is not active, i.e., \(\delta_{ij}(t)=0\). Thus, we have \(\lambda_{i}(t)\geq 0\), which is contrary to (21). The appropriate size of \(\mathcal{B}\) for the energy queue is shown in Theorem 3.  
**Theorem 3**.: _Only and if only the residual energy cannot support the maximum energy consumption, i.e., \(E_{i}(t)\leq E_{\max}^{c}\), the battery capacity of satellite \(i\) can be defined as_  
\[\mathcal{B}=E_{\max}^{c}+\mathcal{D}_{\max}\frac{\xi_{\max}}{P_{t}},\quad i\in U. \tag{26}\]  
Proof of Theorem 3.: See Appendix C.  
As shown in (26), the required battery capacity \(\mathcal{B}\) is related to the maximum data storage size \(\mathcal{D}_{\max}\) of each satellite. The larger \(\mathcal{D}_{\max}\), the larger \(\mathcal{B}\) will be. This indicates that a larger battery capacity is required to provide sustainable energy to transmit the increased data backlog.
## 4 Numerical Simulation
### GMM-FTC Simulation  
To implement the simulation of GMM-FTC, parameters of the controller are given in Table 3 and the orbit elements of target spacecraft are given in Table 4. The initial relative motion parameters are given in Table 5. The simulation step size is set to h = 0.1. The thruster output error is set to \(\pm\)5%. Figure 5: The potential distribution of GMM-based artificial potential function. (**a**) The potential of GMM-based APF in \(x\)-\(y\) coordinate; (**b**) the potential of GMM-based APF in \(x\)-\(z\) coordinate; (**c**) the potential of GMM-based APF in \(y\)-\(z\) coordinate. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline \(\alpha_{1}\) & \(\text{diag}[3\times 10^{-1}\;3\times 10^{-1}\;3\times 10^{-1}]\) \\ \(\beta_{1}\) & \(\text{diag}[1\times 10^{-2}\;1\times 10^{-2}\;1\times 10^{-2}]\) \\ \(\zeta_{1}\) & 0.6 \\ \(\zeta_{2}\) & 1.5 \\ \(\lambda_{1}\) & \(\text{diag}[5\times 10^{-2}\;5\times 10^{-2}\;5\times 10^{-2}]\) \\ \(\lambda_{2}\) & \(\text{diag}[1\times 10^{-2}\;1\times 10^{-2}\;1\times 10^{-2}]\) \\ \(k_{s}\) & \(\text{diag}[3\times 10^{-4}\;1.0\times 10^{-4}\;1.0\times 10^{-4}]\) \\ \(\mathbf{M}\) & \(\text{diag}[6\times 10^{-1}\;6\times 10^{-1}\;4\times 10^{-1}]\) \\ \(\mathbf{P}\) & \(\text{diag}[8\times 10^{-1}\;8\times 10^{-1}\;8\times 10^{-1}]\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameters of Gaussian mixture model-fixed-time control (GMM-FTC). Then, the results of simulation are obtained, as shown in Figures 6-9. Figure 6 shows the trajectory of target spacecraft from initial position (blue point), to desired position (red point) under the GMM-FTC, and the spatial relationship between target spacecraft shape and trajectory. Figure 6b is the projection of Figure 6a in \(x\)-\(O\)-\(y\) plane. It can be seen that under the GMM-FTC, the service can implement the proximity operation process while avoiding collision with any structural component of the target. Figures 7-9 are the time response of position error \(\mathbf{y}_{1}\), velocity error \(\mathbf{y}_{2}\), and control force \(\mathbf{u}\), respectively. It can be seen that these parameters converge to small regions containing zero in 150 s. The stable precision of position error is better than \(1\times 10^{-4}\) m, as shown in partially enlarged view in Figure 7. From Figure 8, the amplitude of velocity is less than 0.15 m/s, which means the service keeps very low speed during proximity, which is good for improving the security. The precision is \begin{table}
\begin{tabular}{c c c} \hline \hline
**Elements** & **Value** & **Units** \\ \hline Semi-major axis, \(a\) & 6878.14 & km \\ Eccentricity, \(e\) & 0.01 & \\ Inclination, \(i\) & 45 & deg \\ Longitude of the ascending node, \(\Omega\) & 0 & deg \\ Argument of perigee, \(\omega\) & 0 & deg \\ Initial true anomaly, \(\sigma_{0}\) & 0 & deg \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Orbit elements of the target spacecraft. Figure 6: The proximity trajectory to target spacecraft. (**a**) The trajectory in 3D coordinates; (**b**) the projection of trajectory in the \(x\)-\(O\)-\(y\) plane.
