esa-sceva/satcom-chunk-collection huggingface dataset:
# Mechanical Properties of Disconnectable Coupling Joints for Steel Bracing under Eccentric Load
## 3 Response Feature under Eccentric Load
### Results of Numerical Simulation
#### 3.2.2 Stress Distribution  
The von Mises stress nephogram of the model is shown in Figure 9. For the entire model, the stress is mainly concentrated on the side where the eccentric load is applied, as exhibited in Figure 9a,b. The stress of the active part is mainly concentrated on the bottom of the middle-rib plate, that is, the contact position with the steel wedge. When the eccentric load increases to 3360 kN, the material on the loaded side of the middle-rib plate begins to yield. At the same time, due to the rotation of the active part, the material on the pressure side of the channel steel begins to yield, as shown in Figure 9c. When the load increases to 5000 kN, the material on the compression and tension side of the channel steel is close to the ultimate strength, as shown in Figure 9d. The channel steel nested in the fixed part has a tendency to move in the negative direction of the \(y\)-axis due to the effect of eccentric load. Therefore, when the load increases to 5000 kN, the stress value of the nested part is about 209.3 MPa, which does not reach the yield strength, as shown in Figure 9d. In the numerical model, the yield strength of steel is 235 MPa, and plastic damage is identified to occur when the stress of steel exceeds 235 MPa. The material of almost the entire steel wedge yielded at the end of loading. Meanwhile, more than half of the material of the steel wedges have reached ultimate strength, as shown in Figure 9e. Therefore, the selection of high-strength steel for processing steel wedges can significantly improve the mechanical properties of DC joints. The excessive stress of the fixed part is concentrated in the middle plate, especially the side where the eccentric load is applied. At the end of the simulation, the maximum stress value of the middle plate is 362 MPa, which is close to the ultimate strength of the material. At the same time, due to the effect of the channel steel of the active part, the stress value of the stiffener is relatively large. In addition, the stress value of the steel tube on the loading side is larger; the maximum value is about 213 MPa. The stress concentration area is symmetrically distributed along the middle plate. The closer to the flange, the greater the stress, as listed in Figure 9f. On the opposite side of the steel tube of the fixed part, the stress is mainly concentrated at the  
Figure 8: Deformation diagram in vertical direction.  
junction of the steel tube and the flange. This is caused by the tensile stress of the steel tube on this side. The stress concentration area is also symmetrically distributed along the middle plate. The maximum stress value is about 121 MPa, as shown in Figure 9.
## 4 Results and Analysis
### Evaluation of Hybrid Encryption Algorithm Strength  
The assessment of encryption strength involved an approach that encompassed several advanced analytical techniques. This included entropy analysis to measure the randomness and unpredictability of encrypted data; pattern analysis to scrutinize recurring sequences or structures within the encrypted content; differential cryptanalysis, which is a method for assessing encryption resistance against chosen plaintext attacks; and linear cryptanalysis to examine the susceptibility of encryption algorithms to linear approximations.
## 5 Experiments and Analyses
### Performance of Nodes on Entering/Leaving the System  
The fifth experiment estimated the SePCS's system performance when different numbers of nodes enter or leave the system. The parameters used were the same as those listed in Table 11. However, the experiment duration was 110 s, packet rate = 6 requests/s, and the scenario was that one node left the wireless system at the 20 th s and came back at the 30 th s, two nodes left the system at the 50 th s and returned at the 60 th s, and three nodes went away at the 80 th s and re-entered the system at the 90 th s. The purpose was to simulate the handover behavior of nodes [24].  
The experimental results are illustrated in Figure 14, in which Figure 14a illustrates the drop rates. When many more nodes dynamically left the system, packets sent to the nodes could not reach their destination, thus raising the drop rates. Figure 14b shows the throughputs. When one node (three nodes) left and came back again, the throughputs were the highest (lowest), because if many more nodes can serve user requests, the throughputs will be higher. Figure 14c shows the throughput distributions on different time points. When more nodes left the system, the throughputs were much lower. In fact, throughputs at time durations 81-90, 51-60 and 21-30 were respectively 123, 147 and 181. Owing to higher drop rates, the Random's and FCFS's throughputs were lower than those of the other two algorithms.  
Figure 14: Performance of the cases in which nodes leave and re-enter the tested systems.  
(**a**) Drop rates; (**b**) Throughputs; (**c**) Throughputs against time durations.
