esa-sceva/satcom-chunk-collection huggingface dataset:
# The Current Development of Structural Health Monitoring for Bridges: A Review
## 5 Damage Identification Methods
### Modal Parameter-Based Methods  
Once the structure is damaged, the change in stiffness will inevitably lead to changes in the structural modal parameters. Therefore, according to the used modal parameters,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Bridges** & **Authors** & **Feature and/or Application** \\ \hline Yingwuzhou Yangtze River Bridge, China & [PERSON] et al. [108] & Vortex-induced vibration warning \\ \hline Hong Kong–Zhuhai–Macao & [PERSON] et al. [109] & ARIMA and hierarchical warning system \\ Bridge, China & & \\ \hline Ge Xian Bridge, China & [PERSON] et al. [110] & Visualization warning based on Revit \\ \hline Sutong Bridge, China & [PERSON] et al. [111] & Integrated girder vibration indicator \\ \hline Nanjing Dashengguan Yangtze River & [PERSON] et al. [112] & Early warning of beam deflection under temperature and train coupling \\ Bridge, China & & \\ \hline Nanjing Dashengguan Yangtze River & [PERSON] et al. [113] & TDR model for early warning of performance degradation of bridge bearing \\ Bridge, China & & \\ \hline A large-span suspension bridge, China & [PERSON] et al. [114] & TDR model for early warning of abnormal boundary conditions of bridges \\ \hline A single pylon cable-stayed bridge, China & [PERSON] et al. [115] & FTR for early warning of abnormal cable force \\ \hline A cable-stayed bridge, China & [PERSON] et al. [116] & Local correlation model between frequency and multiple environmental factors \\ \hline Jacques Cartier Bridge and [PERSON] et al. [117] & InSAR for visualization warning \\ Bridge, Canada & & \\ \hline Tadcaster Bridge, England & [PERSON] et al. [118] & Improved InSAR for bridge erosion warning \\ \hline A suspension bridge, Malaysia & [PERSON] et al. [119] & A system for measuring vibration based on CFAs \\ \hline Sunshine Skyway Bridge, America & Rizzo and Enshaeian [6] &
\begin{tabular}{c} Early warning when temperature and wind \\ direction changes abnormally \\ \end{tabular} \\ \hline Carroll Lee Cropper Bridge, America & Rizzo and Enshaeian [6] & Early warning of crack propagation \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cases of early warning systems for bridges.  
SDI methods can be divided into four categories: natural frequency-based, mode shapes-based, curvature mode shapes-based (CMS), and methods using both frequency and mode shapes [121]. However, due to the unqualified sensitivity of natural frequency to local damage, the method using only natural frequency as an index can generally only achieve Level 1 SDI, which has been phased out in recent years. Among the other three methods, the mode shape-based and CMS-based methods are the focus of recent research.