## 5 Case Studies
### Results and Discussions
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**DMS**} & \multicolumn{3}{c}{**Duplicated DMS**} \\ \hline \multirow{2}{*}{**Local ID**} & \multirow{2}{*}{**TF**} & **Standard** & \multirow{2}{*}{**Count**} & \multirow{2}{*}{**Local ID**} & \multirow{2}{*}{**TF**} & **Standard** & \multirow{2}{*}{**Count**} \\  & & **Information** & & & & & **Information** & \\ \hline
1 & 111333210010 & 1474F971 & 1 & 1 & 111333210010 & 2075Q801 & 1 \\
2 & 111312342100 & - & 1 & 2 & 11133321000 & - & 1 \\
3 & 41233321000 & - & 1 & 3 & 111333211100 & 2075R782 & 1 \\
4 & 113110101100 & 2173G691 & 1 & 4 & 11312342100 & - & 1 \\
5 & 413110101100 & 1974F352 & 1 & 5 & 11212332100 & - & 1 \\
6 & 413110101100 & 2273C982 & 1 & 6 & 111333211100 & 2074B621 & 1 \\
7 & 111333211100 & 2075R782 & 1 & 7 & 113113342100 & - & 1 \\
8 & 311111131100 & - & 1 & 8 & 111333211200 & - & 1 \\... &... &... &... &... &... &... &... \\
5000 & 413010110100 & - & 1 & 5000 & 111333111000 & - & 1 \\
5001 & 111333111010 & - & 1 & 5001 & 111333210010 & 1474F971 & 1 \\
5002 & 413110101100 & 2075S263 & 1 & 5002 & 111223112100 & 2073H022 & 1 \\... &... &... &... &... &... &... &... \\
11455 & 11133311100 & - & 1 & 11455 & 111333111010 & - & 1 \\
11456 & 111333210010 & 2075Q801 & 1 & 11456 & 111333112100 & - & 1 \\
11457 & 111333211100 & 2074B621 & 1 & 11457 & 111223112100 & 2073H872 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Result of TFs generated by using standard information. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Original DMS** & \\ \hline \multirow{2}{*}{**Local ID**} & **TF without Standard** & \multirow{2}{*}{**TF with Standard Information**} \\  & & **Information** & \\ \hline
1 & 111333210010 & 111333210010-1474F971 \\
2 & 111312342100 & 111312342100 \\
3 & 412333321000 & 412333321000 \\
4 & 113110101100 & 113110100-2173G691 \\
5 & 413110101100 & 413110  
duplicated TFs decreases as the depth, defined as the level of a graph illustrated in Figure 14, increases. This means that more integers obtained by searched nodes result in more distinguishable characteristics of an object regardless of the application of the standard information. Furthermore, it is noted that there are some TFs that are not separable from other TFs at a certain level of depth. However, all the TFs turn into distinguishable ones so that the local ID mapping table can be generated when the depth of search goes to the level of 14 with the application of the standard information. In other words, not all integers in a TF need to be used to indicate the uniqueness of an object. For example, it is possible to create a unique TF for every object and generate a complete local ID mapping table even using only the integer string obtained by searching up to a depth of 14. This enables a reduction in memory space for storing TF strings and the time required for creating TFs even for numerous objects in a large and complex distribution system.
## 3 Lightweight Cryptography for Smart Objects
### 3.4. Homomorphic Encryption Schemes  
Homomorphic encryption is a form of encryption that allows specific types of computations to be executed on ciphertexts and obtain an encrypted result that is the ciphertext of the result of operations performed on the plaintext. By denoting \(E\{\cdot\}\) as the homomorphic encryption function and \(f(\cdot)\) as the computation function, it holds that:  
\[E\{f(a,b)\}=f(E\{a\},E\{b\})\]An example of homomorphic encryption is the RSA algorithm. Consider a modulus \(N\) and an exponent \(e\). The encryption of a message \(m\) is given by \(E\{m\}=m^{e}\:\text{mod}\:N\). The homomorphic property holds, since:  
\[E\{m_{1}\cdot m_{2}\}=(m_{1}\cdot m_{2})^{e}\:\text{mod}\:N=(m_{1})^{e}\:\text{ mod}\:N\cdot(m_{2})^{e}\:\text{mod}\:N=E\{m_{1}\}\cdot E\{m_{2}\}\]  
Other examples of homomorphic encryption schemes are the ECC encryption [38], the ElGamal cryptosystem [55] and the Pailler cryptosystem [56].  