## 4 Localization Algorithm
### EKF-Based Localization  
The host vehicle is described with the position of points \(x\) and \(y\) and heading angle \(\psi\) in the global frame. The state vector, \(X\), is expressed by  
\[X=\left[\begin{array}{cc}x&y&\psi\end{array}\right]^{T} \tag{5}\]  
The basic framework for the EKF includes an evaluation of the states of the discretized nonlinear dynamic system, which can be derived as  
\[\begin{array}{c}X_{k}=f(X_{k-1},U_{k})+w_{k}\\ Z_{lme,k}=h(X_{k-1})+v_{lme,k}\\ Z_{gautnall,k}=h(X_{k-1})+v_{gautnall,k}\end{array} \tag{6}\]  
where \(w_{k}\) is the process noise associated with proprioceptive sensors that is assumed to have a zero mean and a Gaussian distribution; \(v_{lme,k}\) and \(v_{gautnall,k}\) are the measurement noises of lane and guardrail matching. \(U_{k}\) indicates the external input (longitudinal speed and yaw rate) and \(Z_{lme,k}\) and \(Z_{gautnall,k}\) are the position corrections by each matching method.  
Velocity and yaw-rate are approximately fixed during the sampling time period by integrating the Euler approximation and assuming that the control signals of the nominal discrete process model equations are derived as  
\[\overline{X}_{k|k-1}=\left[\begin{array}{c}\hat{x}_{k-1|k-1}+v_{k}\Delta t \cdot\cos(\hat{\psi}_{k-1|k-1}+\Delta t\cdot\hat{\psi}_{k})\\ \hat{y}_{k-1|k-1}+v_{k}\cdot\Delta t\cdot\sin(\hat{\psi}_{k-1|k-1}+\Delta t \cdot\hat{\psi}_{k})\\ \hat{\psi}_{k-1|k-1}+\Delta t\cdot\hat{\psi}_{k}\end{array}\right] \tag{7}\]  
where Q indicates the covariance matrix with regard to the noise of the vehicle sensor. The position of the ego-vehicle can be corrected in regular order if the corresponding matching result is valid by a measurement update, according to the following steps:  
\[\begin{array}{c}\overline{X}_{k|k}=\overline{X}_{k|k-1}+K_{k}\left(Z_{lme, k}-H\cdot\overline{X}_{k|k-1}\right)\\ \overline{X}_{k|k}=\overline{X}_{k|k}+K_{k}\left(Z_{gautnall,k}-H\cdot\overline {X}_{k|k}\right)\\ K_{k}=P_{k|k-1}H^{T}\cdot S_{k}^{-1}\\ S_{k}=HP_{k|k-1}H^{T}+R_{k}\end{array}\text{The time update rate is the same as the frequency of the vehicle sensor, 100 Hz, and that of the measurement update is also the same as the vision (10 Hz) and radar (20 Hz) sampling rates.
# Pipe Routing with Topology Control for Decentralized and Autonomous UAV Networks +
## 4 Pipe Routing Scheme  
Figure 3 shows the modules of the Pipe and TC-Pipe routing schemes. To provide strong network connectivity during fast area coverage, each UAV determines its next waypoint using the pheromone-based BS-CAP mobility model (see Section 3 for details). When a UAV detects a target, it broadcasts RREQ packets to establish a route to the BS. From the received RREPs, the target UAV selects a long-lasting, less congested, and energy-sufficient route using a two-step route selection mechanism (discussed in Section 4.2) based on metrics, such as \(HC\), \(IL\), \(RLT\), and node energy (see details in Section 4.1).  
To reduce frequent route discoveries in dynamic networks, the target UAV collects 2-hop neighborhood information along the route, called the \"pipe\" (see Section 4.3 for details). This enables the target UAV to proactively switch to an alternative route within the pipe (if available) or trigger a new route discovery, when the quality of the current route degrades below a threshold (see details in Section 4.4). As a result, the number of route discoveries decreases in the Pipe routing scheme, which improves flow throughput, control (signaling) overhead, and delay.  
Despite maintaining high network connectivity, the pipe can experience thinning, where nodes are unevenly distributed, leading to a lack of alternative high-quality routes, as discussed in Section 5.1. To address this issue, our TC-Pipe routing scheme integrates a topology control mechanism that evaluates pipe quality and provides feedback (in terms of a pheromone mask) to the BS-CAP mobility model (see Section 5.2 for details). This feedback attracts neighboring UAVs, which improves the distribution of nodes along the route. As a result, the number of alternative high-quality routes within the pipe increases, which reduces the number of route discoveries and improves flow throughput, delay, and overhead, as demonstrated by experiments for different network and traffic settings in Section 8.  
