esa-sceva/satcom-chunk-collection huggingface dataset:
# Bridge Health Monitoring Using Proper Orthogonal Decomposition and Transfer Learning
## 3 Validating TL for Bridge Damage Detection Using Simulated Experiments  
### Railway Bridge Study  
The current study initially investigated a multi-span, in-service, steel railway bridge in central Nebraska, with a simply supported truss span being the focus. The bridge carries two tracks and its geometric and material properties are explained in more detail elsewhere [14; 22]. The studied truss span is 44.7 m long and includes six panels. The floor system consists of timber ties resting on stringers that are connected to floor beams. The floor beams, in turn, distribute loads to truss lower chord panel points. Stringers are spaced 2.15 m center-to-center with tracks spaced 3.95 m center-to-center. Isometric, plan, and elevation views of the examined truss span, which also details instrument locations used for field testing, are shown in Figure 1.  
To perform damage identification and extract damage features, strain time histories from bridge response to train passages were obtained from FE models. Stringer-to-floor beam connections were selected as the locations of interest because these spots were reported to be one of the most critical locations in terms of fatigue-induced damages in steel riveted railway bridges [22]. The locations of interests are depicted in Figure 1b and consisted of 20 locations that corresponded to strain transducer placement on the actual structure [34].
## 4 Investigating CRPS-Sum Properties
### The Effect of Summation on CRPS-Sum  
To calculate CRPS-Sum, first, we summed the time-series over the dimensions [18]. Although this aggregation let us turn a multivariate time series into a univariate one, we lost important information concerning the performance of the model in each dimension. Furthermore, the values of dimensions that are negatively correlated negate each other and, consequently, those dimensions will not be presented in aggregated time series.  
For instance, assume we have a multivariate time series \(x\) with two dimensions. Our data follow a bivariate Gaussian distribution with \(\mu=\begin{pmatrix}0\\ 0\end{pmatrix}\) and \(\Sigma=\begin{pmatrix}1&-1\\ -1&1\end{pmatrix}\). Hence, the following relation holds between dimensions:  
\[x^{0}=-x^{1}. \tag{13}\]  
By summing over dimensions, we have:  
\[\sum_{i}x^{i}=0\,. \tag{14}\]  
Clearly, after summation, we acquire a signal with constant zero, and all the information regarding the variability of the original time series is lost.  
To acquire information regarding the performance of the model on each dimension, we can calculated CRPS first. Once the CRPS was validated, we could calculate the CRPS-Sum to check how well the model learned the relationship between the dimensions, and even, at this point, we should not forget the flaws of CRPS-Sum that we witnessed, e.g., sensitivity toward data covariance and loss of information during summation. Unfortunately, the importance of CRPS is ignored in most of the recent papers in the probabilistic forecast domain. In these papers, the CRPS is either not reported at all [20,21], or the argument about the performance of the model is made solely based on CRPS-Sum [18,19]. Considering the flaws of CRPS-Sum, this trend can put the assessment results of these recent models in jeopardy.
## 4 Experiments
### Effect of Different Network Configurations  
A series of experiments are conducted to explore the impact of key hyper-parameter settings on model performance, employing different network configurations. All models adhere to uniform settings except for the studied variable. Figure 6 illustrates the results obtained with varying model dimensions and attention head numbers across all datasets,  
Figure 5: Comparison with Ground Truth.  
with the prediction step set to 30. In comparison to attention heads, the STGNN model is more sensitive to changes in dimensionality. Across all datasets, an augmentation in model dimensions correlates with a discernible reduction in RMSE and MAE. Notably, this trend is particularly pronounced in the Hangzhou dataset, where more substantial improvements are discernible. In contrast, it is noteworthy that an increase in the number of attention heads does not consistently yield improved prediction performance. Optimal results are attained when employing eight attention heads.
