esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation and Results
Figure 6: BER comparison with respect to modulation coding scheme at the user density (UD) = 1.4 Tb/in\({}^{2}\). \begin{table}
\begin{tabular}{c c c} \hline \hline  & \(T_{x}\), \(T_{x}\) & \(T_{x}\), \(T_{x}\) \\ \hline PRML & 21.5 nm (1.4 Tb/in\({}^{2}\)) & 17.5 nm (2.1 Tb/in\({}^{2}\)) \\
4/6 modulation code & 17.5 nm (2.1 Tb/in\({}^{2}\)) & 14.1 nm (3.15 Tb/in\({}^{2}\)) \\
8/10 modulation code & 19.2 nm (1.75 Tb/in\({}^{2}\)) & 15.7 nm (2.62 Tb/in\({}^{2}\)) \\
7/10 modulation code & 18 nm (2.0 Tb/in\({}^{2}\)) & 14.5 nm (3.0 Tb/in\({}^{2}\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The track pitch \(T_{z}\) and bit period \(T_{x}\) for each AD. when the AD is high and interference is severe, one must consider whether a modulation code should be used. Figure 8 shows the BER performance depending on TMR from 0 to 30% when the UD was 1.4 Tb/in\({}^{2}\). In a situation with some reasonable error factors, the proposed 7/10 modulation code showed the best performance at UD = 1.4 Tb/in\({}^{2}\).
# An Enhanced Privacy-Preserving Authentication Scheme for Vehicle Sensor Networks
## 4 Review of the Chuang-Lee's Scheme
### General Authentication  
The general authentication procedure is performed between \(OBU_{i}\) and \(LE_{j}\) after \(U_{i}\) has completed the login procedure. The steps of this procedure are described below:  
1. \(OBU_{i}\) chooses a random number \(r_{i}\) and computes its alias \(AID_{i}=h(r_{i})\oplus ID_{i}\). Then, it produces the request messages \(M_{1}=h(B_{i})\oplus r_{i}\) and \(M_{2}=h(r_{i}||AID_{i}||D_{i})\).
2. \(OBU_{i}\to LE_{j}\): The authentication messages (i.e., \(AID_{i}M_{1}M_{2}\) and \(D_{i}\)) are sent from \(OBU_{i}\) to \(LE_{j}\).
3. Upon receiving the authentication request message (i.e., \(AID_{i}\),\(M_{1}\),\(M_{2}\),\(D_{i}\)), \(LE_{j}\) uses \(PSK\) to retrieve \(A_{i}=D_{i}\oplus PSK\) and \(r_{i}=M_{1}\oplus h^{2}(A_{i})\) and then checks whether \(M_{2}=h(r_{i}||AID_{i}||D_{i})\) holds. The authentication request will be rejected if this equation does not hold. Otherwise, \(LE_{j}\) computes \(ID_{i}=AID_{i}\oplus h(r_{i})\) and produces a random number \(r_{j}\) with which to calculate \(AID_{j}=r_{j}\oplus ID_{j}\) and \(SK_{ij}=h(r_{i}||r_{j})\). Finally, \(LE_{j}\) calculates the response messages \(M_{3}=r_{j}\oplus h^{2}(r_{i})\), \(M_{4}=A_{i}\oplus h(ID_{i})\) and \(M_{5}=h(M_{4}||r_{j}||AID_{j})\).
4. \(LE_{j}\to OBU_{i}\): \(LE_{j}\) returns its response messages (i.e., \(AID_{j}\),\(M_{4}\),\(M_{5}\)) to \(OBU_{i}\).
5. \(OBU_{i}\) computes \(h^{2}(r_{i})\) to retrieve \(r_{j}=M_{3}\oplus h^{2}(r_{i})\) and checks whether \(M_{5}=h(M_{4}||r_{j}||AID_{j})\) holds. \(OBU_{i}\) terminates the process if this equation does not hold. Otherwise, \(OBU_{i}\) computes \(A_{i}=M_{4}\oplus h(ID_{i})\), calculates \(SK_{ij}=h(r_{i}||r_{j})\), and stores \(A_{i}\) in its secure hardware.
6. \(OBU_{i}\to LE_{j}\): The message \(SK_{ij}\oplus h(r_{j})\) is sent to \(LE_{j}\) by \(OBU_{i}\).
7. \(LE_{j}\) uses \(SK_{ij}\) to retrieve \(h(r_{j})\). Then, it checks whether the retrieved hash value is equal to the pre-computed hash value using the chosen \(r_{j}\). In this way, a replay attack from an illegal OBU is avoided.  
