esa-sceva/satcom-chunk-collection huggingface dataset:
# Complete Subhedge Projection for Stepwise Hedge Automata +
## Appendix A Earliest Membership
### Soundness and Completeness  
We next provide soundness and completeness results for our earliest in-memory membership tester with projection.  
**Proposition A1** (Soundness).: _For any Sha \(A\) with schema \(\mathbf{S}=\mathcal{L}(A[F/F_{\mathbf{S}}])\), hedge \(h\in\mathbf{S}\), and partial run \(v\) of \(A_{e(\mathbf{S})}\) on \(h\) that ends in state sel, then \(h\in\mathcal{L}(A)\)._  
So, if the partial run reaches state _sel_ then \(\mathit{proj}_{\Sigma}(v)\) is certain for membership to \(\mathcal{L}(A)\) with respect to \(\mathbf{S}\). Evaluating a Sha\({}^{\downarrow}\)s \(A_{e(\mathbf{S})}\) yields an earliest in-memory membership tester for dSha \(A\). A single adaptation is as follows: whenever _sel_ is reached, the evaluation can stop all over and accept the input hedge.  
**Theorem A1** (Completeness).: _Let \(A=(\Sigma,\mathcal{Q},\Delta,I,F)\) be a dSha that is schema-complete for schema \(\mathbf{S}=\mathcal{L}(A[F/F_{\mathbf{S}}])\). For any hedge \(h\in\mathbf{S}\) and prefix \(v\) of \(\mathit{nw}(h)\):_  
* _If_ \(v\) _is certain for membership in_ \(\mathcal{L}(A)\) _with_ \(\mathbf{S}\)_, then there exists a partial run_ \(w\) _for_ \(A_{e(\mathbf{S})}\) _on_ \(h\)_, such that_ \(v=\mathit{proj}_{\Sigma}(w)\) _and_ \(w\) _ends with state sel._
* _If_ \(v\) _is certain for nonmembership in_ \(\mathcal{L}(A)\) _with_ \(\mathbf{S}\)_, then there exists blocking partial run_ \(w\) _for_ \(A_{e(\mathbf{S})}\) _on_ \(h\)_, such that_ \(\mathit{proj}_{\Sigma}(w)\) _is a prefix of_ \(v\)_._  
This means that certainty for membership and nonmembership is detected at the earliest prefix when running the evaluator for \(A_{e(\mathbf{S})}\).  
Proof.: We notice that this algorithm is basically the same as the earliest membership tester from Proposition 6 of [21], except that it also check for safe rejection and that it accounts for schemas. The fact that Nwas are used there instead of Sha\({}^{\downarrow}\)s here is not essential. So, we can lift the soundness and completeness proof from there without problem.
# Research Progress on Flexible Oxide-Based Thin Film Transistors
## 3 Approaches to Flexible Oxide-Based TFTs
### Flexible TFTs on Different Kinds of Substrates
#### 3.1.1 PET Flexible Substrate
These variations in the mobility and threshold voltage between the TFTs mainly come from two factors: The first one is that the channel of the un-passivated TFT was exposed to the ambient, which resulted in the degradation of the device. The second is the strain effect. When bending the TFTs, stress coming from the mechanical deformation directly concentrates on the active layer in device (a), whereas in the device (b) it concentrates in the passivation layer. As mentioned above, higher quality gate dielectric layers can hardly be deposited on PET substrates due to its low process temperature limit (<100 \({}^{\circ}\)C), which remains the biggest barrier for PET to become promoted. For the further development of TFT technology and for higher device performance, new substrate materials with a higher process temperature limit should be launched. Figure 4: Schematics of bottom-gate-type indium gallium zinc oxide (IGZO) TFT [151], (**a**) un-passivated structure, (**b**) passivated structure. Figure 3: (**a**) All-amorphous-oxide transparent TFT [71]. (**b**) ZnO-TFT with an organic-inorganic heterostructure [69].
