esa-sceva/satcom-chunk-collection huggingface dataset:
# Deformation Study of Strongly Structured Clays Considering Damage Effects
## 3 Test Results and Analysis
### Evolution of Pore Structure Damage  
The damage variable serves as both an internal parameter that influences the material's constitutive relationship and a metric for quantifying the degree of disturbance [27]. To establish a connection between a given stress state applied to the structurally strong clay of the Zhanjiang Group and the degree of disturbance in the soil, we introduced the concept of the damage variable. The rate of change in the void ratio during axial strain was chosen to represent the degree of damage, with the degree of damage for undisturbed soil defined as 0% and that for remolded soil defined as 100%. This relationship can be expressed as follows:  
\[\text{Damage degree }W\text{: }W=\frac{e_{0}-e_{i}}{e_{0}}\times 100\% \tag{2}\]  
In the formula, \(\text{e}_{0}\) represents the initial void ratio of the soil sample prior to any vibrational disturbance, while \(e_{i}\) denotes the void ratio after consolidation has stabilized at a specific pressure level during the compression test.  
In the one-dimensional compression test, each pressure level corresponded to a specific damage index \(W\). Consequently, there was a variation in damage \(\Delta W\) across different pressure levels. The change in \(\Delta W\) as a function of pressure effectively characterized the damage evolution of the specimen under various loading conditions.  
As illustrated in Figure 2, three types of disturbed soil samples demonstrated the evolution of \(\Delta W\) as the pressure level increased. Taking the sample with a damage index of \(D\) = 38.6% as an example, the damage evolution can be categorized into three distinct stages:  
* Pressure range of 12.5 to 400 kPa: During this phase, \(\Delta W\) increased gradually with pressure. As pressure rose from 200 kPa to 400 kPa, the structural integrity of the sample began to deteriorate significantly. At 400 kPa, the degree of damage to the pore structure was markedly higher than at 200 kPa, resulting in an accelerated increase in \(\Delta W\).
* Pressure range of 400 to 800 kPa: In this range, the sample's structural integrity was severely compromised, culminating in complete failure at 800 kPa. This led to a substantial reduction in void ratio at this pressure state, while the damage index \(W\) correspondingly increased. Despite the rapid rise in W between 200 and 400 kPa, the relative value of \(\Delta W\) exhibited a decreasing trend.
* High-pressure range of 800 to 3200 kPa:  
\begin{table}
\begin{tabular}{c c c c} \hline
**Sample Group** & \(\boldsymbol{C_{\mathit{CLB}}}\) & \(\boldsymbol{C_{\mathit{CLR}}}\) & \(\boldsymbol{D/\%}\) \\ \hline Undisturbed soil & \(-0.0145\) & \(-0.1400\) & 10.4\% \\ \(A_{1}\)\(-\)\(A_{3}\) & \(-0.0423\) & \(-0.1400\) & 30.2\% \\ \(A_{4}\)\(-\)\(A_{6}\) & \(-0.0445\) & \(-0.1400\) & 31.8\% \\ \(A_  
In this stage, the overburden pressure significantly exceeded the specimen's yield stress, which may result in the formation of new pore structures. The damage to these new pore structures increased linearly with pressure.