Homomorphic encryption is receiving a growing interest for application into IoT scenarios, since it could be used to preserve confidentiality among the endpoints of communication, while making it possible for intermediate nodes to be able to process the information without the need to decrypt the data prior to processing. Homomorphic cryptosystems usually require higher computation and need longer keys to achieve a comparable security level than symmetric-key algorithms.  
Depending on the operation \(f(\cdot)\) that can be performed on the encrypted data, the homomorphic encryption scheme can be defined as _additive_ or _multiplicative_. Additive homomorphism makes it possible to compute sums, subtractions, and scalar multiplication of its operands; multiplicative homomorphism allows to compute the product of its operands. The RSA algorithm is an example of multiplicative homomorphic encryption. An example of additive homomorphic encryption is the Pailler cryptosystem. Given a modulus \(n\), a shared random integer \(g\), and user-generated random integers \(r_{1}\) and \(r_{2}\), the homomorphic property is:  
\[E\{m_{1}\}\cdot E\{m_{2}\}=(g^{m_{1}}r_{1}^{n}\:\text{mod}\:n^{2})\cdot(g^{m_{ 2}}r_{2}^{n}\:\text{mod}\:n^{2})=(g^{m_{1}+m_{2}})(r_{1}r_{2})^{n}\:\text{mod} \:n^{2}=E\{m_{1}+m_{2}\}\]  
Homomorphic encryption schemes that are either additive or multiplicative are denoted as _partially homomorphic_. If both addition and multiplication are supported, a cryptosystem is denoted as _fully homomorphic_. Fully homomorphic cryptosystems preserve the ring structure of the plaintexts and, therefore, enable more complex procedures. The investigation for fully homomorphic encryption schemes is still at early stages and no practical scheme has been found with acceptable performance (e.g., in terms of decryption delay). Application to IoT scenarios is a rich research topic.
# Molecular Imaging with \({}^{68}\)Ga Radio-Nanomaterials: Shedding Light on Nanoparticles
## 3 Radiolabeling Nanomaterials  
Successful design and development of radiolabeled nanoprobes requires careful analysis and consideration of numerous factors. Proper radioisotope selection is the initial step. There is a wide variety of radionuclides used as positron emitters (Table 1). Physical half-life of the radioisotope will set limits on its use in molecular imaging. Short half-lived isotopes are usually preferred for in vivo applications to limit exposure. However, if physical half-life of the chosen isotope is too short, or mismatches the biological half-life of the nanoprobe, visualization of the target or process studied will not be achieved.  
\begin{table}
\begin{tabular}{c c c} \hline
**Radioisotope** & **Half-Life** & **Production Method** \\ \hline \({}^{11}\)C & 20.4 min & Cyclotron \\ \({}^{13}\)N & 9.97 min & Cyclotron \\ \({}^{15}\)O & 122 s & Cyclotron \\ \({}^{18}\)F & 110 min & Cyclotron \\ \({}^{62}\)Cu & 9.74 min & Cyclotron, Generator \\ \({}^{64}\)Cu & 12.7 h & Cyclotron \\ \({}^{68}\)Ga & 67.7 min & Cyclotron, Generator \\ \({}^{76}\)Br & 16.2 h & Cyclotron \\ \({}^{82}\)Rb & 76 s & Generator \\ \({}^{89}\)Zr & 78.4 h & Cyclotron \\ \({}^{124}\)I & 4.18 days & Cyclotron \\ \hline \end{tabular}
\end{table}
Table 1: Radioisotopes used in positron emission tomography (PET).  
By virtue of the outstanding sensitivity of nuclear imaging techniques, low concentration of the radionuclide is usually required. Hence, nanoparticle radiolabeling is expected to minimally alter nanoparticles' physicochemical properties and pharmacokinetics. Nevertheless, nanoparticle and radioisotope must form a chemically stable complex. Incorporating radioisotope to the nanoparticle will modify the core or the surface of the nanoparticle, depending on the radiolabeling strategy (Figure 1).  
For this reason, radiolabeling strategy must be optimally designed to be fast and reproducible as well as to minimize potential radionuclide dissociation from nanoparticles [20].  
Gallium, in aqueous solutions, is almost exclusively found in oxidation state +3, being highly influenced by pH. Optimal pH conditions range from pH 3 to pH 5. An excessively acidic media may protonate donor atoms and hence inhibit complex formation; and a neutral or basic environment may cause unreactive hydroxides formation.