# Techno-Economic Analysis of Waste Heat Recovery in Automotive Manufacturing Plants
## Nomenclature  
\begin{tabular}{l l} \(A\) & heat transfer surface area (m\({}^{2}\)) \\ \(C\) & estimated cost (EUR or USD) \\ \(C_{\rm BM}\) & bare module capital cost (EUR) \\ \(C_{\rm encf}\) & annual economic savings of the system (EUR) \\ \(C_{\rm PF}\) & total cost of electricity production (EUR) \\ \(C_{\rm t}\) & operational and maintenance costs (EUR) \\ \(C_{\rm TM}\) & total module capital cost (EUR) \\ \(c\) & specific heat capacity (J/kgK) \\ \(d\) & discount rate (\%) \\ \(F_{\rm BM}\) & bare module factor (-) \\ \(h\) & specific enthalpy (J/kg) \\ \(N\) & total number of the data points (-) \\ \(m\) & mass flow rate (kg/s) \\ \(P\) & power (Watt or kW) \\ \(\dot{Q}\) & heat transfer rate (kW) \\ \(Q\) & heat transfer (J) \\ \(T\) & temperature (\({}^{\circ}\)C) \\ \(U\) & overall heat transfer coefficient (W/(m\({}^{2}\)K)) \\ \(V\) & volume (m\({}^{3}\)) \\ \(\dot{V}\) & volumetric flow rate (m\({}^{3}\)/s) \\ \(X\) & specific capacity (-) \\ \(y\) & lifespan (-) \\ \(\dot{y}\) & the data of the system without a recuperator \\ \(\ddot{y}\) & the data of the system with a recuperator \\ Greek letters & \\ \(\zeta\) & mass sizing parameter for TES (-) \\ \(\eta\) & efficiency (-) \\ \(\rho\) & density (kg/m\({}^{3}\)) \\ Subscripts & \\ BB & battery bank \\ env & environment \\ HP & heat pump \\ is & isentropic \\ LMTD & logarithmic average of the temperature difference \\ max & maximal \\ min & minimal \\ sf & secondary fluid \\ wh & waste heat \\ Abbreviations & \\ AC & alternating current \\ ASHRAE & The American Society of Heating, Refrigerating and Air-Conditioning Engineers \\ CDS & condenser \\ CEPCI & chemical engineering plant cost index \\ COP & coefficient of performance \\ CPR & compressor \\ DC & direct current \\ EVA & evaporator \\ EXP & expander \\ GTR & generator \\ GWP & global warming potential \\ LCOE & levelized cost of electricity \\ MSE & mean squared error \\ \end{tabular}
## 3 Overview of Physical Unclonable Functions
### PUF Implementation
#### 3.2.3 Arbiter PUF  
This type of PUF includes multiplexers coupled in succession, as portrayed in Figure 6. In the case of an RO-PUF design, there is an issue with the frequency oscillation, which is dependent on multiple factors. In the arbiter PUF, the transistors in multiplexers are responsible for producing variable delays. The last part of such a PUF is a D flip-flop. The signal from the top multiplexers is used as an input to the D flip-flop. The wave from the bottom multiplexers is given to the clock signal of the D flip-flop.  
Figure 5: Ring oscillator (RO) [12].  
Figure 6: Arbiter PUF [12].
## 2 Borehole Multi-Source Data Acquisition Technology
### Borehole Directional Acoustic Scanning Technology
Let \(c(t)\big{|}_{h,i}\) represent the i-th scan signal of the directional acoustic transducer at the depth of h, including the transmit signal and the echo signal, and discretize the \(c(t)\big{|}_{h,i}\) signal into \(c\Big{|}_{h,i,j}\), where the first point of \(c\big{|}_{h,i,j}\) is the trigger point of the transmit signal. Then take the absolute value \(\big{|}c\big{|}_{h,i,j}\big{|}\) of the \(c\big{|}_{h,i,j}\) signal to ensure that the amplitude of the signal is positive. Normalize the \(\big{|}c\big{|}_{h,i,j}\big{|}\) signal in \([0,1]\) interval to obtain the following \(c^{*}\big{|}_{h,i,j}\):  
\[c^{*}\big{|}_{h,i,j}=\frac{c\big{|}_{h,i,j}-c\big{|}_{h,i,\min}}{c\big{|}_{h, i,\max}-c\big{|}_{h,i,\min}} \tag{5}\]  
In order to highlight the characteristic area, \(C\big{|}_{h,i,j}\) is obtained by inverting the \(c^{*}\big{|}_{h,i,j}\) signal. If the value of the \(C\big{|}_{h,i,j}\) signal is close to 1, it means that there is no reflected echo at this location and the surrounding rock structure is normal. If the value of signal \(C\big{|}_{h,i,j}\) is close to 0, it indicates that there is an abnormal echo at this location, and the surrounding rock structure may be abnormal. The expression of \(C\big{|}_{h,i,j}\) is as follows:  
\[C\big{|}_{h,i,j}=1-c^{*}\big{|}_{h,i,j} \tag{6}\]  
After completing the above signal processing, fill the value of signal \(C\big{|}_{h,i,j}\) into sector \(S\big{|}_{h,i,j}\) in turn, and the corresponding relationship is \(S\big{|}_{h,i,j}\to C\big{|}_{h,i,j}\), as shown in Figure 5b. After the section mapping at depth h is completed, multiple section maps of different depths are generated in the same way, and then the three-dimensional structure map of the borehole-surrounding rock can be formed by fault interpolation fitting. In order to make the image clear and beautiful, the generated image can be gray mapped according to the size of the information value of the sector area. Through image processing, the contour information of the internal defects in the surrounding rock of the borehole can be extracted.
