esa-sceva/satcom-chunk-collection huggingface dataset:
# An Overview of CubeSat Missions and Applications
## 8 The Use of CubeSats in Novel Technology Demonstration Missions
### Quantum Technology Demonstration  
The next demonstration mission type that is going to be presented is quantum technology demonstration without Earth's atmosphere interference. Studying quantum effects, such as entanglement and super position, not only can deepen our knowledge regarding these fundamental physics phenomena but also provide useful solutions in quantum communication and quantum sensing. A pioneering CubeSat mission in this field is the SpooQy-1 mission, which refers to a 3U CubeSat developed by Quantum Technologies (CQT) at the National University of Singapore, in collaboration with SpeQtral (Connexis, Singapore) and other academic institutions [318]. SpooQy-1 was launched in August 2019, with the primary objective of demonstrating generation and polarization measurements of entangled photon pairs in orbit. The primary payload was the Small Photon-Entangling Quantum System-2 (SPEQS-2), which created polarization-entangled photon pairs. To test the quality of entanglement, the Bell's inequality test was utilized. While in the best result, a Clauser-HorneShimony-Holt (CHSH) parameter value of 2.6 (plus minus 0.06) was achieved, confirming the quantum entanglement generation in space. The success of this mission paved the road for two upcoming and related demonstration missions, the SpeQtre, which will demonstrate Quantum Key Distribution (QKD) from space, using BBM92 protocol and SpeQtral-1, which aims to validate global QKD from space by testing and demonstrating QKD using both BBM92 and BB84 protocols [319]. One other upcoming mission is Responsive Operations and Key Services (ROKS) [320]. This 6U CubeSat is developed by Craft Prospect Ltd (Glasgow, UK) (CPL) in collaboration with University of Bristol and University of Strathclyde. Expected to be launched in 2025, its primary mission is to demonstrate QKD in night and cloud detection capabilities in an attempt to optimize secure quantum communication between space and Earth. Planned to operate in a Sun-synchronous 500 km orbit, it will utilize the Joint Alignment, Diode Emitter (JADE) module, necessary for the BB84 protocol, and an optical module named the GNEISS for Generating New and Extended Independent Signal States, with the goal of transmitting quantum signals through an optical beam steering system. Achieving the necessary pointing accuracy was one of the biggest challenges, however ROKS can overcome this by utilizing a mirror system to align the emitted signals to an uplink beacon from the ground station. If ROKS successfully demonstrates its objectives, it will set a milestone for CubeSat based quantum communication with enhanced cybersecurity with reduced reliance to fiber based QKD networks.
## 9 Conclusions  
The state of charge is a critical factor in ensuring any AEV reaches its destination. Hence, in addition to the path-planning problem, the AEV must also consider the charging problem. This paper addresses dynamic path optimization with a charging problem to minimize travel and charging costs based on real-time information. In the charging problem, we considered both CSs and ESs as charging sources. First, we proposed a joint push-pull communication mode to obtain real-time traffic conditions and charging infrastructure service status information. Based on this information, we formulated a dynamic optimization problem to minimize travel and charging costs. However, solving this problem directly is difficult, so we converted the original dynamic optimization problem into individual optimization problems. Moreover, we proposed the DRT-A* algorithm to solve the dynamic path-optimization problem and the DRT-CS algorithm to solve the charging problem based on dynamic path optimization. The comparative experimental results show that the proposed dynamic optimization algorithm outperforms the CA* algorithm when the travel time and queuing time are used as performance metrics. Moreover, our proposed algorithm also has better path-optimization performance in the general scenario, compared to the EVRPTW-TOU method. As our algorithm takes into account dynamic transportation networks, it alleviates both traffic and charging congestion and improves vehicles' efficient operation. In future work, we will focus on addressing communication challenges related to obtaining real-time network information, specifically studying the charging communication protocol in detail to provide safe and efficient communication for vehicle charging. Overall, the proposed approach can pave the way for more efficient and cost-effective routing and charging strategies for autonomous electric vehicles.  