## 1 Introduction
In works such as [25; 26; 27; 32], the integration of ZAD and FPIC techniques show good results in variable regulation, using buck converters with resistive and motor loads. The ZAD-FPIC technique has been applied in second-order systems, however there are no bifurcation analyses for the FPIC technique. All these applications show that the ZAD-FPIC technique integrates well with the buck converter, achieving good regulation and tracking of signals at the output when the load is resistive. In addition, with this technique the chattering problem is avoided and the fixed switching frequency is obtained, which is appropriate for control of motors and electromechanical applications. However, in the case of non-resistive loads, e.g. DC motors, few studies have been carried out [26], creating the need for a detailed study that considers the parameter adjustment of the control law, and the effects of numerical quantization, delays, bifurcations, and chaos behavior. As previously reported in the literature, controllers cause saturation in the duty cycle, creating variable switching frequency and sub-harmonics. This problem has been solved by the applying ZAD-FPIC technique for resistive loads; however, electromechanical systems that consider the quantization effects and delays (to obtain a fixed switching frequency in the switches) have not been used, which are more interesting for industrial applications. Therefore, this paper shows the application of the ZAD-FPIC technique to control the speed of a DC motor using a buck converter as an electric actuator. Additionally, quantization effects are included in the system variables and bifurcation diagrams are obtained to show the behavior when the control parameters are changed. Some of the advantages of using ZAD-FPIC techniques are that the controller presents a fixed switching frequency, low steady-state error, and robustness for the time delays in the system. In [6], some advantages of using ZAD-FPIC techniques to control the buck converter are shown after comparing this controller to the sliding mode control (SMC) and proportional-integral-derivative (PID) techniques. However, this technique is not robust to changes in the system parameters, and the real-time processing requires a high sampling rate and synchronization of signal sensing with a centered pulse width modulation (CPWM) output [33]. Additionally, the design of the controller becomes very complicated when it is used to control higher order systems (more than 6 linear differential equations). Hence, to present the development of this control application, the paper is divided into four more sections. Section 2 presents the materials and methods used in the research, as well as the mathematical modelling required to design the controller using the ZAD-FPIC technique and to perform the closed loop control simulations. Section 3 includes the results and analysis of the different simulation and experimental tests performed with the proposed changes in the control parameters. Finally, Section 4 ends the paper presenting the conclusions.
## 5 Machine Learning Framework for Dimension Optimization
### The Proposed DNN Framework
#### 5.2.1 Input Layer  
To establish the DNN-based supervised learning framework, the input data of a learning system, i.e., \(\mathbf{w}\) in (31), should be determined considering the system model and the problem \(\mathbf{P1}\). Owing to the insight from the lower bound \(\sum_{g=1}^{G}\mathcal{R}_{g}^{\prime}\left(s_{1},\ldots,s_{G}\right)\) in Section 4, our proposed DNN model takes the input of SNR at each user (\(\gamma\)), feedback bit allocation (\(B\)), and the number of users (\(K_{g}\)) and eigenvalues of covariance matrix for all groups as follows:  
\[\mathbf{w}=\left[B,\gamma,\underbrace{\{K_{1},\lambda_{1,1},...,\lambda_{1,M} \}}_{\text{Group 1}},...,\underbrace{\{K_{G},\lambda_{G,1},...,\lambda_{G,M}\}} _{\text{Group }G}\right]^{T}\in\mathbb{R}^{(M+1)G+2}, \tag{33}\]  
where \(\{\lambda_{g,1},...,\lambda_{g,M}\}\in\mathbb{R}^{M}\) for the \(g\)-th group can be divided by the \(r_{g}\) non-zero eigenvalues of the covariance matrix of the \(g\)-th group and \((M-r_{g})\) zeros. For the feature information of the input, the eigenvalues characterize the optimal dimensions because the covariance matrices affect the objective function of problem \(\mathbf{P1}\), which is statistically determined by the effective channels and quantization codebooks. Note that we adopt the eigenvalues rather than the covariance matrices itself in the view of principal component analysis (PCA) for the machine learning technique [22]. It reduces the complexity of the proposed DNN model from large-scale antennas because taking covariance matrices (\(\{\mathbf{R}_{g}\}_{g=1}^{G}\)) as the input requires \(((M^{2}+1)G+2)\) input nodes.
# Information Security Applications in Smart Cities: A Bibliometric Analysis of Emerging Research
## 4 Findings and Discussion
#### 4.1.5 Cluster 5 (Pink): Evaluation of Threats to Cybersecurity  
The domain of cybersecurity threats is directly related to discussions about cybersecurity control and data in online services. Form IT advancement, new communication technologies, and control methods may allow better regulation of the smart grid; however, they also introduce serious threats to cybersecurity. In the Digital Age, security is the keyword. For the authors, having reliable data, systems, and people is indisputable because cyberattacks happen frequently, and systems capable of preceding an attack are essential [357].  