# Characterization of Control-Dependent Variable Stiffness Behavior in Discrete Muscle-Like Actuators
## Muscle-Like Actuators Composed of Discrete Building Blocks
### SEA Model Implementation  
The motivation behind the development of the SEA model is to use standard state space control methods in designing an linear observer to reconstruct the state for full state feedback control. The observer captures the nominal linear behavior of the average stiffness; it does not account for the nonlinearities in the plant due to the hardening behavior. The crucial aspect of using this model, however, is how control of a discretized plant with all of its complexity is accomplished using a simple linear observer architecture based only on measurement of the load position.  
The Series Elastic Actuator was originally developed by [PERSON] and [PERSON] (see [4] for simple model development and control designed for use with their actuator). The model used in the work for this project borrows concepts from [PERSON] and other developments in the SEA [10; 11]. For this project, a model was developed using a linear motor, a spring and a load mass. This greatly simplifies a model in which each motor unit is treated as its own mass-spring-damper system coupled together in series-parallel configurations. Such a model would reach extremely high order state spaces with even one or two actuation units coupled together. The SEA model sufficiently captures all the dominant dynamic effects of the actuator system.  
Following the procedure for designing an observer as presented in [PERSON] [12] and [PERSON] [13], the necessary steps were taken to create an observer using the SEA model discussed previously, see Figure 9a,b.  
Figure 8: Actuator phase portraits of different switching patterns: (**a**) simple-harmonic motion of variable stiffness actuator; (**b**) switching based upon time; (**c**) switching based upon position; and (**d**) switching activation based upon velocity.  
For any linear system, this would give the controller for the system, however, the discrete muscle-like actuator has one last important characteristic which has not yet been discussed thoroughly. The muscle-like discrete actuator must receive a quantization of the control input \(u\) in order to actuate the particular motor units needed to generate the force dictated by the controller. The output, \(u\), of the controller is a continuously real-valued variable which must be translated to a discrete number of motor units \"on\", an integer value [14]. This section examines this problem and shows the novel solution implemented and the initial value simulations that resulted from this solution.
## 2 Materials and Methods
### Framework of HM-YOLO
#### 2.3.1 Compact Inverted Module (PCIB) Based on Pconv  
To enhance the global feature extraction capability of the backbone network, a new C2 fPCIB module is developed based on the original C2f module of the YOLOv8n model. The C2 fPCIB module integrates the Pconv block and PCIB proposed in this study. This design retains the structure of the original C2f module while inheriting its multi-gradient information flow capability and achieving efficient feature extraction with a lower number of parameters and computational cost.  
Figure 2: The structure of the HM-YOLO.  
As shown in Figure 3, the similarity of features between different channels is visually demonstrated through the visualized strawberry feature maps, indicating the presence of a certain degree of channel redundancy in the feature maps. Thus, further cost optimization of the model becomes feasible.  