Conceptualization, Q.T. and D.L.; methodology, Q.T. and D.L.; software, Q.T.; validation, Q.T.; formal analysis, Q.T., D.L., Y.Z. and X.C.; investigation, Q.T.; resources, Q.T.; data curation, Q.T.; writing--original draft preparation, Q.T.; writing--review and editing, Q.T., D.L., Y.Z. and X.C.; visualization, Q.T.; supervision, Q.T., D.L. and Y.Z. All authors have read and agreed to the published version of the manuscript.  
This work is supported by NSF of China under Grant No.61772130, No. 71171045, and No. 61901104; the Innovation Program of Shanghai Municipal Education Commission, China under Grant No.14 YZ130; the International S&T Cooperation Program of Shanghai Science and Technology Commission, China under Grant No. 15220710600.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Survey Results and Analysis
### Preliminary Data Analysis
#### 4.1.4 Relative Importance Index Analysis  
This study agrees with the view that using the mean in addition to the standard deviation of each single factor's attribute is not a suitable way to evaluate the overall rankings as they do not reflect any relationship between them.  
The suggested method for sorting the indicators is using the Relative Importance Index (RII) [56; 57].  
As a result, this study used the Relative Importance Index (RII) to define the comparative ranking of the delay indicators, which can be calculated with the formula of Equation (1):  
\[\text{Relative Importance Index (RII)}=\frac{\sum W}{A+n}=\frac{1n_{1}+2n_{2}+3n_{3}+4 n_{4}+5n_{5}}{5*n};(0\leq RII\leq 1) \tag{1}\]  
where  
W = the weight that was given to each delay factor by the respondent within the range {1, 2, 3, 4, 5}, multiplied by the number of respondents {n\({}_{1}\), n\({}_{2}\), n\({}_{3}\), n\({}_{4}\), n\({}_{5}\)} for each factor; n\({}_{1}\) = number of respondents who strongly disagree; n\({}_{2}\) = number of respondents who disagree; n\({}_{3}\) = number of respondents who neither agree nor disagree; n\({}_{4}\) = number of respondents who agree; n\({}_{5}\) = number of respondents who strongly agree; A = highest weight (in this study: 5); n = overall number of respondents (in this study: 200).  
The ranking of the assessment of the 14 items (I.01 \(\div\) I.14) using the Relative Importance Index (RII) is presented in Table 4.  
According to the specialized studies [51; 54; 55], the values of the Relative Importance Index (RII) can be classified into 5 levels of importance: high (0.8 \(\leq\) RII \(\leq\) 1), high-medium (0.6 \(\leq\) RII \(<\) 0.8), medium (0.4 \(\leq\) RII \(<\) 0.6), medium-low (0.2 \(\leq\) RII \(<\) 0.4) and low (0 \(\leq\) RII \(<\) 0.2). Analyzing the results presented in Table 4, it is noted that the 14 items fall into the following levels of importance: high importance (I.01 \(\div\) I.06, I.09 \(\div\) I.14), high-medium importance (I.08) and medium importance (I.07), their classification being made according to the value of the calculated ratios, thus obtaining values for the IIR between 0.931 (I.01--first ranked) and 0.519 (I.07--last ranked).
# A Novel Mobile Communications Authentication Scheme with Roaming Service and User Anonymity
## 1 Introduction
However, [PERSON]'s scheme has two weaknesses: (1) \(\mathrm{ID_{HA}}\) is transmitted using plaintext in the authentication phase and therefore an attacker can easily perform distributed denial-of-service attacks if \(\mathrm{ID_{HA}}\) is intercepted; and (2) there exist some issues in how the session key is generated in the roaming phase. Specifically, the details of session key management are not shown and are left unaddressed in [PERSON]'s scheme [27]. This study proposes an elliptic curve-based authentication scheme with roaming service and user anonymity for mobile communications that overcomes the weaknesses of [PERSON]'s scheme [27] and ensures fair load-sharing of the session key computation in the authentication phase. The remainder of this paper is organized as follows: In Section 2, we review [PERSON]'s scheme and analyze its weaknesses. In Section 3, we propose an elliptic-curve-based authentication scheme with roaming service and user anonymity for mobile communications. In Section 4, we use BAN-logic (Burrows-Abadi-Needham logic) to demonstrate the security of our proposed scheme. In Section 5, we analyze our proposed scheme, and in Section 6, we compare it with other schemes. Finally, Section 7 presents the conclusions of our study.