# Dynamic Modeling and Optimization of Tension Distribution for a Cable-Driven Parallel Robot
## 3 Dynamics Model of the Robot
### The Driving Device's Dynamics Model  
The generation of a cable's tension is owing to the drag of the cable drum driven by the motor, so it is necessary to establish its dynamics model. Similarly, according to the Newton-Euler method, the dynamic model of the \(i\)th cable drum is as follows:  
\[J_{d_{i}}\ddot{\tau}_{d_{i}}+V_{d_{i}}\dot{\gamma}_{d_{i}}+r_{d_{i}}T_{i}=\tau _{d_{i}},(i=1,2,\dots,8) \tag{9}\]  
where \(J_{d_{i}}\) and \(V_{d_{i}}\) are the cable drum's inertia and viscosity damping coefficient. The radius of the cable drum denotes \(r_{d_{i}}\). \(\tau_{d_{i}}\) is the applied moment generated by the servo motor. \(\gamma_{d_{i}}\) represent the rotation angle of the cable drum and it can be obtained as follows:\[\gamma_{d_{i}}=r_{d_{i}}^{-1}(l_{i}(k)-l_{i}(k-1)) \tag{10}\]  
\(l_{i}(k)\) and \(l_{i}(k-1)\) are the length of the \(i\)th cable at the current time and last time, respectively. Based on Equation (9), the tension's value of the \(i\)th cable is calculated by  
\[T_{i}=r_{d_{i}}^{-1}\Big{(}\tau_{d_{i}}-J_{d_{i}}\tilde{\gamma}_{d_{i}}-V_{d_{i }}\dot{\gamma}_{d_{i}}\Big{)},(i=1,2,\ldots,8) \tag{11}\]  
Because of \(T_{i}=\|\mathbf{T}_{i}\|\), \(\mathbf{T}_{i}\) can be rewritten as \(\mathbf{T}_{i}=T_{i}\mathbf{u}_{i}\). \(\mathbf{u}_{i}\) is the unit directional vector of \(\mathbf{T}_{i}\). According to Equation (5), it can be yielded as follows:  
\[\mathbf{T}=\left[\begin{array}{cccccc}T_{1}^{T}&\mathbf{T}_{2}^{T}&T_{3}^ {T}&\mathbf{T}_{4}^{T}&\mathbf{T}_{5}^{T}&\mathbf{T}_{6}^{T}&\mathbf{T}_{7}^{T}&\mathbf{T}_{8}^{T }\end{array}\right]^{T}\\ \begin{array}{cccccc}=\left[\begin{array}{cccccc}T_{1}\mathbf{E}_{3 \times 3}&&&&\\ &T_{2}\mathbf{E}_{3\times 3}&&&&\\ &&\ddots&&\\ &&T_{8}\mathbf{E}_{3\times 3}\end{array}\right]\left[\begin{array}{c}\mathbf{u}_{1}\\ \mathbf{u}_{2}\\ \vdots\\ \mathbf{u}_{8}\end{array}\right]\\ \begin{array}{c}=\left[\begin{array}{cccccc}r_{d_{1}}^{-1}\Big{(}\tau_{d_ {1}}-J_{d_{1}}\tilde{\gamma}_{d_{1}}-V_{d_{1}}\dot{\gamma}_{d_{1}}\Big{)}\mathbf{ E}_{3\times 3}&&\\ &&\ddots&&\\ &&r_{d_{8}}^{-1}\Big{(}\tau_{d_{8}}-J_{d_{8}}\tilde{\gamma}_{d_{8}}-V_{d_{8}} \dot{\gamma}_{d_{8}}\Big{)}\mathbf{E}_{3\times 3}\end{array}\right]\left[\begin{array}{c}\mathbf{u}_{1} \\ \vdots\\ \mathbf{u}_{8}\end{array}\right]\in\mathbb{R}^{24\times 1}\end{array} \tag{12}\]  
Combined Equations (2) and (12), the dynamics model of the end-effector considering driving devices is as follows:  
\[M\dot{\mathbf{x}}+V\mathbf{x}+\mathbf{G}=\mathbf{I}\begin{bmatrix}r_{d_{1}}^{-1}\Big{(}\tau_{ d_{1}}-J_{d_{1}}\tilde{\gamma}_{d_{1}}-V_{d_{1}}\dot{\gamma}_{d_{1}}\Big{)}\mathbf{E}_{3 \times 3}&&&&\\ &\ddots&&\\ &&r_{d_{8}}^{-1}\Big{(}\tau_{d_{8}}-J_{d_{8}}\tilde{\gamma}_{d_{8}}-V_{d_{8}} \dot{\gamma}_{d_{8}}\Big{)}\mathbf{E}_{3\times 3}\end{bmatrix}\begin{bmatrix}\mathbf{u}_{1}\\ \vdots\\ \mathbf{u}_{8}\end{bmatrix}+\mathbf{E}_{r} \tag{13}\]  
Figure 4: Dynamic analysis diagram of a cable-driven parallel robot.