To solve above issues, [PERSON] et al. [20] proposed a Pconv operator (see Figure 4b), which effectively reduces redundant computations and memory accesses by applying filters only to selected input channels without interfering with other channels, thereby enhancing the utilization efficiency of spatial features. We assume that the input and output feature maps have the same number of channels, denoted as \(C\), where \(K\) represents the kernel size, and \(C_{P}\) indicates the number of Pconv channels. The FLOPs of Pconv are only \(H\times W\times K^{2}\times C_{P}^{2}\), given that the bias ratio \(r=\frac{C_{P}}{C}=\frac{1}{4}\), which is only \(1/16\) of that required for conventional convolution. Additionally, the memory access volume of Pconv, as shown in Equation (1), is approximately \(1/4\)th of the original value. Clearly, Pconv significantly enhances model processing speed and can effectively extract subtle features from strawberry images.  
\[H\times W\times C_{P}+C_{P}^{2}\times K^{2}\approx H\times W\times 2C_{P} \tag{1}\]  
Figure 4: The components of the PCIB: (**a**) PCIB, (**b**) partial convolution, and (**c**) Pconv Block.  
Figure 3: Original strawberry image (**a**) and its corresponding feature maps (**b–i**) reflecting significant redundancy in different channels.  
We developed an innovative compact transpose block (PCIB) based on the Pconv operator to replace the original bottleneck structure in the C2f module of the backbone network. As shown in Figure 4a, this module primarily consists of three Pconv blocks and two \(1\times 1\) convolutional layers. To maintain feature diversity while reducing computational delay, normalization and activation layers are used only after dimensionality expansion and reduction through the \(1\times 1\) convolutional layers. Figure 4c illustrates the structural design of the Pconv block, which mainly includes the Pconv operator and \(1\times 1\) convolutional layers. Additionally, residual connections [21] are incorporated to effectively prevent issues of gradient explosion or vanishing during multi-layer stacking.
## 3 Research Methods and Parameters
### Loading and Boundary Conditions  
During the assembly process of the segment, the position of the first segment will form a periodic rotation due to the wedge shape of the segment itself, which is the main reason for the position asymmetry. The propulsion force of the synchronous propulsion and assembly mode will increase in gradient with different depths, so different boundary conditions and constraints need to be set for segments at different positions. According to the actual engineering situation, three models were established to simulate the assembly process of the three segments. Gravity is introduced in the assembly, and the different positions of the segments are simulated by setting different directions of gravity.  
In the synchronous propulsion and assembly mode, the external loads borne by the segment during the assembly process are the thrust of the jack and its gravity. The main reaction of the asymmetric force of the segment is the angle error formed between the segment and the jack, as shown in Figure 5. The main reasons for this are the assembly error of the segment and the angle formed by the shield and the segment during the construction process. In this paper, it is assumed that the initial position of the segment is suspended in parallel with the segment that has been looped, and the assembly error is transformed into the radial and tangential components of the jack. The deflection error range of the segment in the study mainly refers to the maximum assembly error between the shield and the segment and the maximum stroke difference of the shielding jack.  
The assembly process of the segment consists of a series of complex segment-liner-segment interaction processes. Compared with the unassembled segment, the assembled segment will form a hoop effect due to the interaction of the annular structure, and the relative displacement is smaller. In the study, the back push surface of the ringed segment and the two sides of the ringed segment are used as boundaries. The longitudinal displacement of the looped segment is eliminated by applying hinge constraints to the back push surface of the looped segment and both sides of the segment.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Input Parameters** & **Value** \\ \hline Concrete density (kg/m\({}^{3}\)) & 2450 \\ Concrete Young’s modulus (GPa) & 36 \\ Concrete Poisson’s ratio (-) & 0.20 \\ Butyronitrile cord packer Young’s modulus (MPa) & 7.29 \\ Butyronitrile cord packer C10 and C01 (MPa) & 0.74 and 0.18 \\ Elastic gasket (EPDM) Young’s modulus (MPa) & 5.32 \\ Elastic gasket (EPDM) C10 and C01 (MPa) & 0.56 and 0.14 \\ Bolting system stiffness (kN/mm) & 9.18 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Constitutive parameters for the finite element models.