# Gaussian Mixture Modeling for Detecting Integrity Attacks in Smart Grids
## 4 Experimental Set-Up and Results
### Cyber-Attacks  
The problem description considered in this paper assumes that the attacker has full access to the infrastructure, i.e., he may hijack node data according to his best interest, and thus perform a full-extent man-in-the-middle attack. In addition, the attacker may have the network under surveillance over an extended time interval so as to understand its dynamic behaviour and monitor any of the provided functionalities including component states, connectivity statuses, other type of measurements, etc.  
During an attack, the control inputs are altered by the party implementing it, thus the information gathered by the controller is incorrect and the system becomes an open-loop, i.e., operator side control is lost. In such cases, the problem can only be tackled by prompt detection of the attack.  
Based on the above described logic and the kinds of attacks considered in the literature (see Section 1) in this paper, we encompass a generic set of integrity attacks representing a wide range of scenarios. More specifically:  
1. _Pulse_: In this case, the datastream is altered according to an additive pulse : \(X_{i}^{*}(t)=X_{i}(t)+rect(t)\), where \(X_{i}^{*}(t)\) is the compromised data, \(X_{i}(t)\) the data coming from the nominal network state as recorded by the attacker, and \[rect(t)=\left\{\begin{array}{ll}0,&\quad\text{if }|t|>\frac{1}{2}\\ a_{p}/2,&\quad\text{if }|t|=\frac{1}{2}\\ a_{p},&\quad\text{if }|t|<\frac{1}{2}\end{array}\right.\] where \(a_{p}\) is the attack parameter.
2. _Scaling_: Here the recorded measurements are scaled on the basis of the parameter \(a_{s}\): \(X_{i}^{*}(t)=a_{s}\times X_{i}(t)\).
3. _Ramp_: During this attack type the recorded measurements are gradually modified by adding a ramp function with parameter \(a_{r}\): \(X_{i}^{*}(t)=X_{i}(t)+ramp(t)\), where \(ramp(t)=a_{r}\times t\).
4. _Random_: This type of attack suggests summing the recorded datastream with a uniform random distribution from the interval \((a,b)\): \(X_{i}^{*}(t)+rand(a,b)\).
5. _Replay_: The final type of attack which is usually found in the literature with the descriptive name _replay_ merely involves the identical repetition of a priori recorded data.  
An important note is that the attacker may compromise the network using the same or a different attack type in time, e.g., he may initialize with a _Replay_ attack and continue with a _Random_ one.
# Optimized Travel Itineraries: Combining Mandatory Visits and Personalized Activities
## 3 Methodology
### Adaptive Neighborhood Simulated Annealing Algorithm (ANSA)
#### 3.3.2 ANSA Procedure
```
1:Input:\(I_{iter}\), \(T_{0}\), \(T_{f}\), \(\alpha\), \(K\), InitialSolution;
2:Output:\(Objective=F_{best}\);
3:\(I=0\); \(T=T_{0}\); \(F_{best}=\) obj (X); \(X\) = InitialSolution; \(X_{best}=X\); \(E=5\);
4:\(R_{t}=1/E\) for each \(t\) in \(\{1,2,3,\ldots,5\}\),
5:While\(T>T_{f}\);
6:\(I=1\); \(N_{t}=0\) for all \(t\); \(O_{t}=0\) for all \(t\); \(F_{worst}=0\);
7:While\(I\leq I_{iter}\);
8:\(r\leftarrow\) random (0, 1);
9:If (\(r\leq R_{1}\)) then
10: Generate a new solution \(Y\) from \(X\) by mechanism 1, \(t=1\);
11:\(N_{1}=N_{1}+1\); \(O_{1}=O_{1}+\) obj (\(Y\));
