esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 PAPR Reduction Procedure of PA DCO-OFDM
The estimate of the pilot's angle is taken between two values only (\(\hat{\theta}_{i}=0\) and \(\pi\)), that has the minimum Euclidean distance from the phase of the received pilot's \(\hat{\theta}_{\text{p}}[m]\). The estimate argument of the angle is defined by the following:  
\[\hat{\theta_{\text{p}}}[m]=\underset{1\leq i\leq 2}{\text{argmin}}[(\hat{ \theta_{\text{p}}}[m]-\theta_{\text{i}})^{2}] \tag{13}\]  
The estimated pilot sequence can be given by \(\hat{X}_{\text{p}}[m]=e^{\hat{\theta_{\text{p}}}[m]}\), which is equivalent to the following condition [5]:\[\hat{X}_{\mathrm{p}}[m]= \begin{cases}+1,&\text{if}\quad\cos\left(\hat{\theta}_{ \mathrm{p}}[m]\right)\geq 0\\ -1,&\text{otherwise}.\end{cases}  \tag{14}\]  
Additionally, the amplitude of the pilot sequence, \(\hat{A}_{\mathrm{p}}[m]\), is maintained to unity by using the condition given in (14). The estimated phase, \(\hat{\theta}_{\mathrm{p}}[m]\), of the pilot sequence with its recovered amplitude, \(\hat{A}_{\mathrm{p}}[m]\), is then applied to estimate and recover the phase of the corresponding subcarrier, \(m\), of the relevant block, \(U\), of the received data symbol. Figure 3 shows the PAPR reduction gain of PA with 10 iterations. This result quantifies the PAPR reduction gain compared to the basic DCO-OFDM without PAPR reduction. For example, the PA DCO-OFDM system yields almost a 3 dB PAPR reduction gain compared to the conventional DCO-OFDM at a CCDF of \(10^{-4}\).
# Leveraging IPv6 and ICMPv6 for Delay-Tolerant Networking in Deep Space
###### Abstract
Thankfully, years of innovation and development of the IP, and in particular the introduction of Internet Protocol version 6 (IPv6), allow us to perform a re-evaluation of its possible application in deep space [11]. Recent work in this field defends that a fully IP architecture is capable of providing interplanetary connectivity [12], without the needed rework of existing infrastructure that BP requires. Some research work has already been carried out in this sense, and a notable example of this is the paper \"Adding Support for Delay Tolerance to IPv6 Networks\" [13], where the authors managed to replicate DTN functionalities in the IP by developing a new hop-by-hop extension header. The main drawbacks of the design were the reduced scope and resilience of the solution and that it did not account for the fact that, as a general practice, many nodes discard packets containing the hop-by-hop option header [14] to mitigate potential efficiency and security concerns. Given the recognized success of the BP in DTN, and the peculiar position of IPv6 as the predominant networking protocol in existing networks, the aim of this article is to compare the functionalities of BPv7 and IPv6, investigating the possibility of replicating the main features of the first directly in the second. Then, we propose a new architecture for an IP-compliant DTN-capable node. Such a device would open the possibility of building an IP network that can operate over DTN environments. This node would provide DTN features while interoperating with existing IP networks, providing a solution also applicable to challenged terrestrial networks, like intermittently connected wireless sensor networks and Internet of Things (IoT) mobile ad hoc networks. The remainder of this paper is organized as follows. Section 2 analyzes the initial differences and similarities between BPv7 and IPv6, identifying the absence of functionalities that will then be addressed in Section 3 with the description of the architecture. Section 4 de tails the implementation process, including the modifications and enhancements needed to integrate the missing functionalities. Finally, Section 5 summarizes the findings, discusses their implications, and outlines potential future work.
## 4 Conclusions  
This article pertains to the numerical simulation of a thin-walled perforated beam working in a structural system with a steel grating. The main objective of the study was to develop the load-bearing curves shown in Figure 24 for the main beam within the span range of 3-6 m and to identify patterns of destruction depending on the span of the system.  
Figure 23: Distribution of reduced Mises stresses and deformation of the system with a span of 6 m—view without the bridge deck.  
Figure 24: Load-bearing curves developed based on simulations of structural systems with spans ranging from 3 m to 6 m.  