# Fivadmi: A Framework for In-Vehicle Anomaly Detection by Monitoring and Isolation +
## 4 Implementation of FIVADMI
### Trusted Execution Environment (TEE)  
Open Enclave (OE) is an open source project to develop applications in a _trusted execution environment_. OE enables to build trusted programmes called enclave applications that interact with untrusted applications, known as _host applications_[26]. Through SGX-enabled operations and support of an Intel CPU with SGX capabilities, enclaves ensure the confidentiality of execution and protection of data [26]. Figure 11 gives an overview of the OE architecture. To execute the code of an enclave, a host application, known to the enclave, calls the interface of the functions in the enclave; these calls are Enclave Calls (ECalls). Enclaves can also call untrusted functions and services provided by the operating system through an interface of the host application; these calls are Outside Calls (OCalls) [76]. ECalls and OCalls are listed as trusted and untrusted functions respectively in a configuration file before the enclave is built; Edger8r, a tool part of the Intel SGX SDK, generates the interfaces specified between the host and the enclave using an Enclave Definition Language (EDL). Interactions are restricted to the specified interfaces throughout the lifecycle of the enclave [77].  
In FIVADMI, an enclave is part of the application layer of the ECU. It hosts OBD messages essential to define its functionality. In our prototype, the data hosted within the enclave includes data recorded from real vehicles and used for testing as well as data generated at runtime to simulate real-life behaviour of in-vehicle traffic. While the dynamic data is generated at runtime, the static data is stored in encrypted form. Decryption occurs through a call from the host bound to the enclave.  
Figure 10: Overview of HPC Based Detection Approaches for Side Channel Attacks.
## 3 Vibration Signal Model for a Planetary Gearbox
\begin{split} x_{0}(t)=\sum\limits_{k=-\infty}^{+\infty}A_{1}\cdot J_ {k}(B)\Big{\{}&\cos[2\pi(f_{m}+kf_{c})t+\varphi_{k}]+\frac{A}{2} \cos[2\pi(f_{m}+kf_{c}+f_{c})t+\phi_{k}]\\ &+\frac{A}{2}\cos[2\pi(f_{m}+kf_{c}-f_{c})t+\theta_{k}]\Big{\}} \end{split}  \tag{7}\]  
where \(f_{c}\) is the characteristic frequency, \(\varphi_{k}=k\phi_{1}+\theta_{1}\), \(\phi_{k}=k\phi_{1}+\varphi_{1}+\theta_{1}\) and \(\theta_{k}=-k\phi_{1}+\varphi_{1}-\theta_{1}\). \(A_{1}\), \(J_{k}(B)\) and \(A\) are constants, so (7) is simplified using the properties of trigonometric functions as:  
\[x_{0}(t)=\sum\limits_{k=-\infty}^{\infty}C_{k}\cos[2\pi(f_{m}+kf_{c})t+\alpha _{k}] \tag{8}\]  
where \(C_{k}\) and \(\alpha_{k}\) are constants that are related to \(A_{1}\), \(A\), \(\{J_{i}(B)\}\), \(\{\varphi_{i}\}\), \(\{\phi_{i}\}\) and \(\{\theta_{i}\}\). Equation (8) derives a series of sidebands for the damaged gear, with the spacing within the adjacent sidebands equal to the corresponding characteristic frequency for the faulty gear (\(f_{c}\)). In terms of harmonics, the \(nf_{m}\) (\(n\in\mathrm{N}^{*}\)) of the meshing frequency, the sidebands occur around the positions \(nf_{m}\pm kf_{c}\) (\(k\in Z\)). Equation (8) represents the vibration signal model for a fixed-shaft gear train transformed by a planetary gear train. Sensor rotation causes amplitude modulation and frequency modulation in the vibration signal. This AM is modeled using a Hanning function [19; 20]. For simplification, only the modulating phenomena inside the essential frequency are taken into consideration, and the vibration signal (\(t\)) is measured through the sensor and defined as:  
\[x(t)=A_{H0}[1+A_{H1}\cos(2\pi f_{H}t+\theta_{H})]x_{0}(t) \tag{9}\]  
where \(x_{0}(t)\) is the original vibration signal represented by (8); and \(f_{H}\) is the relative rotation frequency of the sensor, which corresponds to the rotation frequency of the planet carrier; \(A_{H0}\) and \(A_{H1}\) are the signal attenuation coefficient and the strength of amplitude modulating, respectively. Substituting (8) into (9) gives:  
\[x(t)=\sum\limits_{k=-\infty}^{\infty}D_{k}\cos[2\pi(f_{m}+kf_{c}\pm f_{H})t+ \beta_{k}] \tag{10}\]  
where \(D_{k}\) and \(\beta_{k}\) (\(k\in Z\)) are the constants. The effect of sensor rotation on the measured vibration signal is seen as spectral lines at \(\pm f_{H}\) locations around sidebands \(f_{m}\pm kf_{c}\) (\(k\in Z\)). In terms of the harmonics of mesh frequency \(f_{m}\), the modulation effects and harmonics of \(f_{H}\), the sideband locations are \(nf_{m}+kf_{c}+lf_{H}\) (\(n\in\mathrm{N}^{*}\), \(l\in Z\)), where \(f_{c}\) is the characteristic frequency of one corresponding gear damage. For planetary gear sets, the configuration of the gearbox is the defining factor. Section 4 discusses the calculation of the characteristic frequencies of a planetary gearbox. After a period of operation, gears become worn, and manufacturing and assembly errors affect the system, so there are some variations in the theoretical signal model.