## 2 Results and Discussion
### Effect of Nanoparticle on Mitotic Division
The DNA decay observed in Panel K of the figure can be attributed to such oxidative stress-induced damage. This is supported by the data showing DNA decay percentages in Table 4 which increase with nanoparticle exposure. Nanoparticles can also interfere with the formation and function of the mitotic spindle, crucial for proper chromosome segregation during cell division. Studies on cerium oxide (CeO\({}_{2}\)) nanoparticles have shown their ability to disrupt microtubule dynamics, leading to spindle abnormalities and mitotic arrest [53]. The presence of C-shaped metaphase and disturbed metaphase in Panels H and L of the figure suggests similar interference with spindle formation. The quantitative representation in Table 4 highlights the prevalence of such disturbances across different nanoparticle concentrations.
## 2 Overview
### Artificial Neural Networks
#### 2.2.3 TPMs in Quantum Cryptography Error Correction  
As mentioned in the previous section, the security of neural cryptography is highly dependent on the attacker's knowledge of the TPM parameters. The effectiveness of an attack can be reduced if the sender and receiver synchronize from initially similar keys. However, how can pre-synchronization be done without sending the key through a public channel? Here, quantum cryptography fits perfectly. It was mentioned in Section 2.1.2 that the QBER value is usually a few percent. Considering the fact that the attacker cannot gain significant knowledge of the key from the quantum channel without the sender and receiver knowing, TPMs can be used for error correction. In this way, the networks can be initialized with a raw key translated into weights, and synchronization would be much faster, as the weights would already be pre-synchronized. At the same time, the attacker needs to start the synchronization from a random vector, so their probability of success is lower compared to neural cryptography.  
Quantum cryptography, when implemented with ANNs, also benefits in terms of the final key length, as error correction by TTMs does not require removing bits. An algorithm for error correction based on TPM was proposed in [23], and the author states that although privacy amplification is not compulsory, it is still recommended in the future for new attack methods on TPMs in the future. ANN-based synchronization of quantum keys as a solution stands out in terms of the speed of synchronization and the general security of communication compared to either quantum or neural cryptography alone. However, it is important to identify how the values of the K, N, and L parameters affect the overall security of synchronization. Based on the value of L, the maximum number of bits that the attacker can obtain after the \(i\)-th iteration can be calculated as in Equation (9).  
\[Z=\log_{(2L+1)}2^{i} \tag{9}\]  
The impact of K and N is somewhat related, as both values affect the total number of input neurons. In general, the longer the key, the more chances the attacker will have to synchronize their key. However, at the same time, it becomes harder for the attacker to synchronize larger numbers of weights because of starting from a random vector.
# A Service-Constrained Positioning Strategy for an Autonomous Fleet of Airborne Base Stations
## 3 Single Cell User Scheduling
### Round Robin Scheduler  
Round robin consists in the assignment of the same period of time \(t\) (i.e., \(t(x,y)=t\)) to all users in the same region:  
\[t(x_{k},y_{k})=t(x_{j},y_{j})=t,\quad\forall k,j, \tag{15}\]  
where \(t(x_{l},y_{l})\) is the time allocated to a user located at position \((x_{l},y_{l})\) in the region. Therefore, Equation (15) leads to the following value of \(T_{0}\):  
\[T_{0}=\int\int_{A_{i}}t\cdot du=t\int\int_{A_{i}}\lambda(x,y)dxdy=t\cdot N_{i}. \tag{16}\]  
Thus, using Equations (7) and (16), the round robin average rate per user is detailed in Equation (17). This rate expression in Equation (17) comes from the the instantaneous rate in Equation (14), where the simplest channel model, assuming no fading loss, has been applied to facilitate the development of the final scheduler expression. However, once the scheduler has been defined, any other channel model proposed in the system overview could be applied. Thus, according to this, the gain of the transmit antenna is changed by Equation (3) and the fading produced by the presence of buildings is introduced, which leads to the more elaborated average rate per user expression in Equation (18), where \(\xi_{LOS}\) and \(\xi_{NLOS}\) are expressed in linear scale.  