The validation of the developed numerical model through laboratory tests confirmed its accuracy and enabled the simulation of the behavior of the structural system covered with steel gratings over the entire span. Based on the conducted analyses, the following final conclusions can be formulated:  
1. The study of 6 m beams showed a pattern of destruction through loss of stability, while 3 m beams were destroyed through yielding.
2. Numerical simulations and laboratory tests demonstrated analogous destruction patterns.
3. Measurements of horizontal displacements in all tests showed greater displacements in the lower flange of the beam. This is consistent with expectations, as the upper part of the beam is somewhat \"braced\" by the bridge deck.
4. This observation was also taken into account in the numerical model by applying normal and tangential contact with friction between the bridge deck and beams.
5. Based on the conducted tests, it is recommended to place additional bracings between the lower flanges of the beams.
6. The connector and the support zone of the beam are the weakest elements of the tested system. Each time, the beam's destruction process started from the yielding of the connector, which was observed in experimental studies and confirmed in the numerical model for all tested lengths.
7. The presented study contributes to a better understanding of the operation of such structural systems.  
Conceptualization, A.D. and T.S.; methodology, A.D. and G.L.; software, A.D.; validation, A.D. and T.S.; formal analysis, A.D. and K.K.; investigation, A.D. and G.L.; resources, K.K. and W.M.; data curation, K.K., W.M., and W.B.; writing--original draft preparation, K.K. and A.D.; writing--review and editing, A.D. and K.K.; visualization, A.D.; supervision, W.B. and G.L.; project administration, T.S.; funding acquisition, T.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.
## 5 Technology Readiness  
In order to generate a gap analysis with a focus on measuring the technological readiness of CLC and CL-PKC, for the C-ITS domain specifically, a technology readiness framework is needed. Several frameworks for technology readiness have been used in previous research and in industry, e.g., the well-known _Technology Readiness Levels_ (TRL) developed by the National Aeronautics and Space Administration (NASA) [15]. Several other types of different frameworks can be used to measure an organization's readiness to adopt a new technology, with different perspectives; the Technology Readiness Index (TRI) measures an organization's readiness to adopt new technology by assessing its technological infrastructure, human resources, and organizational culture [34], or the Technology Acceptance Model (TAM) assess the attitudes and perceptions of an organization's members towards the technology [35]. The TAM framework evaluates an organization's perceptions of the technology's ease of use and usefulness, and how these perceptions influence its adoption. No cryptography readiness frameworks, aimed for C-ITS or similar domains, exists. However, due to the increasing interest in quantum computing, several frameworks and readiness strategies for adopting post-quantum secure cryptography have been proposed [36; 37].
# Adaptive DecayRank: Real-Time Anomaly Detection in Dynamic Graphs with Bayesian PageRank Updates
## 4 Proposed Method
### Dynamic Node Scoring with Temporal Decay
#### 4.1.2 Temporal Decay Factor  
The concept of our proposed novel _adaptive-decay factor_ formula is inspired by the work on _\"Weight Decay Regularization in the ADAM Optimizer\"_[10], where the authors propose decoupling gradient-based weight updates in both the SGD [11] and Adam [12] optimizers.  
In Equation (4), we introduced the temporal decay function \(e^{-\delta\cdot(t-A_{u}^{(T)})}\), which enhances the adaptability and responsiveness of our algorithm. The parameter \(\delta\), referred to as the _decay factor_, controls the rate at which the influence of an edge or node decreases over time. A higher \(\delta\) value results in faster decay, gradually reducing the impact of older interactions or edges. This ensures that older data have progressively less influence on the computation of new graph snapshots and allows the algorithm to prioritize recent and relevant changes in the graph structure. The variable \(t\) represents the current timestamp, while \(A_{u}^{(T)}\) denotes the timestamp attribute of node \(u\). The term \((t-A_{u}^{(T)})\) calculates the time difference between the current timestamp and the last update of node \(u\).  
Algorithm 1 provides a summary of our previous work on the DecayRank algorithm, which employs a **fixed decay factor**. While effective, this fixed factor struggles to adapt to the varying dynamics of individual nodes, potentially resulting in suboptimal anomaly detection and reduced sensitivity to recent changes in the graph.  
To overcome these limitations, we propose the Adaptive-DecayRank algorithm, which utilizes a Bayesian updating mechanism to dynamically adjust the decay factor in response to observed changes in node rank scores. This adaptive approach not only enhances the algorithm's responsiveness to evolving graph structures but also significantly improves the accuracy and robustness of anomaly detection in dynamic and near real-time graph environments.  