As this time, the state of \(OBU_{i}\) changes to trusted since \(OBU_{i}\) has been authenticated successfully and has obtained the parameter \(PSK=A_{i}\oplus D_{i}\). Now, not only LE but also \(OBU_{i}\) can authenticate other mistrusted OBUs.
## 3 Chance-Constraint Two-Stage Stochastic Unit Commitment
### Scenario-Based Day-Ahead Unit Commitment  
Oriented to the high renewables penetrated system, the proposed model of two-stage chance-constraint stochastic unit commitment could be used to optimize the day-ahead power generation scheme. With the consideration of the intermittency of renewable energy, power generation scheduling and spinning reserve scheduling are optimized simultaneously in the stochastic UC model. Furthermore, based on chance-constrained optimization, a reserve planning method is proposed to balance system reliability and economy, concerning thermal unit reserves and CSP reserves.  
Day-ahead UC programming is separated into two parts. Based on the point forecast of renewables, the first stage of UC model optimizes the expected day-ahead generation, start-up and reserve decisions of each plant. The second stage of UC model is formulated to guarantee that the decisions made in the first stage could be deployed to handle the deviation between the expected point forecast of renewables and the probabilistic forecast of renewables in each pre-decided scenario.  
In this paper, the proposed reserve decision planning is different from the traditional planning model. The traditional reserve decision making usually adopts spinning reserve according to the historical experience, and lacks reliability and economy [38; 39]. In order to address the above issue, the chance-constraint reserve planning model is proposed to ensure that under various stochastic scenarios, the scheduled reserves could satisfy the system reliability requirements (\(EENS\), \(EWVS\)) within the specified probability. Meanwhile, the reliability requirements are introduced in the objective function concerning economic dispatch. Therefore, the proposed reserve planning guarantees the reliable and economic requirements simultaneously.
# A Military Object Detection Model of UAV Reconnaissance Image and Feature Visualization
## 3 UAVT-YOLOv5 Model
### Anchor Box Setting  
Figure 9 shows the distribution of the object bounding boxes of the UAVT-3 dataset, where the center location, width, and height are normalized with the image size. From Figure 9a, it can be seen that the center location points are nearly spread all over the figure, which means that the tanks appear in different locations. From Figure 9b, we can see that most points are located in the left corner, which means that the objects of the UAVT-3 dataset are small objects.  
The anchor boxes were obtained by using K-means to cluster ground truth boxes of the COCO dataset [15], which is not the optimal choice for UAVT-3 since there are some differences between COCO and UAVT-3. Therefore, it is necessary to re-cluster to obtain anchor boxes suitable for the UAVT-3 dataset. The Euclidean distance is one of the most popular similarity metrics in K-means clustering. However, it is very sensitive to the size of the target box. The following distance function was designed using Intersection over Union (IoU)  
\[d(B_{i},C)=1-IoU(B_{i},C), \tag{5}\]  
where \(B_{i}\) denotes the \(i_{th}\) box, and \(C\) denotes its corresponding center point in the clustering. Figure 10a shows the clustering results, where the \(x\)-axis represents the number of clustering center points, and the \(y\)-axis represents the average IoU. It can be seen that when \(K\) is over 12, the average IoU rises slowly. Therefore, 12 can be seen as the inflection point, which  
Figure 9: Distribution of ground truth bounding boxes of UAVT-3, in which each color denotes one class tank. (**a**) Distribution of object center points. (**b**) Distribution of object size.  
was selected as the number of anchor boxes shown in Figure 10b. As shown in Table 1, three boxes were selected for each feature map.