12:Elseif(\(R_{1}<r\leq R_{1}+R_{2}\)) then
13: Generate a new solution \(Y\) from \(X\) by mechanism 2, \(t=2\);
14:\(N_{2}=N_{2}+1\); \(O_{2}=O_{2}+\) obj (\(Y\));
15:Elseif(\(R_{2}<r\leq R_{1}+R_{2}+R_{3}\)) then
16: Generate a new solution \(Y\) from \(X\) by mechanism 3, \(t=3\);
17:\(N_{3}=N_{3}+1\); \(O_{3}=O_{3}+\) obj (\(Y\));
18:Elseif(\(R_{3}<r\leq R_{1}+R_{2}+R_{3}+R_{4}\)) then
19: Generate a new solution \(Y\) from \(X\) by mechanism 4, \(t=4\);
20:\(N_{4}=N_{4}+1\); \(O_{4}=O_{3}+\) obj (\(Y\));
21:Else
22: Generate a new solution Y from \(X\) by mechanism 5, \(t=5\);
23:\(N_{5}=N_{5}+1\); \(O_{5}=O_{5}+\) obj (\(Y\));
24:Endif
25:\(\Delta=\) obj (\(Y\)) - obj (\(X\));
26:If(\(\Delta\leq 0\)) then\(X=Y\);
27:Else\(r=\) random (0, 1);
28:If(\(r<\exp(-\Delta/KT)\)) then\(X=Y\); End if
29:End if
30:If(obj (\(X\)) \(<F_{best}\)) then\(X_{best}=X\); \(F_{best}=\) obj (\(X\)); End if
31:If(\(F_{worst}<\) obj (\(Y\))) then\(F_{worst}=\) obj (\(Y\)); End if
32:\(I=I+1\);
33:Endwhile
34:For each \(t\) in \(\{1,2,3,4,5\}\);
35:If(\(N_{t}=0\)) then\(O_{t}=F_{worst}\); \(N_{t}=1\); End if
36:\(\overline{O}_{t}=O_{t}/N_{t}\); \(O=\sum\overline{O}_{t}\); \(R_{t}=\overline{O}_{t}/O\)
37:Endfor
38:\(T=\alpha T\);
39:Endwhile
```  
**Algorithm 1** ANSA
## 7 Results
### Scenario 4: Variable Irradiance and Temperature  
In Table 17, the ANN once again provided the highest efficiency (94.90%) and fastest convergence time, demonstrating excellent adaptability to combined environmental variations. The DQN also delivered a strong performance, although with slightly higher error metrics. The fuzzy and GA-based hybrids offered moderate improvements over the conventional methods, which struggled to maintain accuracy in dynamic conditions.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Algorithm** & **MAE** & **IAE** & **MSE** & **ISE** & **Avg. Eff. (\%)** & **Conv. Time (s)** \\ \hline P\&O & 2.2901 & 23.5039 & 8.4903 & 95.9163 & 93.5644 & 0.2026 \\ InC & 2.3020 & 23.4960 & 8.5452 & 95.8738 & 93.4919 & 0.2026 \\ Fuzzy & 1.2914 & 13.4304 & 3.4588 & 45.2618 & 95.4024 & 0.2389 \\ InC + P\&O & 2.2737 & 23.5271 & 8.4134 & 96.0351 & 93.6665 & 0.2027 \\ InC + GA & 1.3036 & 13.5125 & 2.9062 & 35.2575 & 95.3384 & 0.3754 \\ P\&O + GA & 1.3040 & 13.5121 & 2.9491 & 35.6423 & 95.3367 & 0.3759 \\ ANN & 1.3586 & 14.1022 & 3.5430 & 47.3818 & 95.1503 & 0.1773 \\ DQN & 1.3499 & 14.2418 & 2.9612 & 43.0156 & 95.1882 & 0.2017 \\ \hline \hline \end{tabular}
\end{table}
Table 16: Performance metrics for Scenario 3 (variable temperature conditions).  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Algorithm** & **MAE** & **IAE** & **MSE** & **ISE** & **Avg. Eff. (\%)** & **Conv. Time (s)** \\ \hline P\&O & 1.9080 & 19.5159 & 6.3287 & 71.3833 & 92.3418 & 0.1937 \\ InC & 1.8832 & 19.5411 & 6.2348 & 71.4879 & 92.5729 & 0.1937 \\ Fuzzy & 1.2264 & 12.6995 & 3.0669 & 38.7689 & 94.0541 & 0.2303 \\ InC + P\&O & 1.9232 & 19.5131 & 6.3794 & 71.3694 & 92.2290 & 0.1937 \\ InC + GA & 1.2610 & 13.0040 & 2.8422 & 33.0738 & 93.8670 & 0.3717 \\ P\&O + GA & 1.2561 & 12.9948 & 2.8414 & 33.1874 & 93.8860 & 0.3747 \\ ANN & 0.8227 & 8.8244 & 2.1729 & 34.3348 & 94.8967 & 0.1746 \\ DQN & 1.3351 & 13.8548 & 2.8987 & 38.8920 & 93.2006 & 0.2272 \\ \hline \hline \end{tabular}
\end{table}
