esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Characterization of Damage Under Blast Loading
### Explosion in Center of Bridge
#### 4.1.1 Disruption of Mode II Response Process  
In this section, the response process of a simply supported steel box girder under the action of a blast shock wave is studied, with case 1 as the research object. Figure 7 shows the damage pressure map of a steel box girder structure under near-field explosion loading over time.  
From Figure 7, it can be seen that when the time is less than 0.3 ms, the explosion wave is freely transmitted in the air; and in the time period between 0.3 ms and 0.6 ms, the explosion wave reaches the top plate and has a reflective effect on it, resulting in large-scale deformation of the top plate by the impact of the shock wave. Then, at 0.6 ms, the top plate is notched, so that the shock wave can penetrate deep into the interior of the box and be transmitted downward. At 1.80 ms, the shock wave has a reflection effect on the top and both sides of the cross partitions and stiffening ribs, and then continues along the cross partitions downward. The shock wave can penetrate into the interior of the box and pass downward. At 1.8 ms, the shock wave causes a reflection effect on the top and both sides of the partitions and stiffening ribs, and then continues to move downward along the partitions. At 15 ms, the shock wave arrives at the base and affects the base of the steel box girder, which causes large-scale deformation of the base. At about 21 ms, the broken fragments of the top plate impact the base, which brings great plastic deformation to the base. The base of the box girder is deformed by the broken fragments of the top plate at around 21 ms, which brings huge plastic deformation to the base but does not break it, and with the formation of the top notch, the top is also pulled out of its petal-like folds. Finally, at 30 ms, the whole steel box girder returns to a stable state, and all the reactions have come to an end. Through the above analysis, we can characterize the behavior of mode II damage to the steel box girder into three parts:  
Figure 6: Steel box girder damage form (unit: kg).  
Figure 7: Case 1 steel box girder damage pressure map (unit: ms).  
In the first stage, there is damage to the top plate: when the explosion shock wave reaches the top plate of the steel box girder, the top plate will immediately experience local plastic deformation, with an increase in deformation and cracks, and even the gradual appearance of splits. The cracks increase the formation of petal-like curls.  
In the second stage, the shock wave acts inside the box girder: the shock wave enters the box girder and affects the transverse and longitudinal partitions, stiffening ribs, and base plate.  
In the third stage, the fragments of the top plate of the bridge collide with the base plate: these fragments impact the base plate at a high velocity, and, together with the shock wave, cause plastic deformation of the base plate.
# Cross-Layer Optimization for Heterogeneous MU-MIMO/OFDMA Networks
## 4 Optimization Framework
### Constraints on MU-MIMO and OFDMA Operation
#### 4.2.2 Bandwidth  
The total bandwidth allocated to node \(i\) at time slot \(t\) cannot exceed the maximum available bandwidth (i.e., \(B_{i}\)). Due to the different minimum bandwidth requirements for MU-MIMO and OFDMA, we also have the following constraint:  
\[B^{\text{(MIMO-min)}}\cdot(m_{i\bar{t}}^{\text{x}}+m_{i\bar{t}}^ {\text{y}})+B^{\text{min}}\cdot(o_{i\bar{t}}^{\text{x}}+o_{i\bar{t}}^{\text{ y}}) \leq B_{i}^{\text{min}}\sum_{k}(m_{itk}^{\text{x}}+m_{itk}^{\text{ y}}+o_{i\bar{t}}^{\text{x}}+o_{i\bar{t}}^{\text{y}})\] \[\leq B_{i}\cdot(m_{i\bar{t}}^{\text{x}}+m_{i\bar{t}}^{\text{y}}+o_{ i\bar{t}}^{\text{x}}), \forall i,t. \tag{35}\]  