## 3 Results  
### Experimental  
Time-average experimental results are summarized in a 1260-row matrix, used as input for the ANN development. Hence, from each test, on the input section, the test matrix includes: current load, temperature, H\({}_{2}\), CO\({}_{2}\), CH\({}_{4}\), CO and N\({}_{2}\) volume flowrates. As output section, the matrix shows voltage outputs related to each combination of the previous parameters. Hereinafter, Figure 2 presents a couple of examples regarding SOFC performance maps obtained experimentally. In detail, performances maps are represented for two load conditions, 100 mA\(\cdot\)cm\({}^{-2}\) and 250 mA\(\cdot\)cm\({}^{-2}\). On the y-axis, measured voltage is shown, while on the primary and secondary x-axis the fuel total indicator (Equation (5)) and the temperature are shown. As explained above, the fuel total indicator is a useful dimensionless parameter to sum up the information relative to the kind of fuel mixture and the way it is employed (that is to say the current load and the anode feeding flow rate).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Temperature**} & \multicolumn{6}{c}{**Gas Mixture**} & \multirow{2}{*}{\(\dot{u}_{H_{2\;eq}}\)} & \multirow{2}{*}{\(\dot{j}\)} \\  & & & & & & & & \\  & & & & & & & & \\ \hline \({}^{\circ}\)**C** & **MIX** & **Ref.** & **H\({}_{2}\)** & **CO** & **CH\({}_{4}\)** & **CO\({}_{2}\)** & **N\({}_{2}\)** & **mL\(\cdot\)min\({}^{-1}\)\(\cdot\)cm\({}^{-2}\)** & **mA\(\cdot\)cm\({}^{-2}\)** \\ \hline  & synA & [37] & 10\% & 13\% & 4\% & 19\% & 54\% & & \\
700 & synB & [38] & 48\% & 0\% & 14\% & 9\% & 29\% & 10.8 & 0 A \(\rightarrow\) I (0.6 V) \\
750 & synC & [39] & 22\% & 26\% & 30\% & 22\% & 0\% & 14.7 & (\(\Delta j\) = 50 mA\(\cdot\)cm\({}^{-2}\)) \\
800 & synD & [40] & 32\% & 38\% & 0\% & 30\% & 0\% & 23.6 & \\  & synE & [41] & 0\% & 0\% & 45\% & 32\% & 45\% & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Design of experiments set-points for process decision variables.
# Immobilization of Levocyetririzine on Mesoporous Silica for Antiallergenic Gel Formulation
## 3 Results and Discussions
### In Vivo Effectiveness of Gel Containing Levb Immobilized on Silica  
Croton oil-induced ear inflammation in the ears of mice was applied to study the anti-inflammatory effect of gel (_gSyLev_) containing Levb immobilized on a Syloid carrier (sample _SyLev_) compared to the control and reference Fenistil gel. Using croton oil on the ears of the mice leads to edema, which can be successfully inhibited by Levo-containing preparations. The effects of the gels and doses used are illustrated in Figure 12. The introduction of croton oil induced the swelling of ears for control mice, which was inhibited significantly (_p_ < 0.05) by 20 \(\mu\)L of the reference Fenistil gel. The gel containing sample _SyLev_ showed a dose-dependent anti-inflammatory effect; it reduced the thickness of the ear proportionally to the doses of 5, 10 and 20 \(\mu\)L. A substantial difference compared to the control was observed with the dose of 20 \(\mu\)L. Half a dose of the Levo-containing gel (10 \(\mu\)L) showed roughly the same effect as Fenistil gel (20 \(\mu\)L). Furthermore, in the application of the same dose, the effect of the Levo-containing gel was approximately twice that of the reference Fenistil gel since the ear edema, and thus, the ear thickness, was reduced to a much greater extent compared to the Fenistil-treated animals.  
[PERSON] and her coworkers produced spray-dried Levo dihydro with HPMC polymer and prepared a gel from it. In their in vivo experiment using the same croton oil-induced ear inflammation mouse model, they reached similar results to ours [43]. However, their gel contained a penetration-enhancing substance (10%) and a higher concentration (10%) of the active ingredient than our gel with 5% active ingredient. Therefore, considering the concentration differences, the gel preparation with a lower concentration produced  
Figure 12: In vivo effectiveness of gel containing levocetirazine free base immobilized on Syloid (gSyLev) on ear inflammation in the ears of mice. Values are mean \(\pm\) standard error of the mean. (n = 8), ns stands for not significant, \({}^{*}\)_p_\(\leq\) 0.05, \({}^{***}\)_p_\(\leq\) 0.0001.  
by us could be more favorable despite the similar effect, as it is expected that smaller side effects will occur.
## 4 Simulation Results  
In this section, we first evaluate the asymptotical orthogonality of the channels between the BS and its served users, which reflects the computational efficiency of MRDS. Figure 1 shows the orthogonal probability (\(\delta=0.2\)) versus the number of BS antennas with \(M=10\), \(15\), and \(20\). The orthogonal probability gets larger with the increase of the number of BS antennas or with the decrease of the user amounts.  
In Figures 2 and 3, the performance of the three proposed algorithms are compared to show the total power consumption and the computation time by 500 independent trials. In our experiments, we use the log-distance path loss model, in which the path loss at distance \(d\) is set as \(\beta(d)[\text{dB}]=\beta_{fr}(d_{0})+10n\log(\frac{d}{d_{0}})\)[14], where \(n\) is the path loss exponent and \(\beta_{fr}(d_{0})\) is the free-space path loss at the reference distance \(d_{0}\) and the carrier frequency of \(f_{c}\). The simulation parameters are detailed in Table 1. Without loss of generality, All the users are assumed to be located 1200 m away from the BS, and all \(\widetilde{\gamma}_{i}\)s are the same and vary from 0 dB to 30 dB.  