**Input:**_A_: Array of dynamic graph outEdges over time  
**Output:**_v_: Updated PageRank scores  
```
1:Initialization:
2:Initialize PageRank values \(v[i]\leftarrow\frac{c}{n}\) for all nodes \(i\)
3:while each timestep \(step\) from 0 to \(numSteps-1\)do
4:\(\triangleright\)Power Iteration:
5: Update scores based on graph structure.
6:\(\triangleright\)Decay Factor Application:
7: Apply decay factor \(\delta\) to adjust scores over time.
8:\(\triangleright\)Update PageRank Scores:
9:\(\triangleright\)Integrate new scores: \(v[i]\)\(+=\)\(nq[i]\)
10:endwhile\(\triangleright\)Normalization Anomaly Score:
11:return\(v\leftarrow\) normalizeNodeScore(\(v,n\))
```  
**Algorithm 1**DecayRank: Streaming Anomaly Scoring
## 2 Materials and Methods  
### Genome-Wide Identification and Chromosomal Distribution of OSC Gene Family in G. pentaphyllum  
To identify members of the _OSC_ gene family across the genome of _G. pentaphyllum_, we initially downloaded all known _OSC_ genes of _A. thaliana_ available in the TAIR library ([[http://www.Arabidopsis.org/](http://www.Arabidopsis.org/)]([http://www.Arabidopsis.org/](http://www.Arabidopsis.org/)), accessed on 29 December 2023). Additionally, sequences of previously identified _OSCs_ from _P. ginseng, P. notoginseng, P. quiueifolium, Luffa cylindrica, Cucurbita pepo_, and _Momordia charantia_ were retrieved from NCBI (Table S1). Subsequently, BLAST searches were conducted against the database for _G. pentaphyllum_ ([[https://www.ncbi.nlm.nih.gov/datasets/genome/GCA_028015215.1/](https://www.ncbi.nlm.nih.gov/datasets/genome/GCA_028015215.1/)]([https://www.ncbi.nlm.nih.gov/datasets/genome/GCA_028015215.1/](https://www.ncbi.nlm.nih.gov/datasets/genome/GCA_028015215.1/)) [30], accessed on 29 December 2023), utilizing an E-value threshold \(\leq 1\times 10^{-5}\) with data assembled and annotated through the BLASTP online tool [31]. Next, HMMER 3.0 [32] was employed with default parameters to search for _OSC_ coding proteins based on hidden Markov models corresponding to both the C-terminal domain (PF13243) and N-terminal domain (PF13249) of squalene-hopene cyclase [17]. Candidate genes containing both domains were validated using Pfam ([[http://pfam.xfam.org/](http://pfam.xfam.org/)]([http://pfam.xfam.org/](http://pfam.xfam.org/)), accessed on 31 December 2023) as well as the Conserved Domain Database (CDD) at NCBI ([[https://www.ncbi.nlm.nih.gov/cdd/](https://www.ncbi.nlm.nih.gov/cdd/)]([https://www.ncbi.nlm.nih.gov/cdd/](https://www.ncbi.nlm.nih.gov/cdd/)), E-value of \(1\times 10^{-5}\), other parameters set to defaults, accessed on 31 December 2023 [33]), thereby identifying them as _GpOSCs_. The chromosomal distribution of target _GpOSC_ genes within each chromosome of \(G\). _pentaphyllum_ was determined by utilizing whole-genome annotation files along with gene density information via the Gene Location Visualizer tool in the GTF/GFF function module of TBtools version 2.096 [34].
# Simulation-Based Analysis on Operational Control of Batch Processors in Wafer Fabrication
## 3 Operational Control Rules for Simulation Analysis
In this control scheme, the loading decision is made with the MBS rule in an ordinary situation. However, when the bottleneck station is expected to be idle in the near future, loading activities are initiated by forming a batch with wafer lots in the queue no matter how many wafer lots are waiting in the queue at the BP station. Here, we define the concept of virtual WIP as the work content of all jobs either in the waiting queue at the BOT station or on the way to the BOT station after visiting a BP station. Let \(V\) be the virtual WIP, \(p\) be the mean processing time per batch at the BP station and \(m\) be the number of bottleneck processors. Then, it is expected to take \(pV\) hours for the BOT station to finish all the jobs in the virtual WIP. With \(m\) BP processors in the BP station, the expected BOT starvation start time, \(s\), is \(s=pV/m\) hours when no more new jobs are additionally released from the BP station. Let \(t\) be the time required to arrive at the BOT station after being loaded on a batch processor under the condition that no waiting time is experienced on the way to the BOT station. If \(s<t\), then the BOT station is expected to be idle, due to starvation. As a result, it is recommended to load the jobs immediately at the BP station to minimize the idle time of the bottleneck machines. To minimize the downtime due to starvation, some buffer WIPs are needed to absorb the flow variability. The loading decision is made based on the target BOT workload, \(at\), where \(\alpha\) is the buffer factor considering system variability. An appropriate value for \(\alpha\) (\(>\)1) is selected through preliminary simulation experiments. The LKDN control rule is summarized in Figure 5.