In addition, since the range of subchannels allocated to the nodes cannot exceed the available bandwidths of the nodes, we have:  
\[B^{\text{min}}\cdot(k_{1}-k_{2}+1)-B_{i}\leq U\cdot(2-m_{i\bar{t} _{1}}^{\text{x}}-m_{i\bar{t}_{2}}^{\text{x}}), \forall i,t,(1\leq k_{1},k_{2}\leq K) \tag{36}\] \[B^{\text{min}}\cdot(k_{1}-k_{2}+1)-B_{i}\leq U\cdot(2-m_{i\bar{t} _{1}}^{\text{y}}-m_{i\bar{t}_{2}}^{\text{y}}), \forall i,t,(1\leq k_{1},k_{2}\leq K)\] (37) \[B^{\text{min}}\cdot(k_{1}-k_{2}+1)-B_{i}\leq U\cdot(2-o_{i\bar{t} _{1}}^{\text{x}}-o_{i\bar{t}_{2}}^{\text{x}}), \forall i,t,(1\leq k_{1},k_{2}\leq K)\] (38) \[B^{\text{min}}\cdot(k_{1}-k_{2}+1)-B_{i}\leq U\cdot(2-o_{i\bar{t} _{1}}^{\text{y}}-o_{i\bar{t}_{2}}^{\text{y}}), \forall i,t,(1\leq k_{1},k_{2}\leq K) \tag{39}\]  
where \(k_{1}\) and \(k_{2}\) are subchannel indices and \(k_{2}\leq k_{1}\). Please note that we can combine the above constraints into one since \(m_{i\bar{t}}^{\text{x}}+m_{i\bar{t}}^{\text{y}}+o_{i\bar{t}}^{\text{x}}+o_{i \bar{t}}^{\text{y}}\leq 1\) is guaranteed.  
Lastly, based on the assumption that MU-MIMO spatial streams should share the same network bandwidth, we have:  
\[-(2-m_{l_{1}tf}-m_{l_{2}tf})\leq z_{l_{1}tfk}-z_{l_{2}tfk}\leq(2 -m_{l_{1}tf}-m_{l_{2}tf}),\\ \forall i,l_{1}\in L_{i}^{\text{out}}\cup L_{i}^{\text{in}},l_{2} \in L_{i}^{\text{out}}\cup L_{i}^{\text{in}},t,f,k \tag{40}\]  
where \(l_{1}\) and \(l_{2}\) represent different links belonging to the same link set (i.e., \(L_{i}^{\text{out}}\) and \(L_{i}^{\text{in}}\)).
# Laser Inter-Satellite Link Visibility and Topology Optimization for Mega Constellation
## 3 Methodology and Analytical Approach
### Initial Phase  
After the satellites are assigned their numbers, their spatial position information can be visually observed and analyzed, as shown in Figure 3, a schematic diagram of the distribution of satellites in the Delta constellation. At this time, the satellites in three different orbital planes are all climbing at speed \(\
u\) in the ascending orbital segment. Taking \(S(1,1)\) as the initial position, assuming that the constellation is uniform, and the inclination of all orbital planes in the constellation relative to the equator is the same, the initial phase of any satellite \(M_{j}\) on any orbit \(P_{i}\) in the constellation \(\tau\) is shown in Equation (1).  
\[\tau=\left(-2\pi\times F\times\left(P_{i}-1\right)\right)/S+\left(2\pi\times \left(M_{j}-1\right)\right)/M \tag{1}\]  
where \(\Delta\sigma\) is the phase difference of adjacent satellites in the same orbital plane; \(\Delta\Omega\) is the phase difference of the ascending intersection point of two adjacent orbital planes; and \(\Delta\omega\)  
Figure 2: This is the schematic diagram of heterodyne LISLs. (a) Adjacent and nearby OP LISLs; (b) Crossing OP LISLs.  
is the phase difference between two neighboring satellites in adjacent orbital planes, as shown in Equations (2)-(4), respectively.  
\[\Delta\sigma=2\pi/M \tag{2}\] \[\Delta\Omega=2\pi/P\] (3) \[\Delta\omega=2\pi\times F/S \tag{4}\]
## 5 Experimental Results
### Security Analysis
#### 5.6.2 Confidentiality  
The protocol guarantees the confidentiality of shared symmetric keys derived through the D-H key exchange mechanism. Even if attackers intercept the public keys ( \(PK_{A}\) and \(PK_{B}\)), the difficulty of solving the discrete logarithm problem (DLP) ensures that the private keys (\(a\) and \(b\)) and the derived shared key remain secure. This ensures the confidentiality of key exchange communications, protecting them from unauthorized access.