Table 17: Performance metrics for Scenario 4 (combined variable irradiance and temperature conditions).  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Algorithm** & **MAE** & **IAE** & **MSE** & **ISE** & **Avg. Eff. (\%)** & **Conv. Time (s)** \\ \hline P\&O & 2.0134 & 20.7350 & 7.1603 & 81.7637 & 92.4991 & 0.2008 \\ InC & 2.0333 & 20.7282 & 7.2322 & 81.7327 & 92.3735 & 0.2008 \\ Fuzzy & 1.2820 & 13.3091 & 3.5243 & 44.9620 & 94.2180 & 0.2370 \\ InC + P\&O & 1.9957 & 20.7619 & 7.0857 & 81.8770 & 92.6808 & 0.2010 \\ InC + GA & 1.3113 & 13.5763 & 3.2909 & 38.6606 & 94.0737 & 0.3749 \\ P\&O + GA & 1.3100 & 13.5586 & 3.2617 & 38.3420 & 94.0772 & 0.3892 \\ ANN & 0.9426 & 9.9433 & 2.7069 & 38.8351 & 95.2038 & 0.1837 \\ DQN & 1.3796 & 14.1881 & 3.5495 & 42.8869 & 93.7760 & 0.2316 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Performance metrics for Scenario 2 (variable irradiance conditions).
# Enhancing Reactive Ad Hoc Routing Protocols with Trust
## 3 The Proposed Method of Trust-Aware Reactive Ad Hoc Routing
### Delay Functions
#### 3.5.2 Linear Delay Function  
The previous function does not take into account the value of the estimated _PDR_. The \(THR\) value helps solve this insufficiency only partially. The Linear Delay function delays RREQ messages linearly according to the _PDR_ of the node that sent or present the RREQ message up to the _MD_ value; we use the threshold, too. The value of \(D\) is calculated as a function of \(THR\), _MD_, and estimated _PDR_ (Algorithm 2).  
```
Input:\(THR\in[0,1];MD>1;PDR\in[0,1]\) Output:\(D\geq 0\) if\(PDR<THR\)then \(D\gets MD*(1-PDR)\) else \(D\gets 0\) endif return\(D\)
```  
**Algorithm 2** Linear Delay function specification  
In this case, we expect that the _THR_ value does not have a positive impact on the results. We also expect that higher _MD_ values increase \(PDR_{avg}\) and both _RDD_ metrics.  
Figure 2: Delay functions. (**a**) Constant Delay function, (**b**) Linear Delay function, (**c**) Exponential Delay function.
## 3 Proposed Multi-User Full-Duplex Visible Light Communication System
### Downlink Section  
The downlink section of the proposed system consists of a transmitter and receiver. The block diagram of the downlink section is shown in Figure 2. Working of the transmitter shall now be examined. Data streams (from users and/or devices) are fed into the data spreading block. The block spreads the user/device data by using a unique orthogonal Walsh code. The next step combines all the encoded data streams and sends it to the modulation block. The ACO-OFDM scheme is used as it is power efficient and to avoid DC bias [31]. The ACO-OFDM is described in detail in Section 3.1.1. The modulated signal is then passed through the DC bias to make sure the overall output of the LED is a bright white light. The signal then propagates through the optical wireless channel and is received by the user or a smart device. The architecture of the user and smart device receiver is mostly similar except a slight modification is made in the photodetector section. Since the user data is sent over the red light region, the user receiver must have a red optical filter at the photodiode (PD). Similarly, as the device data is sent over the green light region, its receiver must have a green optical filter at the PD.  
