esa-sceva/satcom-chunk-collection huggingface dataset:
# Controller-Pilot Data Link Communication Security
## 5 Potential Countermeasures
### Comparison of the Proposed Solutions  
CPDLC technology was introduced without built-in security in mind. All three near-future security solutions presented above offer the possibility to be applied within the already existing system through modification.  
The PACARS standard is based on ECC for both encryption and authorization. It lacks mechanisms to address system's availability. Its documentation supports only FAN-1/A but lacks any information about the ATN-B1 used in continental Europe. We assume it is technically possible to apply it to ATN-B1 for encryption and authorization of ACARS in general and CPDLC in particular.  
The HIP offers better opportunities to improve and fulfill all the five requirements of a secure communication. Not only does it incorporate the tools to encrypt the information, but also brings a secure authentication, including a foundation on how to handle keys using secure Domain Name System (DNS)-servers. However, it has been argued that the safekeeping of private keys will be a major issue during implementation of such systems. The reason to choose the HIP over PACARS is that this standard is supported and well documented, and shows a potential to be implemented in the future generation of digital datalinks within aviation, including new versions of CPDLC, whereas PACARS is limited to the current design of CPDLC and ACARS.  
Further investigations should uncover the feasibility and applicability of the proposed measures in the narrowed scope for implementation.
## 1 Introduction
### Our Contribution  
This paper presents a secure MPSI protocol based on the Bloom filter and Shamir threshold secret-sharing scheme for a large number of participants and unbalanced scenarios.  
Our protocol follows the methods of [27; 28], which were based on the Bloom filter and homomorphic encryption. The Bloom filter is an efficient data structure, which can be used to reduce the computation for both clients and server. The main difference is that we used the relatively efficient ElGamal multiplicative homomorphic encryption algorithm instead of the Paillier additive homomorphic encryption algorithm. The ElGamal encryption algorithm cannot encrypt 0. To address this issue, we randomized the 0s in the Bloom filter, i.e., we obtained the encrypted Bloom filter by following this process: if an item in the Bloom filter is 1, it is encrypted directly; otherwise, if the item is 0, we select a random value to represent it and, then, encrypt the random value. To ensure that participants can obtain the intersection properly, we adopted the idea of the Shamir \(t\)-threshold secret-key-sharing scheme, in which the private key is divided into \(n\) copies and at least \(t\) shares are needed for decryption.  
The contributions of this paper are as follows:  
(1) A secure MPSI protocol based on the Bloom filter and threshold ElGamal encryption scheme for unbalanced scenarios is proposed. In this protocol, the runtime of the client hardly varies with the number of participants, and it is linearly correlated with the data size of the server.  
(2) We present three comprehensive experiments, and the results showed that, in our protocol, the number (\(t\)) of participants has a minimal influence on the computation and runtime of the client. When \(t\geq 2^{4}\), the client's runtime is approximately \(1/(2^{log(t)})\) of the server's runtime. Therefore, our protocol is highly suitable for unbalanced scenarios with a large number of participants. Furthermore, our protocol exhibited significant efficiency improvement compared to the related protocol [28], and the sever and clients' runtimes were approximately \(1/6\) of [28].  
(3) We provide a formal security proof of our protocol against semi-honest adversaries.
# Improving the Performance of Turbo-Coded Systems under Suzuki Fading Channels
## 4 Numerical Results  
### LLR Calculations  
The noise PDFs that are derived in Equations (13) and (14) are utilized in this section to calculate the LLR equations for the coded system, such that  
\[b_{0}=\log_{e}\left(\frac{p(\hat{w}^{I}|_{\hat{x}^{I}=1})}{p(\hat{w}^{I}|_{ \hat{x}^{I}=-1})}\right), \tag{15}\]  
\[b_{1}=\log_{e}\left(\frac{p(\hat{w}^{Q}|_{\hat{x}^{Q}=1})}{p(\hat{w}^{Q}|_{ \hat{x}^{Q}=-1})}\right). \tag{16}\]  
where, superscripts \(I\) and \(Q\) are used to denote the in-phase and quadrature parts of the signal. Equations (15) and (16) represents the LLRs for the 4-QAM scheme of the modulation. Similar equations can be calculated for the 16-QAM scheme with 4 LLR levels, such that signals \(\hat{x}^{I}\) and \(\hat{x}^{Q}\) take four values, and such that \((-3,\,-1,+1,+3)\), with \(b_{0},\ b_{1},\ b_{2},\ b_{3}\) being the soft LLR for the coded system.