###### Abstract  
To detect moving weak targets in the dual function radar communication (DFRC) system of an orthogonal frequency division multiplexing (OFDM) waveform, a modified hybrid integration method is addressed in this paper. A high-speed aircraft can cause range walk (RW) and Doppler walk (DW), rendering traditional detection methods ineffective. To overcome RW and DW, this paper proposes an integration approach combining DFRC and OFDM. The proposed approach consists of two primary components: intra-frame coherent integration and hybrid multi-inter-frame integration. After the echo signal is re-fragmented into multiple subfragments, the first step involves integrating energy across fixed situations within intra-frames for each subcarrier. Subsequently, coherent integration is performed across the subfragments, followed by the application of a Radon transform (RT) to generate frames based on the properties derived from the coherent integration output. This paper provides detailed expressions and analyses for various performance metrics of our proposed method, including the communication bit error ratio (BER), responses of coherent and non-coherent outputs, and probability of detection. Simulation results demonstrate the effectiveness of our strategy.  
OFDM; intra-frame; inter-frame; coherent integration +
Footnote †: journal: remote sensing
# Research on Corrosion Damage Evolution of Aluminum Alloy for Aviation
## 3 Results and Discussion  
### Corrosion Evaluation  
The macroscopic corrosion morphology of the specimen under different corrosion times is shown in Figure 4. From the macroscopic morphology, it can be clearly seen that as the corrosion time continued to increase, the corrosion type gradually changed from pitting to denudation after 120 h.  
In the early stage of corrosion, when the accelerated corrosion time was 8h, the surface gloss of the specimen was good and the corrosion pits were few and relatively uniform. When the accelerated corrosion time was 24 h and 48 h, there were corrosion pits visible to the naked eye, the pit density increased, and the color gradually darkened, but the surface of the specimen still had metallic luster. When the accelerated corrosion time was 72 h and 96 h, the specimen had basically lost its metallic luster. When the accelerated corrosion time was 120 h, the corrosion pits on the surface of the specimens had been aggregated into large areas, the small pits were connected to each other to form large pits, and the surface roughness of the specimens was further increased. Through further observation by VEGA II XMU scanning electron microscope (TESCAN, Shanghai, China), it was found that several micro cracks along intergranular corrosion appeared in the direction of the plane (as shown in Figure 5).  
In the denudation stage, when the accelerated corrosion time was 144 h, the surface of the specimen began to show obvious erosion characteristics: Bulge. When the accelerated corrosion was 192 h, the erosion phenomenon was more obvious. From the macroscopic point of view, we saw that there were a lot of scales and flakes on the surface of the specimen. Figure 6 shows the 3D morphology of the specimen surface, which can be seen that the surface of the specimen has a \"cliff-type\" shape due to  
Figure 3: Accelerated corrosion environment spectrum.  
the peeling of the material. It indicates that the phenomenon of denudation and stratification occurred. When the corrosion was accelerated for 240 h, the whole exfoliation layer appeared on the surface of the specimen. Since the damage evolution law of pitting stage (corrosion pits) was mainly studied in this paper and it was difficult to accurately obtain the depth of corrosion pits' data after denudation, the corrosion cycle before 120 h was selected for the following analysis.
# Analysis of Security Issues and Countermeasures for the Industrial Internet of Things
## 3 Security Issues in IIoT Systems
### Security Issue 2--Infecting Data Packets  
In this type of attack, an attacker controls an IIoT device by injecting malicious codes into the data packet [79]. Attackers mostly try to gain extensive control over the resource of a victim node (e.g., source, destination, number, size, as well as the transmission time). This can be done in two different ways, via an _active attack_ and a _passive attack_[80]. These are discussed as follows:  
In the former case (i.e., the active attack), one of the most frequent attacks is the _Man in the Middle_ (MITM) attack. In this case, the attacker joins the network as a legitimate user and gains control over the nodes through the active attack path to sniff information. In an MITM attack, an attacker sits in between two nodes (the sender and the receiver) and creates an independent connection by secretly relaying traffic between the nodes, making the attacked nodes believe that they are directly communicating with one another (by impersonating the sender for transmitting information to the receiver) [47]. This is a potential threat to the IIoT systems towards data modification and data fabrication.  