The modules of the Pipe routing scheme are explained below.
# Influence of Monolith Length on Temperature Field of Concrete Gravity Dams
###### Abstract  
This paper examines the influence of monolith length on the temperature field of concrete gravity dams built using the block method. The developed 3D model is capable of conducting a thermal analysis of a 95.0 m high concrete gravity dam built using the block method, where each newly cast block represents a new analysis phase. The calculation accounts for the period of construction, the filling of the reservoir, and the service for a total duration of about 5 years. The thermal properties of the material, the influence of cement hydration heat, the temperature of the surrounding rock mass, the temperature of the fresh concrete mixture, and the corresponding boundary conditions defining a heat transfer were taken into account. The height and width of the blocks, as well as the sequence of concreting, were considered invariable, while the length of the blocks (dimension in the direction of the dam's axis equal to the monolith length) varied, with values of 10.0, 12.5, 15.0, and 20.0 m. The obtained calculation results for the control nodes showed that the maximum reduction in the monolith length (from 20.0 m to 10.0 m) caused a decrease in the maximum temperature values of the concrete (from 1.6 to 3.4 degC, depending on the control node). Also, the results showed that, by reducing the length of the monolith, there was a delay in the moment at which the maximum temperature values of the concrete appeared in the selected control node. The delay in reaching the maximum, in relation to the 10.0 m long monolith, was from 7 days (for points on the crest dam) to 49 days (for points in the central zone of the monolith) for the other considered monolith lengths. The above indicates the importance of concrete temperature control for longer monoliths, especially during construction in extreme air temperatures. The main contribution of the conducted analysis is the development of insight into temperature field changes depending on monolith length, which can help engineers during the design and construction of new, as well as the maintenance of existing, dams.  
concrete gravity dam; phased thermal analysis; initial and boundary conditions; temperature field; blocks; monolith length +
Footnote †: journal: Applied  
1
# Long-Term Analysis of Wave Climate and Shoreline Change along the Gulf of California
## 4 Results
### Trend and Multi-Year Mean of Wave Climate  
The multi-year mean of \(H_{99}\) showed the highest values at C2 (\(H_{99}\)* = 2.38 m), followed in descending order by C3 (\(H_{99}\)* = 2.37 m) and then by C1 (\(H_{99}\)* = 2.33 m), although the difference between the highest and lowest values was less than 2.2% (Figure 5a). On the other side, the multi-year mean values of \(H_{s}\), \(T_{s}\) and \(P\) at the three sites (Figure 5b-d) showed the highest parametric values at C1 (\(H_{s}\)* = 0.88 m, \(T_{s}\)* = 7.78 s and \(P\)* = 3.19 kW/m), which gradually decreased to the north, with \(H_{s}\)* = 0.62 m, \(T_{s}\)* = 4.49 s and \(P\)* = 1.33 kW/m at C2, and \(H_{s}\)* = 0.54 m, \(T_{s}\)* = 3.32 s and \(P\)* = 1.05 kW/m at C3.  
Furthermore, the annual mean of \(H_{99}\) showed upward trends of 0.0099 m (0.99%), 0.0175 m (1.75%) and 0.0034 m (0.34%) at C1, C2 and C3, respectively (Figure 6a). Additionally, the annual mean of \(H_{s}\) showed upward trends at C1, C2 and C3 of 0.0009 m (0.09%), 0.0022 m (0.22%) and 0.0010 m (0.10%) per year, respectively (Figure 6b). The annual mean of \(T_{s}\) showed an upward trend at C3 of 0.0028 s (0.28%) per year, and downward trends at C1 and C2 of \(-\)0.0096 s (\(-\)0.96%) and \(-\)0.0010 s (\(-\)0.10%) per year, respectively (Figure 6c). Nonetheless, the annual mean of \(P\) showed upward trends at these three sites: 0.0063 kW/m (0.63%) per year at C1, 0.0139 kW/m (1.39%) per year at C2 and 0.0045 kW/m (0.45%) per year at C3 (Figure 6d). The time series of the annual means of \(H_{99}\), \(H_{s}\), \(T_{s}\) and \(P\) at all the sites showed statistically significant trends at a 90% accuracy level.