## 4 Numerical Simulation
### Material Contents of the Model
#### 4.3.2 Poisson's Ratio  
According to Equation (5), the tensor of elasticity moduli [c] is dependent also on the Poisson's ratio; therefore, the precise value of [PERSON]'s ratio of the 1:1:2 mixture is necessary to have an accurate simulation. The Poisson's ratio of the four silicone mixtures was investigated in the previous studies by tensile test. [PERSON] [45] measured the Poisson's ratio of the 1:1:2 mixture by static setup in the range of 0.29 to 0.40 with 0.35 average. [PERSON] et al. [54] determined the [PERSON]'s ratio 0.40 for the 1:1:2 mixture by tensile test. Since the silicone sample was excited by an oscillating pressure in our experimental setup, the static test results seem to be improper for our application. [PERSON] et al. [54] and [PERSON] et al. [55] measured the Poisson's ratio close to 0.5 using harmonic excitation. The numerical simulation performed with different [PERSON]'s ratios within the range of 0.399-0.499 extracted from [45; 54; 55], which can be assumed to be nearly constant over the frequency range. Since the simulated displacement reveals a high dependency on the Poisson's ratio, we set the Poisson's ratio of the model 0.499 of which the simulated and measured displacement (starred red line) was matched better, which will be discussed in Section 5.2.5.
# Seven-Year Retrospective Study on Conometric Retention for Complete Fixed Prosthesis
## 4 Discussion
Nevertheless, this marginal bone resorption over time can be explained as part of the physiological bone remodeling occurring after implant placement and is compatible with peri-implant tissue health [25; 26]. Additionally, subcrestal implant placement has been associated with higher MBL over time [27]. In this study, all the implants were placed 2 mm subcrestally, and this could explain the low mean marginal bone loss after seven years. The stability of peri-implant tissues over time might also be related to the loading type. Immediate load requires less abutment dis- and re-connections, minimizing the trauma to peri-implant hard and soft tissues, and therefore improving their stability over time [28; 29]. Furthermore, when the final prosthesis is immediately delivered, it allows the overall treatment time and costs to be reduced, with great advantage both for patients and clinicians [28; 30]. Even if immediate implant load has traditionally been considered a risky procedure for implant osseointegration [31], it has been proven to be a reliable and reproducible option for the rehabilitation of completely edentulous mandible [32]. In the present study, all the implants were immediately loaded, and seven years after, no implant failed. This result is in line with the extremely high implant success rate (94.4-100%) reported in the literature [32]. Implant supported overdentures have been associated with a higher risk of implant failure compared to fixed full-arch rehabilitations [33; 34]. This might be related to the better mechanical stability of fixed prosthesis and better stress distribution between the implants. In the present study, no implant failure occurred during the 7-year follow-up period. Conometric retention provide a good force distribution between the four parallel implants, even with a reduced soft tissue support of the prostheses. Therefore, it can be postulated that this solution allows the prostheses to behave more like a fixed than a removable implant supported solution in terms of implant survival rate. On the other hand, four prostheses frameworks fractures were observed during the follow-up period (17.4%), with a prosthetic success rate of 82.6%. This finding is in line with the 12% framework fracture rate reported by previous studies for implant supported overdentures [20; 35]. It must be noted that all the fractures occurred at the distal cantilever portion, and it was always possible to restore the prostheses. Extended cantilever has been reported as a risk factor for prosthetic complications, and therefore should always be avoided [20].