# All about Delay-Tolerant Networking (DTN) Contributions to Future Internet
## 3 Related Work  
Categorizing the application domains that utilize DTN protocols poses a significant challenge due to the constantly expanding spectrum of DTN usage. The current review aims to address some of the most significant and broadest scientific areas in which DTNprotocols find application, in particular (i) space; (ii) Information-Centric Networking (ICN) and specific architecture designs, e.g., Named Data Networking (NDN); (iii) the Internet of Things (IoT) and its applications in smart cities; and (iv) underwater applications. Before delving into a detailed analysis of DTN implementations within these specific areas, we provide an overview of each domain.
## 9 The Future: The Challenges in Smart Grids  
### The key Challenges in Smart Grids  
The smart grid has benefits in energy conservation, low environmental pollution, higher energy efficiency, self-healing properties, and bidirectional communication. Despite the enormous benefits stated, smart grid achievement is quite far away. Listed below are smart grid challenges, which are also potential research areas:  
1. Problems in smart grid routing design [65].
2. Issues related to cybersecurity [57].
3. Interoperability and the incorporation of renewable energy [66; 67].
4. Regulatory challenges [29].
5. Compatibility issues of intelligent devices, communications, and data logging standards [30].
6. Security in the handling and study of big data [68].
7. Automation of distributed system and distributed generation in distribution system [31].
8. Interoperability standard, cognitively recovering unlicensed spectrum and enhancing cybersecurity [69].
9. Physical and cyber-security, advanced metering system vulnerabilities [70].
10. Obstacles to smart grid implementation: costs and advantages, institutional in-action [71].
11. Vehicle-to-grid (V2G): low electric vehicle penetration [72].
## 5 Experimental Results  
The average and standard deviation of AUROC for TCN and RNN are presented in Tables 13 and 14, respectively. The following observations can be made from these tables. First, the Coordinate-A-based model (estimating the x and y coordinates based on the available time, x and y coordinates, and angular information) produced the best result for both TCN and RNN for all cases except one (Korean 3). Furthermore, the TCN model \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Loss** & **Batch Size** & **Learning Rate** & **Optimization Method** & **Initialization Method** & **Number of Iterations** \\ \hline Huber loss & 10 & \(10^{-2}\)–\(10^{-5}\) & RMSProp & He & 300 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Training guide details. Figure 10: Two types of performance indices. (**a**) AUROC; (**b**) Youden index (\(J\)) by threshold. based on the FULL method produced average AUROC of 0.001 greater than that of the Coordinate-A-based TCN model. In the case of Korean 3, the difference in the performance of the aforementioned models is extremely small. Second, by comparing the performance of the two models, Coordinate-B, i.e., excluding the angular velocity-based features, and Angular-B, i.e., excluding the coordinate-based features, we confirmed that the former exhibited relatively superior performance for all cases. \[\mu_{TCN}>\mu_{RNN} \tag{14}\]  
Based on these results, it can be concluded that coordinate-based features are more suitable than angular velocity-based features for user authentication. Third, although all participants knew the authenticated user's drawing pattern (e.g., shape, direction, and number of strokes) in our experimental design, the Coordinate-A-based model yielded higher than 0.990 average AUROC regardless of the algorithm or pattern type used in this study. Therefore, it can be confirmed that the proposed DDP is robust to threat scenarios, such as SSA and SA. Moreover, we can expect superior user-authentication performance in real-world scenarios than that shown by our experimental results, because the authenticated user's drawing pattern may not be available to the intruders in the former. Fourth, in most cases, the TCN-based model exhibited better authentication performance than the RNN-based model. Table 15 presents the \(p\)-value of the paired \(t\)-test for the hypothesis shown in Equation (14). In most cases, the \(p\)-value is less than or equal to 0.05, which indicates that the authentication performance improvement of the TCN-based model and the RNN-based model is statistically significant in general.