# Structural Health Monitoring for Prefabricated Building Envelope under Stress Tests
## 3 Results and Test Analysis
### FBG Sensors  
FBGs for temperature--Figure 26 displays the results obtained by the FBG sensor for temperature (Channel 4) associated with the InComEss architecture. A noticeable increase in temperature was recorded during the first day of testing, indicating the correct procedure for data collection. The markable timestamps are as follows: 11.50 h--temperature decrease recorded during the air exfiltration test (\(-600\) Pa) (i); 12.10 h--temperature increase of 0.5 degrees can be observed during the test since a heat lamp was placed near the temperature sensor (ii); 16.30 h--rapid temperature increases from 16 to 18 degrees due to the rain test likely due to the water temperature (iii).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Channel**} & \multicolumn{2}{c}{**Tightness at the Beginning of**} & \multicolumn{2}{c}{**Tightness at the End of**} & \multirow{2}{*}{**Delta**} \\  & **the Test on Day 2** & & **the Test on Day 2** & \\ \hline
1 & 0.4 & \(-0.6\) & 0.64 \\ \hline
2 & \(-0.06\) & \(-1.3\) & 1.2 \\ \hline
5 & 0.02 & \(-0.8\) & 0.82 \\ \hline
6 & \(-0.08\) & \(-1.02\) & 0.94 \\ \hline
7 & \(-0.004\) & \(-1.03\) & 1.026 \\ \hline
8 & 0.04 & \(-0.4\) &The temperature on this test day was much more stable throughout the second day, as recorded by the temperature sensor reported in Figure 27.  
In this paragraph, the FBG for strain's test results are reported. In Figure 28, the strain results obtained by the cable applied on the glass panel during the first day can be observed. The test started with air infiltration followed by exfiltration, repeated twice. The glass surface reached negative values of strain during the increasing pressure and positive ones during the negative pressure. Indeed, the FBG sensors in the first case were compressed, while in the second, they were expanded.  
The strain levels on channel 5 (the cable on the aluminum profile) are minor because the sensors are applied on the aluminum profile where the facade strain due to deformation is contained (Figure 29).  
The highest strain levels were measured at the end of the second day in channel 3 (z) when the wind pressure and depressure were executed; indeed, in less than 5\({}^{\prime\prime}\), the facade was stressed from 0 to +2625 Pa, and from 0 to \(-3000\) Pa (Figures 30 and 31).  
Figure 31 displays the result obtained for channel 5 (x, y) during day 2.  
Figure 28: Day 1 FBG for strain—Channel 3.  
Figure 27: Day 2 FBG for temperature—Channel 4.  
Figure 32 reports the result obtained for channel 2 during day 2. Strain results obtained from channel 2 (z direction monitoring) are higher than channel 5 (x, y). This can clearly be observed in the rain--dynamic test, which executes from 11:50 h to 12:20 h, and the rain--test static, which executes from 16:20 h to 17:20 h, where the strain level is raised in steps following the pressure curve increasing(+600 Pa).  
Figure 31: Day 2 FBG for strain—Channel 5.  
Figure 29: Day 1 FBG for strain—Channel 5.
###### Abstract  
We examine the effects of imperfect phase estimation of a reference signal on the bit error rate and mutual information over a communication channel influenced by fading and thermal noise. The Two-Wave Diffuse-Power (TWDP) model is utilized for statistical characterization of propagation environment where there are two dominant line-of-sight components together with diffuse ones. We derive novel analytical expression of the Fourier series for probability density function arising from the composite received signal phase. Further, the expression for the bit error rate is presented and numerically evaluated. We develop efficient analytical, numerical and simulation methods for estimating the value of the error floor and identifying the range of acceptable signal-to-noise ratio (SNR) values in cases when the floor is present during the detection of multilevel phase-shift keying (PSK) signals. In addition, we use Monte Carlo simulations in order to evaluate the mutual information for modulation orders two, four and eight, and identify its dependence on receiver hardware imperfections under the given channel conditions. Our results expose direct correspondence between bit error rate and mutual information value on one side, and the parameters of TWDP channel, SNR and phase noise standard deviation on the other side. The results illustrate that the error floor values are strongly influenced by the phase noise when signals propagate over a TWDP channel. In addition, the phase noise considerably affects the mutual information.  