Figure 1: The orthogonal probability (\(\delta=0.2\)) versus the number of base station (BS) antennas.  
Figure 2: The average minimum total consumed power calculated by our methods versus the signal-to-noise ratio (SNR) constrains.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(N\) & 200 & \(\eta\) & 1 \\ \(n\) & 3.8 & \(\theta\) & 0.8 W \\ \(d_{0}\) & 100 m & \(\sigma_{l}^{2}\) & \(-96\) dBm \\ \(P_{\text{cir}}\) & 80 mW & \(f_{c}\) & 2.5 GHz [10] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation Parameters.  
Figure 2 shows the average minimal total power consumption calculated by our methods versus the SNR constrains when \(M=10\). The average minimal power consumption gets larger when the SNR requirement becomes more strictly. The difference of the average minimum total consumed power is minor between the RDS algorithm and the MRDS algorithm. The L0 NM algorithm significantly outperforms the RDS algorithm, because it does not remove antennas randomly in each step as done by the RDS algorithm.  
Figure 3 evaluates the average run time of our algorithms versus the SNR constrains when \(M=10\). The codes are executed on a 64-bit desktop with 8 Gbyte RAM and Intel CORE i5 using YALMIP as the Matlab package. It can be seen that, either the MRDS algorithm or the L0 NM algorithm provides noticeable improvement compared with the RDS algorithm in terms of the average run time.
# COVID-19: A Comparison of Time Series Methods to Forecast Percentage of Active Cases per Population
## 3 Description of Time Series Models
### Prophet: Automatic Forecasting Procedure  
Prophet is a time series forecasting model developed by Facebook and was originally designed to handle business time series problems. Although there is a wide diversity of methods for predicting business-related outcomes, many business problems share the same pool of common features such as seasonal effects [17].  
The method uses an easily decomposable time-series model [52] consisting of three main components: trend, seasonality and holidays. In its core, it is regression model with interpretable parameters that often work well with their default values, while also allowing the user to intuitively choose the components that concern their forecasting problem and effortlessly apply the necessary adjustments [17].  
To forecast trend, prophet employs two models: a saturating growth model and a piece-wise linear model. For growth predictions, a model similar to population growth models in natural ecosystems [53] is used, where there is nonlinear growth that reaches a saturation point at a carrying capacity. For forecasting problems where this saturating point is never reached, a piece-wise model of constant growth-rate provides an efficient and often useful solution. To capture seasonality, prophet relies on Fourier series to provide a flexible model of periodic effects [54], while, to account for holidays, a predefined list of past and future holiday events is required. Holiday effects are assumed to be independent and therefore incorporating them to the model becomes a trivial task [17].
# Visual Navigation Systems for Maritime Smart Ships: A Survey
## 2 Smart Ships and Navigation Technology
### Navigation Technology  
Navigation technology is the top priority of the whole smart ship system, and appropriate navigation technology can improve the safety of navigation, reduce operational costs and improve the efficiency of navigation at the same time. Determining collision avoidance strategies and paths by sensing and learning sea information in intelligent ships during navigation is a problem that needs to be solved through further research. Effective collision avoidance and safe navigation of smart ships on the sea rely heavily on efficient and useful smart navigation systems. Therefore, the research on smart ship navigation technology in Section 2.2 is mainly categorized into perception technology, communication technology, motion control technology and collision avoidance technology. Their relationships can be represented in Figure 3:
# Visual Analysis of Spatiotemporal Data Predictions with Deep Learning Models
## 7 Conclusions  
In this paper, we proposed a visualization system that can analyze deep learning models. We proposed an approach to select the appropriate features and deep learning model by analyzing correlations, spatial correlations, and temporal correlations for spatiotemporal data prediction. We analyzed deep learning based prediction model with an air pollutant data set, which represents an irregularly distributed spatiotemporal data set. Our system allows us to explain the reason for the low performance of a deep learning model in the aspect of spatial and temporal correlations. We believe that our approach supports us in understanding the parameter settings and improving deep learning models for spatiotemporal data. It is possible to extend our system to include more deep learning models and explain the predicted results, which is crucial in deep learning research. However, our model has some limitations, including the lack of feature extraction and the hyper parameter setting of deep learning networks. To overcome this problem, we plan to add spatial filtering, apply feature extraction techniques, including PCA, LDA, and t-SNE, and apply a DCRNN architecture by transforming the extracted feature into a directional graph form. We also plan to apply the RBF network to the ConvLSTM neural network in the future.  