In such cases (i.e., data modification and data fabrication), the attacker captures data packets and collects its information (e.g., destination header) and then tampers (i.e., doing intentional alteration of data packets) with the information to inject a malicious code and makes an unauthorized attempt to channel the modified data packet to another unsecured network direction rather than its actual destination [81]. By tampering with the data packets, attackers get unauthorized access to the data traffic as they drop data packets while passing through the unsecured channe, or cause a further delay in forwarding other data packets.  
Another form of the active attack is the _jamming channels_ attack. In this type of attack, attackers exploit the transmission and stuff the network with the infected data packets to disrupt normal communications. This can be done by re-sending a data packet over a long period of time to saturate the system's buffer [47]. Furthermore, unauthorized data manipulation via spoofing of identities (e.g., manipulation of a node's digital identity by impersonating falsified data) can allow an attacker to successfully gain control over the data packets of the victim node [82].  
In the latter case (i.e., the passive attack), the most common form of attack is _eavesdropping_, where an attacker takes unauthorized control over an IIoT node and silently listens to the real-time conversations between the users and captures the information transmitted. This allows the attackers the ability to capture and manipulate sensitive information in real-time through _traffic analysis_ and _traffic monitoring_ that intercepts and reads different communication patterns and other secret information (e.g., decipher the payload) between the victimized nodes [83].
## 3 Results and Discussion
### Analysis of Results of Recorded Deflections  
The analysis of deflection, as the most obvious indicator of the structure behavior under the influence of load, is paid special attention in this research. A comparative analysis of experimentally determined deflections of continuous beams depending on the percentage of beam reinforcement for Sections I and II in the middle of the span of continuous beams was performed (Figure 4).  
In the curves that define the relationship between load and deflection, one can observe the characteristic phases through which the beam passes during the application of the test load: (1) until the onset of the first cracks, (2) from the onset of the first cracks to the onset of yielding in tensioned steel reinforcement and (3) from the onset of yielding to the failure of the beam. For all beams, an almost identical behavior at low load levels is observed in the elastic phase, before the formation of the first crack. With the emergence of the first crack, the relation between the load and the deflection becomes nonlinear.  
Comparative representation of the measured deflections of Sections I and II of continuous beams, reinforced with different percentages of reinforcement for different load intensities is shown in Figure 4. Based on the results, the flexural stiffness of the beams can be defined as the ratio of force and deflection.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \begin{tabular}{c} **Beam** \\ **Label** \\ \end{tabular} & \begin{tabular}{c} **Tensioned** \\ **Steel** \\ **Reinforcement** \\ \end{tabular} & \begin{tabular}{c} **Compressed** \\ **Concrete** \\ \end{tabular} & \
\begin{tabular}{c} **Tensioned** \\ **Concrete** \\ \end{tabular} &
\begin{tabular}{c} **Deflection** \\ **Deflection** \\ \end{tabular} \\ \hline \multirow{3}{*}{Section I} & B–1 & SG4 & SG1 & LVDT3 & LVDT1 \\  & B–2 & SG4 & SG1 & LVDT3 & LVDT1 \\  & B–3 & SG4, SG5 & SG1 & LVDT3 & LVDT1 \\ \hline \multirow{3}{*}{Section III} & B–1 & SG6 & SG3 & - & - \\  & B–2 & SG6 & SG3 & - & - \\  & B–3 & SG8, SG9 & SG3 & - & - \\ \hline \multirow{3}{*}{Section II} & B–1 & SG5 & SG2 & LVDT4 & LVDT2 \\  & B–2 & SG5 & SG2 & LVDT4 & LVDT2 \\  & B–3 & SG6, SG7 & SG2 & LVDT4 & LVDT2 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Position of strain gauge tapes (SG) and linear variable displacement transducers (LVDT) dilatometers in Sections I, II and III of the tested beams.  
Figure 4: Load vs. deflection curves in Section I—left span and Section II—right span for tested Beams B–1, B–2 and B–3.  
The stiffness of non-cracked concrete section is approximately 105 kN/mm, 115 kN/mm and 125 kN/mm for the Beams B-1, B-2 and B-3, respectively. After the crack propagation the flexural stiffness decreases to approximately 50 kN/mm, 60 kN/mm and 70 kN/mm for the Beams B-1, B-2 and B-3, respectively. The yielding of tensioned reinforcement leads to excessive decrease of the flexural stiffness up to the failure of the beams.