fading channel; error probability; wireless communications; simulations +
Footnote †: journal: _Entropy_  
0023
## 4 Discussion
The hip joint coordinated other distal joints to ensure accurate footstep positions, particularly during the terminal swing. Because of the essential roles, the hip joints of both limbs decomposed the movement and coordinated with the knee joints, which significantly increased the DI, which was in agreement with existing studies that the amputees predominantly regulated the hip joints for balance and walking stability [16; 34; 35]. The amputees needed to utilize and coordinate the ankle joint of the non-amputated side at a comparatively lower flexibility than the able-bodied controls, particularly during the midstance and terminal stance. [PERSON] et al. [25] studied patients with cerebellar ataxia and concluded that reducing the speed of movement and adopting decomposition moments can increase the accuracy of movement, which is a manifestation of compensatory gait. Increasing the knee-ankle DI of the non-amputated limb can yield a stable and accurate gait at the cost of the walking speed. Different prosthetic designs can have different effects on the inter-joint coordination of the amputated limb, resulting in a higher DP for the amputee groups. In this study, the prostheses were not identical. Amputees wore their own daily used prostheses, walking at their preferred comfortable speeds. The knee-ankle DP and hip-ankle DP of the amputated limbs were 9.1\({}^{\circ}\) and 6.2\({}^{\circ}\) higher than those of the controls in the stance phase, respectively. Different prosthetic joints can have different characteristics and mechanisms. Solid ankle cushion heel (SACH), multi-axis ankle-foot, and energy-storing-and-returning (ESR) feet were used in this study. The SACH foot consisted of a rigid ankle joint, while the multi-axis ankle-foot allowed rotation in multiple directions. The ESR foot did not contain a real ankle joint, but the functions were partially replicated by the deformation of the material. The different mechanisms may lead to variations in the joint angle [33] and angular velocity. This may have caused differences in the knee-ankle and hip-ankle CRPs among the amputees. Thus, the amputee groups exhibited higher DPs (which indicated the variation among subjects) than the control group. This study had limitations. First, all the participants were male. Second, inter-joint coordination was investigated only in the sagittal plane. Information on the coronal and transverse planes could be essential to establish a more complete inter-joint coordination profile for amputees. Moreover, the walking speed was not controlled, because amputees may have adopted their natural gait at different walking speeds. The amputees walked slower than the able-bodied controls in our experiment, which may have contributed to covariances. In addition to the inter-joint coordination, the interaction between the asymmetry and the variability can be investigated as a manifestation of gait instability [36]. Additionally, it would be interesting to know whether wearables with feedback installed on the prosthesis can improve the gait symmetry and inter-joint coordination [37; 38; 39].
## 2 Related Work
### State of the Art of Research Works
Therefore they propose an integrated SDN/NFV management and orchestration architecture which is specifically conceived for the dynamic deployment of Virtual Tenant Networks and the related SDN Controllers (implemented as VNF in DCs). However, they do not address the service chaining problem, as this works does. [PERSON] _et al._[34] propose the CloudNFV Platform for enabling a telecom operator to deploy and manage service functions in a distributed cloud infrastructure. Although they focus on service chaining, their work differs from our contribution in that the proposed architecture specifically reflects the software design of their prototype (_i.e._, instead of providing general functional specifications, some functional blocks are defined in terms of software implementation OpenStack and OpenDayLight). Moreover, they do not explicitly refer to service oriented principles and architectural guidelines. On the other side, [PERSON] _et al._[35] stress the importance of a description model for network service. Although they do not explicitly refer to SOA guidelines, this approach is in principle compliant with those guidelines. However, their contribution consists in a straw man model for service descriptions, not on architectural frameworks. As regards the second area of research we identified several authors focusing on the problem of VNF placement, _i.e._, the (sub)optimal placing of a set of virtual network functions on a network of physical nodes to serve a set of service chain requests while optimizing a certain utility function, for instance to minimize the number of utilized servers [36; 37] or to apply load balancing policies [38]. In [39] the authors provide a formalization of the VNF scheduling problem, _i.e._, finding the corresponding time slots for functions to be executed over a given set of machines. Other authors [40] focused on the routing problem, _i.e._, assigning paths to the incoming traffic flow requests in order to connect nodes running virtual functions. Ultimately, such works focused on specific issues of service chaining, proposing focused algorithms and techniques, but, to the best of our knowledge none of them discuss how the proposed solution fits into a comprehensive architectural framework.