## 1 Introduction
However, the development and implementation of new signal methods is an ambitious task, as it should include not only the creation of an algorithm and its study using different types of objects but also evaluation of their reliability. In accordance with the protocols and requirements specified in [30; 31], each measurement method must be reliable, accurate, and precisely tailored to the intended purpose. Thus, the feasibility of the proposed method should be executed after its development to determine the performance characteristics [32]. The basic characteristics developed for assessing the reliability of the SPMs and the influence factors of the combined uncertainty should be indicated. The presentation of such information would facilitate the application and utilisation of the proposed methods and their choice for completing a certain task. The main goal of this work is to present Lamb waves as one of the most promising tools for safety assurance of WTBs that detect defects during manufacture and perform monitoring of structural changes during the WBTs service life. In addition, to demon strate that the special SPMs are used for the analysis of Lamb waves signals only in the investigation stage in order to develop standards for WTBs certification. The work aims to:  
* Highlighting the main shortcomings of the SPMs used for the analysis of Lamb waves that complicate the preparation process of reliable testing, inspection, and monitoring standards for WTBs certification. * Present the main conditions and impact factors of the combined uncertainty, which are necessary to be performed for the verification and accuracy assessment of the SPMs according to the requirements of ISO 17025. The arrangement of the article is organised as follows. Typical defects in the construction of the WTB and methods for safety and quality assurance based on ultrasonic Lamb waves are presented in Section 2. Section 3 presents the results related to limitations of Lamb waves application and reliability evaluation of SPMs. The concluding remarks are presented in the Section 4.
# Satellite Network Security Routing Technology Based on Deep Learning and Trust Management
## 4 Experimental Simulation and Analysis
### Analysis of Results
#### 4.2.3 Network Throughput  
The amount of network throughput refers to the maximum rate the system can accept without losing data packets. Bit/second or byte/second represent the throughput test results. In the following graphs, the number of malicious nodes affects the throughput of the network. As the number of malicious nodes increases, the average network throughput of the network exhibits a decreasing trend.  
When the network was maliciously attacked, because the ACO algorithm did not adopt any security trust mechanism, it was difficult to deal with the attack behavior of malicious nodes. Therefore, the number of throughputs increased by the number of malicious nodes. It is equipped with a trust mechanism, so it can be identified in advance when dealing with malicious attacks to slow down the effect of malicious attacks. Therefore, the three algorithms decreased without the ACO algorithm when there was an increase of malicious nodes.  
In the same way, because the TAODV algorithm's trust model is relatively simple, it has a general effect when dealing with malicious attacks. Therefore, its throughput was compared to SLT and TVAE algorithms. The SLT algorithm and its trust model are more complicated than TAODV, and there is a dynamic health detection module. Therefore, its throughput decreased moderately. Finally, the TVAE algorithm is used for abnormal detection and its trust model is more sensitive when detecting malicious nodes' attacks, so  
Figure 7: The impact of a malicious attack on the package rate.  
it can effectively avoid the malicious packet loss attack launched by malicious nodes. Thus, its throughput declines the slowest.  
As indicated in Figure 8, when the number of malicious nodes is 0, the ACO algorithm exhibits the highest network throughput, surpassing the TAODV algorithm by 7 kbps, the TVAE algorithm by 8 kbps, and the SLT algorithm by 11 kbps. The reason behind this outcome is the absence of time overhead due to a security trust model in the ACO algorithm, allowing it to process more task requests within a unit of time. However, as the number of malicious nodes reaches four, the network throughput of the ACO algorithm becomes lower than that of the TAODV and TVAE algorithms. Furthermore, with an increase in the number of malicious nodes, the ACO curve steadily declines. Ultimately, when the number of malicious nodes reaches 16, the network throughput of the ACO algorithm drops to only 60 kbps.  
This outcome is attributed to the fact that in the presence of packet loss caused by malicious attacks, the ACO algorithm's probability of timeout and retransmission significantly increases. As a result, the quantity of tasks processed per unit of time by the ACO algorithm experiences a substantial decrease.
# Time-Aware and Feature Similarity Self-Attention in Vessel Fuel Consumption Prediction
###### Abstract
In this study, we propose each attention-based model and their ensemble model to accurately predict fuel oil consumption by representing the ship data properties. We use bi-directional LSTM [14] as the backbone model to consider sequential property. For irregular time interval property, we design **Time-aware Attention** (TA) based on self-attention. TA represents irregular time information and alleviates the problem of time irregularity in sequence models. To reflect the feature importance property, we propose **Feature-similarity Attention** (FA). FA, which estimates feature importance, emphasizes data based on feature similarity. Finally, we adopt the **ensemble** model of TA and FA-based BiLSTM, which makes it feasible to reflect two different properties simultaneously. Experimental results on the ship data show that the proposed models outperform the other sequence and attention-based models. Furthermore, attention maps of different scenarios and feature importance values illustrate the relationship between data properties and models, which increases interpretability. Figure 1: Irregular time interval plot examples. Each plot shows acquired sequential ship data during each voyage and their time difference. The time difference is calculated by \(t_{(i+1)}-t_{i}\), where \(t_{i}\) is the time when the \(i\)th data was acquired during the voyage. The examples are sampled from the dataset used in this paper. Figure 2: Correlations between features including a dependent variable, fuel oil consumption (FOC). In the correlation matrix, the correlation is higher as the color becomes darker. The correlation is calculated by the dataset used in this paper. The rest of the paper is organized into various sections, beginning with a summary of related works. This is followed by the proposed methods which contain TA, FA, and its ensemble. Finally, we address the experimental results and analyze them using visualizations.