## 4 Experimental Results
Growth of the wavelet order increases the number of wavelet coefficients (which is equal to the wavelet order) and smoothens the wavelet and scaling function, which can result in better defect detection. Ultimately, Daubechies 8 wavelet was applied. This range of Daubechies wavelet best indicated the presence of the damage. The main goal of the experiment was to find local discontinuities contained in a registered signal caused by deterioration(s) of the plate. However, the global response of the structure may not indicate the presence of damage in the element. For this purpose, the wavelet transform for signal decomposition was applied. In the case of the undamaged plate analysis, no obvious disturbances were observed in the transformation windows in Figure 6b-d for the first three DWT details. One peak  
Figure 8: Registered signal for a plate with two apertures (**a**), detail 1 (**b**), detail 2 (**c**), detail 3 (**d**) of DWT, Daubechies 8 wavelet, N—number of time intervals  
visible in Figure 7b-d suggests the presence of one defect, which fully coincides with the reality, i.e., the hollow in the tested plate. Two obvious peaks observed in Figure 8b-d indicate the occurrence of two defects in the analyzed steel element--two holes. It should also be noted that no influence of the excitation site was observed on the details of the wavelet transform., i.e., regardless of the excitation site (A, B, C, D, E), the results of the wavelet transform were similar. This confirms the assumption that the type of damage only influences the results (details of transformation). Similarities of individual elements (Figures 6, 7 and 8b-d) to the signal from the transmitter (Figures 6, 7 and 8a) shows that they are wideband signals. It should be noted that the analysis was subjected to not denoised signals, i.e., no measurement errors were removed from them, which is a good prognosis for using the proposed method in field tests.
## 4 Finite Element Analysis Validation  
Finite element analysis (FEA) was performed to validate the analytical model of the RCC mechanism. The ANSYS Workbench finite element software was utilized to construct the FEA model of the mechanism. In order to validate the efficiency of the analytical model sufficiently, three samples with different geometrical parameters were set for the comparisons with FEA results, as shown in Table 1. The material of the mechanism was selected as nylon PA 2200 with a Young's Modulus of 1646 MPa and a Poisson's ratio of 0.33. The three-dimensional (3D) model was generated by Solidworks as a monolithic part, and was subsequently imported to ANSYS in parasolid format. The 3D tetrahedron element was used to mesh this model, and the upper rigid body of the mechanism was fixed.  
The compliance factors in the compliance matrix shown in Equation (11) were obtained using the static analysis module of the ANSYS Workbench. By respectively applying the four different sets of forces (lateral forces \(F_{X}\), \(F_{Y}\), pure moments \(M_{X}\), \(M_{Y}\), vertical forces \(F_{Z}\), and pure moments \(M_{Z}\)) at Point \(O\), the corresponding displacements of Point \(O\) from the FEA results (shown in Figure 7) can be used to calculate the compliance factors. All of the applied loads and moments were 1 N and 1 Nm, respectively, in FEA. The compliance factors calculated from the analytical model and the FEA model are listed in Table 2. The analytical results and FEA results are in good agreement. The relative errors between the two results are less than 8%. The maximum error of 7.83% between the two results is mainly caused by the strong non-linearities of beam flexure when the end-effector undergoes the \(Z\) axis force.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & \(I\) **(mm)** & \(D\) **(mm)** & \(\theta\) (\({}^{\circ}\)) & \(|OO_{1}|\) **(mm)** \\ \hline
1 & 12.5 & 1.5 & 45 & 10 \\
2 & 15 & 2 & 60 & 15 \\
3 & 20 & 1.2 & 30 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geometrical parameters of different samples.  
Figure 6: Illustration for real twist axes and ideal twist axes.