After filtering the respected signals, the reverse procedure is performed to recover the original data. Every user has his/her own orthogonal Walsh code to increase security and avoid interference and data corruption during data transmission.
## 2 Materials and Methods  
This paper presents results of experiments conducted to obtain the values of fracture mechanics parameters. Values obtained are threshold stress intensity factors, fracture toughness, and resistance curves. As mentioned earlier, experiments have been conducted on test specimens produced by two different production technologies. Test specimens representing conventional technologies were made from drawn rod of a 12 mm diameter procured from Dentaurum GmbH, and those representing selective laser melting used Ti-6 Al-7 Nb metal powder procured from TLS Technik Spezialpulver. Chemical composition of the drawn rod was confirmed with the inspection certificate delivered by the manufacturer, while for the SLM-produced specimens, an Olympus Innov-X Delta element analyzer was used, and the measurement confirmed the composition of the Ti-6 Al-7 Nb alloy. Production of the test specimens made from drawn rod was carried out on a standard turning and milling machine, while for SLM-produced specimen an MCP Realizer II SLM-250 machine was used, produced by MCP-HEK. The powder was prepared via gas atomization, during which molten metal is dispersed into spherical particles with diameters between 20 and 63 \(\upmu\)m. The production process takes place in a protective atmosphere inside a working chamber filled with argon to a level that ensures an oxygen content of less than 0.8%. The process parameters used for production of the test specimens were as follows: laser power 160 W, laser scanning speed 400 mm/s, layer thickness 0.05 mm, and hatch distance 0.12 mm.
# Multimode Guided Wave Detection for Various Composite Damage Types
## 3 Detection of Various Damage Types Using ABT-ABT on a 2-mm Quasi-Isotropic Composite Plate
### Composite Specim
#### 3.1.1 Simulated Delamination  
A CYCOM 5320-1/IM7 12K prepreg was used to manufacture the composite specimen. The simulated delamination was introduced by inserting a Teflon film into the composite plate before curing it in a hot press machine. A circular Teflon film (25 mm in diameter) between plies 12 and 13 was inserted to create the delamination. The thickness of the Teflon film was 50 \(\upmu\)m. The depth of the Teflon insert was based on the experimental results in Reference [8]. The delamination size was determined from the practical application, where the critical delamination size for growth monitoring was around 25 mm in diameter. Figure 2 shows a schematic of a 2-mm quasi-isotropic composite plate with purpose-built delamination. The experimental setup and curing cycle of the specimen manufacturing are given in Figure 3.  
Figure 1: Single-mode wave tuning using an angle beam transducer (ABT) and wedge in a composite laminate.  
Ultrasonic nondestructive testing (NDT) was conducted on the composite plate to verify and image the simulated delamination. An ultrasonic immersion tank was used to inspect the specimen. In the experiment, a 10-MHz, 25.4-mm focused transducer was used. First, a full-specimen NDT inspection was conducted, and there were no other manufacturing defects other than the simulated delamination. Then, a small-area scan was performed to detect and image the simulated delamination. The scan area was 50 mm \(\times\) 50 mm. After an initial scan with the focus on the top surface of the sample, the transducer was refocused at the depth of the Teflon film for a clearer C-scan image. Figure 4 shows the B-scan and C-scan images of the NDT results. The C-scan image shows the presence of the simulated delamination in the composite plate. The depth of the delamination can be observed in the B-scan image (0.5 mm away from the bottom surface). In the C-scan image, the size and shape of the delamination can be clearly observed. Therefore, the simulated delamination could be detected and quantified from the NDT detection, which was consistent with the design.  
Figure 3: Experimental setup and curing cycle for the specimen manufacturing: (**a**) hot press machine; (**b**) curing cycle.  
Figure 2: Schematic of the 2-mm quasi-isotropic [\(-\)45/90/45/0]\({}_{\text{2s}}\) carbon fiber-reinforced polymer (CFRP) composite plate with delamination simulated by the Teflon insert.