## Nomenclature  
\begin{tabular}{p{142.3 pt} p{142.3 pt}}
**Abbreviation** & **Description** \\ AEBS & Autonomous Emergency Braking System \\ EBS & Emergency Braking System \\ AEB & Autonomous Emergency Braking \\ FCW & Forward Collision Warning \\ ECU & Electronic Control Unit \\ MPC & Model Predictive Control \\ SMC & Sliding Mode Control \\ TTC & Time-To-Collision \\ SDA & Stopping Distance-based Algorithms \\ CWS & Collision Warning System \\ ANN & Artificial Neural Network \\ PID & Proportional Derivative and Integral \\ BP & Back Proportion \\ PB & Partial Braking \\ FB & Full Braking \\ PI & Proportional Integral \\ ST-SMC & Super Twisting Sliding Mode Controller \\
**Symbol** & **Description** \\ \(T_{ttc}\) & Time to collision \\ h & Distance between the preceding and following vehicles \\ L & Length of the preceding vehicle \\ V\({}_{\rm P}\) & Speed of the preceding vehicles \\ V\({}_{\rm F}\) & Speed of the following vehicles \\ \(\
u_{ego}\) & Ego vehicle's velocity \\ \(d_{driver}\), \(u_{brake}\) & Ego vehicle's deceleration \\ \(a_{p1}\), \(a_{p2g}\), \(a_{FB}\) & Deceleration of both the first and second phases of PB and FB \\ \(T_{stop}\) & Stopping time is the period to stop the vehicle from first deceleration \\ \(x_{relative}\) & Distance between ego and leading vehicle \\ \(\
u_{relative}\) & The relative velocity of the lead vehicle toward the ego vehicle \\ \(T_{FCW}\) & The time period for stopping from a warning to fully stopping \\ \(T_{react}\) & Delay time for the reaction of the driver during the FCW \\ \(T_{PB1}\), \(T_{PB2}\), \(T_{FB}\) & Time period to stop the vehicle during first PB and second PB and FB \\ \(U\) & The boundary of the control value \\ \(\sigma_{0}\) & Boundary layer surrounding the sliding surface \\ \(\sigma\) & Sliding Surface \\ \(W\), \(\lambda\) and \(\rho\) & Control gains \\ \(z(k)\) & Measured data at time step k \\ \(z^{v}(k)\) & The measured value of the vision \\ \(z^{R}(k)\) & The measured value of the radar \\ \(z^{L}(k)\) & The measured value of the lidar \\ \(y\) & The distances of each sensor \\ \(x\) & Longitudinal distance \\ \(\dot{x}\), \(\dot{y}\) & Leading vehicle relative velocity \\ \(p\), \(d\), and \(r\) & Number of objects the sensors have identified \\ \(\dot{y}_{k}\) & The lateral velocity of vision \\ \(\dot{\rho}_{K}^{V}\) & The azimuth angle of vision \\ \(\dot{z}_{k}^{LR}\) & The longitudinal velocity of the radar and lidar \\ \(\dot{x}_{k}^{LR}\) & Longitudinal distance of the radar and lidar \\ \(\dot{x}_{k}^{VLR}\) & The fusion track \\ \end{tabular}
# Efficient Multi-Identity Full Homomorphic Encryption Scheme on Lattice
## 4 Identity-Based Full Homomorphic Encryption Scheme
### Homomorphic Property  
**Definition 8**.: _Let \(\mathbf{C}\in\mathbb{Z}_{q}^{m^{\prime}\times N}\) be the ciphertext matrix corresponding to plaintext \(\mu\) of the identity id, and the secret key is \(\mathbf{s}\in\mathbb{Z}_{q}^{m^{\prime}}\). If \(\mathbf{s}^{\mathsf{T}}\mathbf{C}=\mu\mathbf{s}^{\mathsf{T}}\mathbf{M}+\mathbf{e}\) where \(\|\mathbf{e}\|_{\infty}\leq\beta\), \(\mathbf{C}\) is called the \(\beta\)-noise ciphertext of plaintext \(\mu\)._  
Proof.: Let \(\mathbf{C}_{1}\) and \(\mathbf{C}_{2}\) be the ciphertexts of identity \(id\) corresponding to plaintexts \(\mu_{1}\) and \(\mu_{2}\) respectively, namely \(\mathbf{s}^{\mathsf{T}}\mathbf{C}_{1}=\mu_{1}\mathbf{s}^{\mathsf{T}}\mathbf{M}+\mathbf{e}_{1}\), \(\mathbf{s}^{\mathsf{T}}\mathbf{C}_{2}=\mu_{2}\mathbf{s}^{\mathsf{T}}\mathbf{M}+\mathbf{e}_{2}\), where \(\mu_{1},\mu_{2}\in\{0,1\}\), \(\|\mathbf{e}_{1}\|_{\infty}\leq\beta_{1}\), \(\|\mathbf{e}_{2}\|_{\infty}\leq\beta_{2}\).  