Cyberattacks may also cause cascading failures in a power system, thus posing a serious threat to national infrastructure. Because of this, the authors suggest that the preconditions for managing cybersecurity risks are discovering incidents, collecting data, and viewing that data [174; 358]. Three principles support this management cycle: maintaining the right data, robust IT infrastructure (systems), and an appropriate scope of sharing (people).  
Impact analysis of threats is necessary to analyze the consequences of interruptions in the flow to protect and enable the evolution of business through technology, as well as to monitor users, observe the behavior, and monitor the development of attacks. Therefore, making potential threats clear can improve the protection shield and allow for new business opportunities [341].  
The idea of resilience against a cyberattack, in addition to helping to know how to deal with a situation for which companies are not prepared, is to recognize the complexity of a scenario and have a contingency plan and defenses at different levels of security. In this way, it is possible to mitigate possible impacts resulting from cyberattacks [359].  
In this sense, performing defensive security planning is essential, as the systems will cease to function over time, generating large potential losses for companies. [PERSON] et al. [360] comment that investing in business cybersecurity is essential, given that criminals focus on operating systems with security gaps that have not been fixed or that have not yet been updated to a newer version. This vulnerability increases the risk and highlights the importance of investing in a consistent monitoring process [361].  
In several countries, cyber defense constitutes a national security framework in which states establish policies at all levels (public and private) to guarantee individual freedoms and to respond to aggressions and invasions by developing response and cooperation systems [362]. Taking these security policies as a reference related to cyber resilience, emerging countries can adopt the definition of tasks and missions to establish security standards in the public and private environment, highlighting the specific criticality of the IT infrastructure [363].
## 3 Results
### Shear Wave Speed Analysis  
The quantitative analysis of shear wave observations consisted of shear modulus estimation based on group and phase velocity analysis for real and virtual SWE acquisitions, as visualized in Figure 6. For the measurements, the group velocity analysis provided median shear stiffness values of 14.6 kPa and 17.1 kPa for single and compounded PWI respectively, when discarding data of 5 edge elements for each shear wave during shear modulus estimation. These estimations increased to 23.8 kPa and 18.8 kPa when 15 more data points were not considered during the fitting procedure for each shear wave. Phase velocity analysis gave median values of 24.7 kPa and 27.3 kPa for the measurements. For single PWI, the stiffness range of TOF-estimations when taking less data points into account during fitting (15.9 kPa) was remarkably higher than for other stiffness estimation methods (5.0 kPa and 4.6 kPa for group and phase speed analysis respectively). Actual PVA stiffness was mechanically determined at 24.3 \(\pm\) 0.6 kPa.  
For the virtual imaging acquisitions, the group velocity-based method estimated median stiffness at 14.4 kPa and 15.4 kPa for single and compounded PWI respectively. When discarding data from 20 edge probe elements, TOF stiffness estimations increased to 18.0 kPa and 15.6 kPa. Phase velocity analysis provided, for both imaging simulations, higher estimates of median shear stiffness, i.e., 24.9 kPa and 25.2 kPa for single and compounded PWI respectively. As for the experiments, the largest spread in stiffness estimation across depth (11.8 kPa) was obtained for single PWI when applying the group velocity analysis and discarding data from 20 probe elements. For the biomechanics simulations, median shear stiffness of 16.5 kPa, 21.8 kPa and 24.7 kPa were obtained for group velocity (discarding 5 data points), group velocity (discarding 20 data points) and phase velocity analysisrespectively. Again, the depth-dependency of stiffness estimations was the largest for the group speed method taking less data points into account during fitting (10.3 kPa).