\[\begin{split}\overline{R}\big{|}_{A_{i}}=&\frac{ \int\int_{A_{i}}c(x-x_{i},y-y_{i})du}{N_{i}^{2}}\\ =&\frac{\int\int_{A_{i}}BW_{A}\cdot\log_{2}\left(1+ \frac{k\cdot G_{T}}{BW_{A}\cdot((x-x_{i})^{2}+(y-y_{i})^{2}+h_{i}^{2})}\right) \lambda(x,y)dxdy}{\left(\int\int_{A_{i}}\lambda(x,y)dxdy\right)^{2}},\end{split} \tag{17}\]  
\[\begin{split}\overline{R}|_{A_{i}}=&\frac{1}{\left( \int\int_{A_{i}}\lambda(x,y)dxdy\right)^{2}\int\int_{A_{i}}BW_{A}\cdot\left(P_ {LOS}(x-x_{i},y-y_{i}\,h_{i})\right.\\ &\left.\quad\cdot\log_{2}\left(1+\frac{k\cdot h_{i}^{2}}{\xi_{ LOS}\cdot BW_{A}\cdot((x-x_{i})^{2}+(y-y_{i})^{2}+h_{i}^{2})^{2}}\right)\right.\\ &\left.\quad+P_{NLOS}(x-x_{i},y-y_{i},h_{i})\cdot\log_{2}\left( 1+\frac{k\cdot h_{i}^{2}}{\xi_{NLOS}\cdot BW_{A}\cdot((x-x_{i})^{2}+(y-y_{i}) ^{2}+h_{i}^{2})^{2}}\right)\right)\lambda(x,y)dxdy.\end{split} \tag{18}\]  
Note that, although the round robin scheduler leads to a simple expression, it has an important disadvantage. All the users are served with the same fraction of time, whereas the instantaneous rate per user depends on the distance between the user and the ABBS, on the radiation pattern of the antenna and on the obstruction of the direct path due to the presence of buildings. Thus, a user placed far away from the UAV will obtain a lower average transmission rate than a closer user, which entails an unfair service among the users in the same region. This is the reason the equal rate scheduler is proposed in the next subsection.
## 4 Fault-Tolerance Control Strategy
### Differential Drive Control
#### 4.3.1 Reference Model  
The reference model was a simple two-degrees-of-freedom auto dynamic model, as demonstrated in Figure 15.  
Figure 14: Working principle of differential drive.  
Based on the forces, the differential equation of motion for the vehicle are given as follows:  
\[(k_{1}+k_{2})\beta+\frac{1}{u}(ak_{1}-bk_{2})w_{r}-k_{1}\delta_{f}-k_{2}\delta_{ r}=m(\dot{v}+uw_{r}) \tag{20}\]  
\[(ak_{1}-bk_{2})\beta+\frac{1}{u}(a^{2}k_{1}+b^{2}k_{2})w_{r}-ak_{1}\delta_{f}+ bk_{2}\delta_{r}=I\dot{w}_{r} \tag{21}\]  
where \(m\) denotes the vehicle mass; \(\delta_{f}\), \(\delta_{r}\) denote the fore and aft wheel angle, respectively; \(v\) denotes the lateral velocity; \(u\) denotes the vertical speed; \(w_{r}\) denotes the yaw; \(\beta\) is the center of mass's side slip angle; \(k_{1}\), \(k_{2}\) the cornering stiffness of the fore and aft.  
The rate of yaw is chosen as the control parameter of the steady-state response, and the rate of yaw in Equation (22):  
\[w_{rd}=\frac{u/L}{1+Ku^{2}}\delta_{f} \tag{22}\]  
where \(K\) is the stability factor.  
\[K=\frac{m}{L^{2}}\bigg{(}\frac{a}{k_{2}}-\frac{b}{k_{1}}\bigg{)} \tag{23}\]  
At the same time, considering the impact of vehicles on the road in the process of driving, its critical value is as in Equation (24):  
\[w_{r_{d,bound}}=0.85\frac{\mu g}{u} \tag{24}\]