# Efficient SMC Protocol Based on Multi-Bit Fully Homomorphic Encryption
## 2 Preliminaries  
### Symbolic Representation  
In this paper, \(\mathbb{Z}\), \(\mathbb{R}\), and \(\mathbb{Z}_{q}\) respectively represent the integer set, real number set, and integer modulo \(q\) residual ring. Bold italic lowercase letters represent vectors, and bold italic uppercase letters refer to matrices. Moreover, the length of the \(n\)-dimensional vector \(a\) is defined as its Euclidean norm \(\|\mathbf{a}\|=\sqrt{\sum\limits_{i=0}^{n-1}\mathbf{a}_{i}^{2}}\), and the length of the vector set \(S\) is defined as \(\|S\|=\max_{a\in S}\|\mathbf{a}\|\). \(\mathbf{a}\gets D\) means randomly selecting variable \(\mathbf{a}\) from probability distribution \(D\), and \(\mathbf{a}\stackrel{{\mathcal{R}}}{{\leftarrow}}U\) means randomly and uniformly selecting variable \(\mathbf{a}\) from set \(U\). Vector \(\mathbf{a}\in\mathbb{Z}_{q}^{n}\) can be expressed as \(\mathbf{a}=(a_{0},\cdots,a_{n-1})\). The polynomial \(b\in R_{q}\) can be expressed as \(b=(b_{0},\cdots,b_{n-1})\cdot c_{i}\) represents the \(i\)-th row of the matrix \(\mathbf{C}\), \(I_{n}\) is the \(n\)-dimensional identity matrix, and \(\varphi(y)\) refers to the probability \(Pr[y\leq x|y\sim N(0,1)\,]\). In addition, for the polynomial \(b,c\in R,b\times c=bc\bmod(x^{n}+1)\) can be defined.  
In this paper, the logarithmic function \(\log\) is based on 2, except that the basis is specified. \(O\) and \(o\) represent the complexity of the calculation. At the same time, for variable \(\mathbf{\sigma}\) and any constant \(c\), if \(f(\sigma)=O(\sigma^{c})\), then \(f(\sigma)\) can be expressed as \(poly(\mathbf{\sigma})\). If there is \(f(\sigma)=o(\sigma^{-c})\), then \(f(\sigma)\) can be expressed as \(negl(\mathbf{\sigma})\), which is called a negligible function of \(\mathbf{\sigma}\).
## 5 Parameter Analysis
### Contention Probability  
Contention may arise in the network when insufficient Tx/Rx cells are scheduled, leading to overlapping transmission attempts. The probability of contention in transmission depends on the network traffic load and the number of scheduled cells within a slotframe. Assuming a timeslot duration of \(10\) ms and that each node generates one packet every \(I_{t}\) ms the traffic \(T_{r}\) per slot can be expressed as \(T_{r}=10/I_{t}\).  
The average contention probability in the proposed AAS scheme over a slot is given by  
\[P_{cont}=1-e^{\gamma_{T}} \tag{11}\]  
From (5), the total number of cells allocated for all nodes within a slotframe is  
\[B=\sum_{h=1}^{H}B_{h} \tag{12}\]  
When the total available cells in a slotframe are given as \(SF\cdot c_{min}\), of these, \(B\) cells are scheduled for data transmission and reception, over which the network traffic is distributed. The contention probability, \(P_{cont}\), can then be expressed as  
\[P_{cont}=1-e^{-\frac{Tr\cdot SF\cdot C_{min}}{B}} \tag{13}\]  
The network experiences higher contention when nodes handle heavy traffic, especially when the slotframe has a sparse distribution of cells. In the proposed technique, by adapting the optimal number of cells, the contention probability is reduced by a factor of B. This results in improved PDR and enhanced transmission reliability.