# Research on a Noise Reduction Method Based on Multi-Resolution Singular Value Decomposition
## 3 Analysis
### Comparison among Multiple Models
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**MSE**} & \multicolumn{4}{c}{\(x1\)} & \multicolumn{4}{c}{\(x2\)} \\ \cline{2-9}  & \(-5\) **db** & \(1\) **db** & \(5\) **db** & \(20\) **db** & \(-5\) **db** & \(1\) **db** & \(5\) **db** & \(20\) **db** \\ \hline Undecomposed & 3.351 & 0.707 & 0.299 & 0.009 & 3.206 & 0.833 & 0.303 & 0.010 \\ EMD & 0.662 & 0.187 & 0.916 & 0.002 & 2.162 & 1.015 & 0.697 & 0.468 \\ EEMD & 0.652 & 0.146 & 0.052 & 0.002 & 1.338 & 0.298 & 0.117 & 0.004 \\ \hline VMD & 1.058 & 0.282 & 0.101 & 0.003 & 0.713 & 0.201 & 0.097 & 0.018 \\ SVD & 0.242 & 0.114 & 0.103 & 0.003 & 0.428 & 0.205 & 0.066 & 0.005 \\ MRSVD & 0.184 & 0.071 & 0.022 & 0.002 & 0.138 & 0.112 & 0.039 & 0.004 \\ Wavelet decomposition & 0.241 & 0.256 & 0.239 & 0.201 & 0.811 & 0.654 & 0.627 & 0.612 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: MSE of various noise reduction models at different noise powers. Figure 7: Noise reduction wave in multiple models on signal \(x2\). As shown in Figure 8, it can be clearly found that the SNR of MRSVD is always at the maximum value in the whole range, and the MSE is basically the minimum value, and when the noise power is larger, the advantage of MRSVD is more obvious than other methods, this is consistent with the results reflected in the noise-reduced waveforms in Figures 6 and 7. In summary, MRSVD has better results in signal noise reduction than other methods.
# A Real-Time Simulator for Navigation in GNSS-Denied Environments of UAV Swarms
## 5 Conclusions  
This paper introduces a real-time simulator for navigation in GNSS-denied environments. The sensor models are established and imported to the simulator as a plugin. Based on Gazebo, a series of scenarios is simulated to facilitate the validation of the navigation algorithms in various environments. Addressing navigation challenges in denial environments, an inertial-aided scene matching navigation method is proposed for a camera working in a highly oblique scenario to improve the navigation performance. The position error is 13.1 m when the flight altitude is 800 m. Focusing on relative navigation, a method based on vision and UWB is proposed and the relative positioning accuracy is 0.75 m.  
Figure 11: Formation structure.  
Figure 12: Relative navigation result.  
Although we verify our simulation system in various environments, we find that there are many more outliers in complex conditions. In future research, we will tackle this challenge from the perspective of multirobot cooperative optimization in the continuous time domain. Moreover, relative navigation is closely related to the formation structure. The design of formation based on the relative navigation accuracy is worthy of in-depth research. In addition, we will develop hardware for robotic applications.  