# C2B: A Semantic Source Code Retrieval Model Using CodeT5 and Bi-LSTM
## 5 Experimental Results
### Practical Application of the C2B Hybrid Model  
The C2B hybrid model offers significant practical application value in the domain of code search and retrieval. By combining the strengths of CodeT5 for natural language processing and Bi-LSTM for contextual code comprehension, the C2B hybrid model provides a robust and versatile solution capable of accurately understanding and matching complex queries with relevant code snippets. This dual approach ensures that the model not only grasps the semantic nuances of natural language queries but also captures the contextual dependencies within code, leading to superior performance across various programming languages and scenarios.  
In practical terms, the C2B hybrid model enhances developer productivity by reducing the time and effort required to locate pertinent code snippets from large codebases. This is particularly beneficial in environments where quick access to specific code segments is critical for debugging, code review, or learning purposes. Additionally, the model's ability to handle diverse code search tasks makes it a valuable tool in educational settings, where students and instructors can use it to find relevant examples and improve coding skills.  
Furthermore, the high accuracy and effectiveness of the C2B hybrid model in retrieving relevant code can lead to improved code quality. By facilitating the reuse of well-tested and optimized code snippets, developers can minimize errors and redundancies, thereby enhancing the overall robustness of software projects. The model's practical application extends to automated code documentation and intelligent code assistants, which can leverage its capabilities to provide context-aware suggestions and insights, ultimately streamlining the software development life cycle.
## 2 Laboratory Model Test  
### Model Test Equipment  
The chamber used for the laboratory model test is shown in Figure 2. Its dimensions were 1500 mm \(\times\) 100 mm \(\times\) 700 mm (width \(\times\) depth \(\times\) height). It had an aluminum frame with front and rear acrylic plates. On the acrylic plates, 50 mm \(\times\) 50 mm grids were engraved to allow the deformation of the prepared soil to be observed with the naked eye. A hole with a diameter of 100 mm was prepared at the center of the chamber for the  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Area That Cannot Be Penetrated** \\
**Country** & **by Structures** \\ \cline{2-3}  & **Crown (m)** & **Crown (m)** \\ \hline London Underground Limited standard & 32m & 3 \\ Japanese geotechnical society standard & 5 & 0.5m \\ \hline Seoul Metropolitan Rapid Transit standard & **4-6m** & **4-6m** \\ Busan City standard & **4-6m** & **4-6m** \\ Busan City standard & **4-6m** & **4-6m** \\ \hline Busan City standard & **4-6m** &  
installation of the model tunnel. The loading device could move to the left or right of the chamber. It could apply loading through a connection to a shallow foundation, a single pile, or grouped piles.  
The literature contains several studies that used a sand-pouring device to generate uniform sandy soil in model chambers [13; 14; 15; 16]. In this study, a 1500 mm \(\times\) 100 mm (width \(\times\) height) sand-pouring device was used (Figures 3 and 4). The lever on the left side of the device was used to control the pouring of sand. The height of the device was adjusted with a hoist according to the height of the sand accumulated in the chamber so that the relative density of the sandy soil inside the chamber could be held constant.  
Figure 3: Sand-pouring device and model test chamber.  
Figure 2: Size of model test chamber.  
The model tunnel (Figure 5) was fabricated using a 70 mm diameter aluminum cylinder. It corresponded to a 10 m tunnel at 1/150 scale. Owing to the conditions of the test, the stiffness of the tunnel support and lining according to the tunnel standard specifications [17] and tunnel design standards [18] could not be reflected in the model. The thickness of the aluminum model tunnel (2 mm) was chosen to withstand the weight of the sandy soil and the surcharge loads. Rubber was attached to both ends of the model tunnel to prevent the loss of sandy soil inside the chamber. Eight strain gauges were attached to the model tunnel in all directions to measure the deformation of the tunnel.  
A pile made of aluminum was embedded in the sandy soil during the test. In general, a single steel pile has a size corresponding to approximately 1/10 of the tunnel size and has a diameter between 500 and 1200 mm. As shown in Figure 6, a single pile of size 7 mm \(\times\) 100 mm \(\times\) 250 mm (width \(\times\) depth \(\times\) height) was used in the test, and a 1/150 scale effect was applied. The load was increased using the loading device, and the behavior of the existing tunnel and surrounding ground under the influence of the pile was measured.  
Figure 4: Sand-pouring device.  
Figure 5: Model of tunnel.