## 3 Results and Discussion  
### Physicochemical Properties  
#### 3.1.1 Texture Profile Analysis (TPA)  
The results of the determination of the texture properties of the hairtail surimi gel by Monascus fermentation at different times are shown in Figure 1. The fermentation time had significant effects on the texture of surimi gel (\(p<0.05\)). The hardness, gumminess, and chewiness of surimi increased after fermentation for 1 h and then gradually decreased. Springiness did not change within 3 h of fermentation but significantly decreased after 3 h. The cohesiveness value gradually decreased, while the adhesiveness value significantly increased with the increase in fermentation time. Therefore, the 1 h fermentation group improved the texture properties of the hairtail surimi gel.  
The hardness and elasticity indexes reflect the sensory palatability of surimi. The elasticity of surimi is generally related to the content of salt-soluble protein; in particular, it is directly related to the content of myosin. Myosin generally consists of myofibrillar proteins in the form of a thick filament structure of myofibrils. Figure 1 showed that the elasticity of hairtail surimi gel did not change within 3 h of fermentation time but decreased after 3 h. The gelation process of surimi myofibrillar protein is affected by many factors, such as metal ions, pH, enzymes, etc. [40]. Through the controlled experiments, it could be revealed that Monascus fermentation produced protease that was mainly the hydrolyzed myofibrillar protein, which unfolded the structure of myofibrillar protein to a degree and exposed the reaction groups in the molecule. It could promote the aggregation of protein-protein and the formation of a gel network, and it showed improvements in the hardness, adhesion, chewiness, and recovery of surimi gel, but has little impact on the myosin of the surimi myofibrils. However, with the extension of fermentation time, the proteases produced by fermentation further hydrolyze myofibrillar proteins after 3 h of fermentation, and myofibrillar proteins are composed of 55% to 60% myosin. This suggests that myosin may be decomposed by the influence of the enzyme produced by fermentation, which reduces the formation of the gel network structure and shows the lower quality of the surimi gel.  
Adhesiveness is a term that refers to the force required to separate the tongue and teeth from the food surface when they are affixed (usually negative). Figure 1 demonstrates that Monascus fermentation significantly enhanced the adhesion of hairtail surimi gel, which considerably reduced the power of adhesion or the sticky taste of surimi when eating, improving the palatability of hairtail surimi. Myosin hydrolysis in surimi resulted in the structure of the gel network deteriorating as fermentation time increased, probably due to the continuous fermentation of Monascus. The hydrolysis-produced small molecular proteins clumped together through chemical pressures, which decreased the [PERSON] of the hairtail surimi gel.
# A Survey on Open Radio Access Networks: Challenges, Research Directions, and Open Source Approaches
## 2 Evolution from Traditional RAN to vRAN
### Traditional RAN or D-RAN  
The first version of RAN was equipped with an integrated system between RU and BBU. BBU was usually installed in a room right below BS. RU could be installed in the room or at the top of a tower, enabling the RU to support connectivity in a large area [1]. In this context, the RU can also be called the remote RU (RRU). Either way, the distance between RU/RRU and BBU is short. This traditional RAN framework was called D-RAN [8]. Implementing D-RAN is straightforward as it does not require a high-speed interface between RU and BBU. Each RAN operates independently. The network densifies as the number of UEs increases and more BS are built.  
Mobile Network Operators (MNOs) initiated a search for a solution to reduce Operating Expenditure (OpEx) due to substantial costs for renting the space for BS and the required cooling systems to operate the network, ultimately leading to the proposal of the C-RAN framework.
# A Survey of Candidate Waveforms for beyond 5G Systems
## 4 Performance Results and Discussion
### PAPR  
The PAPR of a time domain signal, \(x[n]\), is expressed by  
\[PAPR(x[n])=10\log_{10}\left(\frac{max[x[n]x^{*}[n]]}{E[x[n]x^{*}[n]]}\right) \quad[\text{dB}] \tag{17}\]  
where \(x^{*}[n]\) corresponds to the conjugate of \(x[n]\) and the \(E[.]\) operator represents the mathematical expectation or mean value.  
As mentioned, the PAPR is evaluated by its CCDF, which can be expressed by  
\[CCDF(PAPR(x[n]))=Prob\{PAPR(x[n])>\delta\} \tag{18}\]  
where \(PAPR(x[n])\) is the PAPR of the symbol and \(\delta\) is a PAPR threshold.  