Conceptualization, C.M. and H.Z.; methodology, H.Z.; software, L.Z.; validation, Y.Z., Y.L. and K.F.; data curation, K.F.; writing--original draft preparation, H.Z.; writing--review and editing, L.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key Research and Development Program of China grant number 2022 YFB3205000.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Results and Analysis
### Analysis of NonCompliance Behaviors
#### 4.2.1 Neutralization, SRS and Noncompliance
Likewise, [98] measured technostress among various IT professionals and found that all technostress creators (i.e., overload, complexity, invasion, uncertainty, and insecurity) were significant technostress predictors in organizations. Furthermore, their results indicated that technostress is positively associated with perceived strain and intention to violate ISPs. Reference [11] analyzed the daily ISPEC behavior of employees. These researchers proposed a research model based on cognitive beliefs and moral considerations. This research was based on the experience sampling method to evaluate employees' attitudes daily. Theresearch concluded that work impediments (i.e., daily work stress), positive affect, negative affect, and computer monitoring influence compliance attitude towards compliance behavior daily. An ethical work climate, neutralization, and beliefs (i.e., about the perceived cost of compliance, the perceived cost of noncompliance, and the benefits of compliance) interact towards employees' noncompliance behavior with ISP, as demonstrated in detail by [51]. These researchers presented a framework based on social information processing theory. The work climate consists of three significant bases of moral judgment: egoism (i.e., self-centeredness), benevolent (i.e., maximize other's interests), and principled climates (i.e., binding with laws and policies). The study concluded that work climates influence neutralization and beliefs differentially. Furthermore, the neutralization of noncompliance and the perceived cost of compliance significantly affect ISP employees' noncompliance behavior. Furthermore, moral beliefs, coworker compliance, and self-efficacy also influence daily compliance behavior positively. Reference [99] evaluated cross-cultural factors with deterrence theory. This study examined the influence of culture, deterrence, shame, neutralization, and moral beliefs on ISP compliance. Employees of a large multinational company who were based in forty-eight countries' participated in this study. The results demonstrated that cross-cultural factors were not significant towards deterrence. In contrast, shame, neutralization, and moral beliefs significantly affect ISP noncompliance across all global cultures. Similarly, [100] presented a framework based on punishments, rewards, and situation-specific ethical orientation. They tested their model with six neutralization scenarios. The results of the study proved that gender has no effects on noncompliance intent with ISP, whereas noncompliance with ISPs' vary from person to person and with different situations. The study further stated that rewards and punishments are dependent on the internal and external situation-specific ethical orientation (gender and neutralization techniques). Table 11 shows a complete demonstration of the neutralization and noncompliance studies.
## Materials and Methods
### Surface Treatment of Spacers  
The surface treatment of spacers aimed to roughen the surface of the spacers to improve the adhesion and bonding between the spacer and the concrete at the interface. [PERSON] et al. [2] utilised sandpaper or scored the notches on plastic spacers to improve the quality of the spacer-concrete interface. However, these approaches may not be cost-effective for mass production in a factory, as it would require intensive labour and additional specialised apparatus. [PERSON] et al. [12] introduced surface textures to improve the spacer-concrete interface. Although this approach is promising, it is currently not practical for in-factory manufacturing, as it requires a set of new moulds with various surface textures that is not readily available.  
In light of these surface-treatment approaches and the state-of-practice concrete production, the research presented in this paper has introduced a different surface treatment approach using a hardening retarder. The retarder is widely used in massive concrete construction (e.g., dam and deep beam) to slow the hardening of concrete and to release the heat of hydration over time. Using retarder on the surface of the spacers allows the cement on the surface to remain unset while the inner portion of each spacer reaches its initial setting time. After removing the surface laitance, the aggregate of the spacers would be partially exposed, forming a \"natural\" rough surface.  
The proposed approach can be easily implemented within the current spacer manufacturing process. Specifically, before filling the spacer mould, the inner surface of the mould was covered with a thin film of retarder material to slow down the hardening of the cementitious material on the inner surfaces (i.e., the surfaces in contact with the mould). After filling the mould, the as-cast surface (i.e., the upper edge of the mould) was carefully levelled, on which a thin film retarder material was sprayed evenly. A spacer can only be unmoulded when its inner part reaches the initial setting time. The surface laitance was removed under running water before entering the water curing stage. Examples of spacer surface with and without this surface treatment are presented in Figure 2. Since the spacer was tiny in size and its surface area was small in quantity, the amount of retarder used per spacer was minimal compared to that usually used in the concrete design. The cost of the surface treatment agent per spacer in this study was negligible (around one cent U.S. dollar), which made it economically feasible for the mass production of spacers with surface treatment.
# Detecting Underwater Concrete Cracks with Machine Learning: A Clear Vision of a Murky Problem
## 5 Underwater Concrete Crack Detection Using Machine Learning Approaches
### Transfer Learning and Use of a Pre-Trained Network  
The machine learning approach employed in this paper utilizes transfer learning for training, testing, and validation purposes. This approach is chosen for several reasons. Firstly, it enables the evaluation of various well-established deep learning networks. Moreover, transfer learning saves time by eliminating the need to develop a network from scratch, which can be a challenging task requiring expertise in network design. Furthermore, implementing the transfer learning approach is straightforward and facilitates faster simulation, leading to improved sensitivity within a relatively short time-frame for real-world applications.  