(1) \(\quad\)Homomorphic addition: \(\mathbf{C}_{Add}=\mathbf{C}_{1}+\mathbf{C}_{2}\), satisfy \(\mathbf{s}^{\mathsf{T}}\mathbf{C}_{Add}=\mathbf{s}^{\mathsf{T}}(\mathbf{C}_{1}+\mathbf{C}_{2})=(\mathbf{u }_{1}+\mu_{2})\mathbf{s}^{\mathsf{T}}\mathbf{M}+\mathbf{e}^{+}\), where \(\mathbf{e}^{+}=\mathbf{e}_{1}+\mathbf{e}_{2}\). Obviously \(\mathbf{C}_{Add}\) is \(\beta_{1}+\beta_{2}\) noise ciphertext, that is, after one-time homomorphic addition, the error increases by two times the factor.  
(2) \(\quad\)Homomorphic multiplication: \(\mathbf{C}_{Mult}=\mathbf{C}_{1}\mathbf{M}^{-1}(\mathbf{C}_{2})\), satisfy \(\mathbf{s}^{\mathsf{T}}\mathbf{C}_{Mult}=\mathbf{s}^{\mathsf{T}}\mathbf{C}_{1}\mathbf{M}^{-1}(\mathbf{C}_ {2})=(\mu_{1}\mu_{2})\mathbf{s}^{\mathsf{T}}\mathbf{M}+\mathbf{e}^{\times}\), where \(\mathbf{e}^{\times}=\mathbf{e}_{1}\mathbf{M}^{-1}(\mathbf{C}_{2})+\mu_{1}\mathbf{e}_{2}\). Obviously \(\parallel\mathbf{e}^{\times}\parallel_{\infty}\leq\left(\sqrt{N}\beta_{1}+\beta_{2}\right)\), \(\mathbf{C}_{Mult}\) is \(\left(\sqrt{N}\beta_{1}+\beta_{2}\right)\) noise ciphertext. The same calculation is also applicable to NAND gates. \(\square\)
# Automatic Failure Modes and Effects Analysis of an Electronic Fuel Injection Model
## 3 Automatic FMEA System
### The Function of Automatic FMEA
#### 3.2.3 Rule-Based Failure Determination Function  
Automatic FMEA uses rule-based engines to analyze the result of the fault injection simulation and determine the severity of the failure effects for the given failure mode. Automatic FMEA determines the effect of a failure mode by comparing the results of a golden run simulation to that of the fault simulation. In addition, it also examines the value, state, and condition of the parameter of the fault simulation.  
The rules in the automatic FMEA are designed to support the fault models and the fault injection block shown in Figure 5. Each rule evaluates the failure mode and the value of the parameter. First, the condition part of the rules is designed to identify the failure mode of the current simulation, including (a) the underestimation values check for system degradation, (b) the overestimation values check for system overload, (c) the error margin check for the stability of a performance parameter, or (d) the failure event check for the detection of the specific failure effect.  
Next, the action part of the rules is used to determine the value of the entries of the severity, occurrence, and controllability in the FMEA table. For the given failure mode, the automatic FMEA performs a large number of fault injection simulations to find the behavior of the failure effect. Thus, we can compare the distribution of the parameters from the current simulation runs to that of the golden run simulation so that the action part of the rule can assign the resulting severity between 0 and 10. The occurrence and detection entries of the FMEA table are not yet implemented, so it is necessary to have a meeting with the domain experts and the designers of the model. This process of identification of the severity, occurrence, and detectability of the failure effects is summarized in Figure 6.
## 3 Detailed Analysis of Maximum Power Point Tracking (MPPT) Methods
### Conventional MPPT Method  
Conventional MPPT methods have been used in PV systems for many years as simple and effective algorithms. These methods optimize the operating point of the PV system based on direct measurements of environmental conditions.  
* Simple and cost-effective.
* Low hardware requirements.  
* May become unstable under rapidly changing environmental conditions.
* Can miss the MPP.
* A trade-off between the tracking speed and steady-state oscillation is required.