# A Consensus-Based Likert-LMPP Model for Evaluating the Earthquake Resistance of Existing Buildings
## 3 Methodology
### The Consensus-Based Likert-ANN Model
#### 3.5.1 Consensus Degree  
Experts will evaluate each vulnerability parameter individually using the form (Appendix A) based on a five-point Likert scale. Following that, the risk index (_RI_) based on the consensus value for each parameter will be calculated using the equation shown below.  
\[RI=Cns*\sum_{i=1}^{N}\frac{a_{i}}{5N} \tag{1}\]  
where \(Cns\) stands for the consensus degree of responses to each parameter, \(N\) indicates the total number of experts (suggested five), and \(a_{i}\) is the constant that represents the weight assigned to each response (in this case, very low = 1; low = 2; moderate = 3; high = 4; and very high = 5). Individuals in a sample group are said to be in consensus when they agree on a declarative statement [59]. Based on this definition, if an equal number of participants choose their responses in the two extreme categories on a Likert scale, i.e., strongly disagree and strongly agree, there is no consensus; however, this group shows full consensus if all participants choose the same category on the Likert scale. Therefore, for all combinations of response patterns, the consensus degree varies between zero and one. [PERSON] and [PERSON] [59] concluded that the consensus provides a value for comparing  
Figure 1: The structure of the consensus-based Likert–ANN model, only the output processing frame was adapted from [PERSON] et al., 2014 [58].  
different Likert distributions and matches human intuition, and suggested calculating a consensus (_Cn_s) degree as follows:  
\[Cns(X)=1+\sum_{i=1}^{n}p_{i}log_{2}\bigg{(}1-\frac{|x_{i}-\mu_{x}|}{d_{x}}\bigg{)} \tag{2}\]  
where \(X\) is the response, \(\mu_{x}\) is the mean of \(X\), \(n\) represents the number of categories on an ordinal scale, \(x_{i}\) is the degree of agreement in category \(i\), \(p_{i}\) is the probability of the occurrence of \(x_{i}\), and \(d_{x}\) is the width of \(X\), i.e., \(d_{x}=X_{max}-X_{min}\) (in this case, \(d_{x}=5-1=4\)).
## 5 Methods, Results and Discussion
### Conveyance Validation and Return Step Influence  
Figure 11 shows a sequence of pictures at each conveyance step (explained in Figure 3). The displacement step is clearly visualized in Figure 11b once the MPMs have moved. Then the return step impact on the object position is shown in Figure 11c.  
The trajectory controller assumes that the return step (the MPM reset process once a displacement step has been done) does not displace the object. In practice, a return displacement was observed, attributed to possible uneven distribution of the conveyed object weight on the MPMs and heterogeneity in friction conditions. The return current intensity influence on this return step was studied to find the best operating conditions for multi-step execution.  
Different return current intensities along the \(x\) and \(y\) axes of a \(2\times 2\) DAA were injected following the return process. The DAA carried a 0.416 g object. Each return current intensity was executed 20 times to obtain a mean and standard deviation values. Figure 12 presents the return step displacement vs. return current intensity. From Figure 12, different behaviors were observed for the \(x\)-axis and \(y\)-axis.  
For the \(y\)-axis, the return displacement and its standard deviation decreased as the current intensity increased, until 8 A. For the 9 A point, both return displacement and standard deviation increased from the previous experimental point. For the \(x\)-axis, the return displacement and its standard deviation increased when the return current intensity went from 5 to 6 A. Then, the return displacement and its standard deviation collapsed to a minimum at 7 A. Increasing the return current intensity further than 7 A resulted in a steady increase in return displacement and standard deviation. This behavior is attributed to the \(z\) component of the electromagnetic force. For such high intensity currents, this force component overcomes the MPM and conveyed object's weight, making the MPM to \"jump\" inside its cavity. This jump produces a highly variable movement of the object along the magnet's displacement axis. The return current is then fixed at 7 A for both axes to reduce its influence on the conveyance task.  
Figure 11: Object to be conveyed on the DAA (**a**), displacement step measure (**b**) and return step influence (**c**).  
Figure 12: Return current influence on return displacement.