The underlying concept of transfer learning involves leveraging a pre-trained network's knowledge and applying it to a similar task by training or exposing it to an additional set of parameters. In the context of this paper, which focuses on image classification, there are several existing pre-trained image classification networks such as SqueezeNet and AlexNet. These networks have been trained to classify a wide range of images, surpassing 1000 categories. Therefore, it is possible to select one of these networks and retrain it for a new classification task. This retraining process involves adjusting some network parameters to classify a fresh set of images. Fine-tuning the parameters of a pre-trained network is considerably faster and easier compared to building a network from scratch. The paper follows this precise approach; for more detailed information on creating transfer learning networks, please refer to [62]. Although numerous pre-trained networks exist that can be used for comparison, in this work, we have kept the focus on using AlexNet and SqueezeNet for analyzing our test cases. The choice of these two networks is dictated by comparison of speed vs. accuracy, as can be seen in Figure 2. AlexNet and SqueezeNet offer the two most reliable and reasonably accurate networks with very high efficiency for prototyping the algorithms. Since the aim of this paper is a prototype and not commercial development, we use these two networks for the following presented cases.
# Secure Automatic Identification System (SecAIS): Proof-of-Concept Implementation
## 5 How the SecAIS Works
### SecAIS Source Authentication
#### 5.1.1 Creating the SecAIS Message  
Assume that the vessel with MMSI = \"2011-02\(\backslash\)tel: +447700900123\(\backslash\)0\" uses the SecAIS to cryptographically sign and transmit the original conventional ID1 AIS message depicted in Figure 3. Note that, in a SecAIS production infrastructure, the MMSI of the Transmitter should be the same as the MMSI of the original ID1 AIS message (\"265547250\" in the depicted AIS message). This is necessary because the source authentication is validated against the **claimed**ID/MMSI of the ID8 AIS message that encapsulates the signed AIS message (i.e., the original ID1 AIS message).  
The process is as follows: the AIS device of the Transmitter generates the original AIS message for broadcasting. Before transmission, the SecAIS-App intercepts the message and creates the corresponding authentication signature. In this implementation, the original ID1-AIS message (i.e., \"1 AIVDM,1,1,A,13u?etPv2,0n:dDPw UM1U1 Cb069D,0*24\" in the red rectangle in Figure 3) is copied to the SecAIS-App (see Figure 4, part A)]. The SecAIS-App automatically prepends a timestamp and appends the authentication signature. The final message to be signed is the concatenation of the timestamp, the original AIS message, and the signature; see Figure 4, part C. The addition of the signed timestamp is a countermeasure to a potential replay attack, where an adversary may attempt to impersonate a legitimate vessel by using a recorded signed SecAIS message from the victim. Therefore, a SecAIS message needs to have both a valid signature and a valid timestamp to be valid.  
Figure 2: Simulated operation of the Authenticated SecAIS (mode 2) using ECSSI (RFC6507).
# Fronthaul Design for Wireless Networks
## 3 Fronthaul Design Methodology
### Link Design Algorithm
#### 3.1.1 Communication Technologies Models
In mathematical terms [30], the FO link budget, \(L_{B}\), is given by (in dB):  
\[L_{B}=Tx_{min}-Rx_{min}, \tag{23}\]  
where \(Tx_{min}\) and \(Rx_{min}\) correspond to the minimum transmit power (at the transmitter) and minimum received power required (at the receiver), respectively (both in dBW or dBm). The total loss suffered by the transmitted signal along the link, \(T_{L}\), is given by (in dB):  
\[T_{L}=L+(d\times F_{L}), \tag{24}\]where \(L\) stands for the losses in optical connectors (in dB) and \(F_{L}\) corresponds to the normalized fiber loss (in dB per units of distance); typical values for these parameters can be found in the related literature [30]. Finally, an FO link is assumed to be feasible if the FO link margin, i.e., \(L_{B}-T_{L}\), is greater than a user-specified minimum accepted link margin (e.g., 3 dB, as adopted in this work), and if the link bit rate \(\times\) distance product (i.e., the required bit rate times the link length) does not exceed the maximum bit rate \(\times\) distance product of the fiber [10].