## 5 Evolution of Opportunistic Routing in Asynchronous WSN
### Evolution of Asynchronous Duty-Cycled MAC Protocols
#### 5.1.3 X-Mac  
X-MAC [30] is a design for asynchronous duty-cycled WSNs. The main feature of this protocol is that its preambles are shortened in order to reduce both the sender's and the receiver's waiting time. This mechanism reduces energy consumption for both the transmitter and the receiver. Small preambles are sent with a short pause so that the intended receiver can send an ACK reply in between, which can notify the sender to send data packets instead of more preambles. Because of preamble-ACK,  
Figure 6: Packet transmission in Wise-MAC [29].  
the per-hop latency is reduced. X-MAC offers additional advantages such as flexible adaptation to both burst and periodic sensor data sources.  
The packet transmission procedure in X-MAC is shown in Figure 7. Node A has a packet to send, and so it wakes up and sends short preambles with a pause between them. In each pause, node A waits for an ACK from receiver B. After preamble data is received by B, B will send a preamble-ACK. After receiving the preamble-ACK, A will stop sending the preamble and send the data packet instantly.  
However, in a multi-hop network, if multiple senders send preambles at the same time, and all of them are out of each other's transmission range, only having the same receiver will cause a hidden node problem. Then collision will occur at the receiver's end, and it will be unable to send preamble-ACK, which will increase the sender's waiting time and energy consumption as well.
# Spatial Assessment and Prediction of Urbanization in Maseru Using Earth Observation Data
## 2 Materials and Methods
### Future Urban Growth Simulation  
The multi-criteria evaluation (MCE), weights of evidence (WOE), artificial neural network (ANN), and logistic regression (LR) algorithm models were incorporated into the MOLUSCE plugin for potential transition modeling [43]. In this study, we applied the ANN for potential transition modeling, followed by the cellular automata (CA) for the LULC simulation. The ANN determines the transition probability of LULC using numerous output neurons in simulating LULC changes [44]. The training procedure in ANN is a process in which the connection weights are iteratively modified to accomplish a specific task [45]. This training process is called supervised because the desired outputs and all the input parameters are specified for each example [46]. The ANN represents a set of inputs that is a real-valued vector \([x_{1,...}x_{n}]\). The error is the difference between the output results and the desired output desired by the ANN [46]. The back-propagation algorithm is applied after the training process to minimize the network error function by adjusting the weight values [47]. The error function is calculated using the following equation:  
\[E=\frac{1}{2}\underset{j-1}{\sum}^{L}\left(d_{j}-o_{j}\right)^{2} \tag{6}\]  
where \(L\) represents the number of nodes in the later output and \(d_{j}\) and \(o_{j}\) represents the desired output and current response of node \(j\), respectively [48]. In an iterative or repetitive approach, the corrections made to the weight parameters are computed and added to the previous values as follows:  
\[\begin{cases}\Delta w_{i,j}=\eta\frac{\partial E}{\partial w_{i,j}}\\ \Delta w_{i,j}(t+1)=\Delta w_{i,j}+\alpha\Delta w_{i,j}(t)\end{cases} \tag{7}\]  
where the weight parameter between nodes \(i\) and \(j\) is represented by \(w_{i,j}\), \(\eta\) represents the positive constant that controls the amount of adjustment, \(t\) is the number of iterations and \(\alpha\) denotes the momentum factor, which takes on values in the range of 0 and 1. This parameter is also known as the stabilizing factor because it smoothes the rapid changes between weights [47].  
We used the MOLUSCE model to simulate urban expansion in the study area for 31 years, from 2019 to 2050. The CA is a cell-based model that extrapolates historical and current LULC for the prediction of future changes using transition rules that are based on the status of the cells and their neighbors [41]. The state of each cell in the future is predictedbased on the historical state of the neighboring cells [16]. The performance of the model was validated to determine the correctness level. The classified or reference map was compared with the simulated map to determine the level of agreement using the validation table. A model of accuracy was presented using the kappa location, overall kappa, percentage correctness and kappa histogram. The ANN and CA approaches were used because they produced good results in the modeling and prediction of LULC changes [24; 49; 50].