**Author Contributions:** All authors contributed to this study. H.S., S.K., H.Y., and Y.K. developed the system and wrote the article. S.-E.K. and Y.J. supervised the project and wrote the article. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the Basic Research Program through the National Research Foundation of Korea (NRF) funded by the MSIT (2019R1A4A1021702) and in part by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2019-0-00374, Development of Big data and AI based Energy New Industry type Distributed resource Brokerage System).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Adaptive DecayRank: Real-Time Anomaly Detection in Dynamic Graphs with Bayesian PageRank Updates
## 1 Introduction
### Hypothesis and Research Questions  
Given a large, time-evolving graph stream, \(\mathcal{G}=\{G_{t}\}_{t=1}^{T}\), where structural changes occur dynamically across timestamps, \(t\), can we develop an **adaptive, near-real-time, and scalable** anomaly detection framework that leverages an _adaptive decay factor with Bayesian updating_ to effectively detect anomalous patterns with high precision and robustness.  
We aim to address the following questions in our work:  
* **Q1:** Can we detect sudden changes in dynamic graphs, including node insertions, edge formations, and structural deviations, with high precision while minimizing false positives?
* **Q2:** Can we ensure **scalability** in real-time anomaly detection for high-velocity graph streams while maintaining **low computational overhead**.
# Intersection-Based Routing with Fuzzy Multi-Factor Decision for VANETs
## 6 Conclusions  
In this paper, the IRFMF proposed is an intersection-based routing protocol. The routing decision is determined at intersections by the RSU using the Triangular Fuzzy Number (TFN) combined three factors including the number of lanes, direction, and the traffic flow. The IRFMF selects the next-hop road segment based on the fuzzy score of the candidate road segment, which is calculated by comparing the real-time information of the three factors. According to the fuzzy weighting method, the packet is forwarded to the candidate road segment with the highest fuzzy score at intersections. At the same time, in order to increase the lifetime of the link between two adjacent intersections, we propose a speed-basedlimited greedy forwarding based on the relationship between time headway and vehicle speed. It can be said that the IRFMF makes full and effective use of the characteristics of the VANETs. The simulation results show that the performance of the IRFMF is better than other compared routing protocols in terms of data delivery ratio and end-to-end delay in the same simulation environment, although more hops are traded for more robust and stable links.  
Conceptualization, Z.C.; methodology, Z.C.; software, Z.C.; validation, J.K.; formal analysis, Z.C. and Z.F.; investigation, Z.C. and Z.F.; resources, J.K.; data curation, Z.C. and Z.F.; writing--original draft preparation, Z.C; writing--review and editing, Z.C. and Z.F.; visualization, Z.C.; supervision, J.K.; project administration, J.K.; funding acquisition, J.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019R1F1A1042721). This study was also supported by the BK21 Plus project (SW Human Resource Development Program for Supporting Smart Life) funded by the Ministry of Education, School of Computer Science and Engineering, Kyungpook National University, Korea (21A20131600005).  
The authors declare no conflict of interest.
# An Intrusion Resistant SCADA Framework Based on Quantum and Post-Quantum Scheme
## 4 Proposed Scheme  
To address the research questions in Set 1, we developed an algorithm that includes the following three phases: key generation, encryption and authentication. We have used the B92 protocol for key generation and encryption, and SPHINCS-256 for integrity and authentication. Moreover, to address the research questions in Set 2, we have modified the SPHINCS-256 algorithm. The SPHINCS-256 algorithm has four main components, namely, PRNG based on Chacha-12 algorithm for HORST secret-key generation and expansion, hashing in WOTS and HORS public-key generation, hashing in trees, Chacha Permutation used in trees. Furthermore, the sender transmits a combination of signature, message, and SPHINCS-256 public key to the receiver. In our proposed algorithm, we use the quantum random number generator (QRNG), instead of pseudo random number generator (PRNG), to generate a truly random HORST secret key. Instead of the message, the sender sends the cipher along with signature and public key to the receiver. Figure 1 provides a brief overview of the steps in our proposed scheme.  
Figure 1: The Proposed Scheme ModelIn the following, we present an overview of our proposed scheme.
* Key Generation: The quantum key is generated from the B92 protocol.
* Encryption: The quantum key is used to encrypt the message to obtain the cipher.
* Authentication: The SPHINCS-256 protocol is applied to generate a signature and public key for verification and validation.