The transmitted signal's PAPR's CCDF for the different waveforms are presented in Figure 5, wherein \(\delta\) is depicted for \(8\leq\delta\leq 15\) dB. For the TIBWB-OFDM with and without WTO and GFDM cases, the SRRC window is employed with a roll-off of \(\beta=0.5\). As we can observe all the waveforms presented in this figure present relatively high PAPR values, since they all are MC modulation techniques. We can clearly observe that the waveform that provides the worst PAPR behaviour is the TIBWB-OFDM. As mentioned, the high PAPR values are result of the windowing operation performed in the transmitter, which is responsible for decreasing the average power of the transmitted signal, increasing the PAPR as in (17). Additionally, the PAPR of the TIBWB-OFDM waveform tends to increase as the roll-off increases [50]. On the contrary, for the WTO case, the time overlapping operation allows the reduction of the PAPR by creating a waveform without the low amplitude of the symbol's tails, which in turn, opposes the decrease in the average signal power and, consequently, reducing the PAPR. In this operation, only the tails of the windowed OFDM symbols are overlapped, thus, the increase of the peak power is negligible. The F-OFDM candidate presents similar performance as OFDM since the only difference is regarding the filtering stage and the simulation was performed assuming large OFDM and F-OFDM signals with a total length equal to a TIBWB-OFDM multi-symbol. Therefore, although the Hanning window presents smooth transitions, it does not affect the PAPR considerably in this analysis. Hence, the high PAPR values still occur since the transmission scheme is based on the IFFT operation, wherein the instantaneous amplitude of each sub-carrier that form the OFDM sub-symbol are added. GFDM falls into the same category, however, in this case, the SRRC window employed with \(\beta=0.5\) makes the PAPR to increase slightly when compared to OFDM.  
In brief, from the point of view of this KPI, the only waveform that is not recommended is the TIBWB-OFDM.
# Testing Accuracy and Repeatability of UAV Blocks Oriented with GNSS-Supported Aerial Triangulation
## Results and Discussion
### eBee RTK Survey Accuracy Assessment
MicMac is almost always the most accurate in XY in every configuration; PhotoScan is the best in elevation in the **12 GCP** and **RTK** cases, while in the **RTK+1 GCP** case there is no clear winner. The accuracy obtained on checkpoints is finally summarized in Table 4, where the best and worst RMSE in each configuration are reported for all packages and all flights, expressed both in metres and in GSD units. As stated before, the statistics in Figure 3 and Tables 11 and 12 refer to the 14 checkpoints available. However, the accuracy of **RTK** and **RTK+1 GCP** configurations has also been calculated on all available CP, i.e., 26 and 25, respectively. The changes to all the RMSE shown in Figure 3 are in both cases negligible, since they amount to just a few mm at most. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**MEAN Z (m)**} \\ \hline  & \multicolumn{3}{c}{**RTK 1**} & \multicolumn{3}{c}{**RTK 2**} \\ \hline  & **12 GCP** & **RTK** & **RTK+1 GCP** & & **12 GCP** & **RTK** & **RTK+1 GCP** \\ \hline
**Pix4D** & 0.005 & 0.012 & \(-\)0.002 & **Pix4D** & 0.006 & 0.083 & 0.001 \\
**PS** & \(-\)0.001 & 0.011 & 0.010 & **PS** & \(-\)0.003 & \(-\)0.093 & \(-\)0.002 \\
**MicMac** & 0.003 & \(-\)0.085 & \(-\)0.004 & **MicMac** & 0.000 & \(-\)0.063 & \(-\)0.002 \\ \hline  & \multicolumn{3}{c}{**RTK 3**} & \multicolumn{3}{c}{**RTK 4**} \\ \hline  & **12 GCP** & **RTK** & **RTK+1 GCP** & & **12 GCP** & **RTK** & **RTK+1 GCP** \\ \hline
**Pix4D** & \(-\)0.011 & 0.069 & 0.001 & **Pix4D** & \(-\)0.008 & 0.013 & \(-\)0.026 \\
**PS** & \(-\)0.001 & 0.006 & 0.024 & **PS** & 0.004 & 0.004 & \(-\)0.011 \\
**MicMac** & 0.004 & 0.035 & \(-\)0.019 & **MicMac** & 0.010 & \(-\)0.039 & 0.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Mean error in Z on the 14 checkpoints for the three BBA configurations: with GCP only (**12 GCP**), with GNSS-AT only (**RTK**) and with GNSS-AT with the addition of 1 GCP (**RTK+1 GCP**).