## 3 Results and Discussion
### Effect of His-Glu on Intracellular Signaling Pathways in RBL-2H3 Cells  
Since it was suggested that His-Glu inhibits degranulation by affecting the calcium ion-independent pathway, we performed an immunoblotid analysis. Figure 4 shows that Syk and phospholipase Cy (PLCy) were phosphorylated after antigen stimulation of RBL-2H3 cells. We confirmed that His-Glu does not affect the phosphorylation of PLCy1 or PLCy2. The phosphorylation of Syk tended to decrease by treating the cells with His-Glu, but not significantly. These signaling proteins play an important role in signaling that increases calcium ion concentrations in the cytosol [24]. Thus, this result was as expected because His-Glu did not significantly inhibit the increase in cytosolic calcium ion concentrations (Figure 1B). Figure 4 also shows that PI3K and Akt were phosphorylated after antigen stimulation in RBL-2H3 cells. We found that His-Glu significantly downregulates the phosphorylation of PI3K and Akt. PI3K and Akt play a crucial role in the reorganization of microtubules in basophils and mast cells to carry intracellular granules for degranulation [25]. These results indicated that L-glutamic acid-containing dipeptides downregulate the phosphorylation of PI3K and Akt, leading to suppressed degranulation by inhibiting the reorganization of microtubules.  
Figure 3: Effect of an acidic amino acid residue-containing dipeptide on microtubule reorganization in RBL-2H3 cells. Degranulation was induced with antigen after IgE-sensitized RBL-2H3 cells were treated with 2.0 mM His-Glu. After staining with an anti-\(\alpha\)-tubulin antibody labeled with Alexa Fluor 488, the cells were observed under a fluorescence microscope with \(\times\)60 magnification of an objective lens.
# Automatic Detection Method of Sewer Pipe Defects Using Deep Learning Techniques
## 1 Introduction
We evaluated the detection performance of different models on our self-made dataset, and the experimental results show that our method is superior to the current state-of-the-art object detection methods. Overall, the main work of this paper is as follows:  
1. To address the shortcomings of existing defect detection methods, an improved YOLOv4 model is proposed to detect and classify sewer defects. This model has high detection performance and fast detection speed, which can be better adapted to the sewer defect detection tasks;
2. Based on the processing speed and detection accuracy of the model, the influence of three bounding box loss functions on model performance are compared, including GloU, DIoU and CIoU;
3. To address the lack of datasets for sewer defect detection, this study selects a total of 2700 images from the public dataset Sewer-ML for defect location annotation. The dataset is labeled in a multi-label form, including four types of the most common defects such as crack, deposition, root and stagger. As shown in Figure 1, the overall workflow of this study contains: (1) sewer defect images collection and annotation; (2) image augmentation; (3) the proposed model training; and (4) defect detection and model performance evaluation.
# Research on Equivalent Static Load of High-Rise/Towering Structures Based on Wind-Induced Responses
## 2 Refinement of Structural Wind-Induced Response Assessment Method  
For a multi-degree-of-freedom structure, the basic equation of motion under wind load is  
\[[M]\ddot{Y}(z,t)+[C]\dot{Y}(z,t)+[K]Y(z,t)=P(z,t) \tag{1}\]where \([M]\), \([C]\) and \([K]\) are the mass matrix, damping matrix and stiffness matrix of the structure, respectively, \(P(z,t)\) is the pulsating wind load time course at node \(z\), and \(\dot{Y}(z,t)\), \(\dot{Y}(z,t)\) and \(Y(z,t)\) are the acceleration time course, velocity time course and total pulsating displacement time course, respectively.  
Decoupling the above structural dynamic equilibrium equations yields N mutually independent equations expressed by generalized coordinates. The normalization of the vibration mode in relation to mass gives the following equations:  
\[\ddot{q}_{j}(t)+2\zeta_{j}\omega_{j}\dot{q}_{j}(t)+\omega_{j}^{2}q_{j}(t)=F_{j} (t) \tag{2}\]  
\[Y(z,t)=\sum_{j=1}^{N}\phi_{j}(z)q_{j}(t) \tag{3}\]  
\[F_{j}(t)=\sum_{i=1}^{N}P(z_{i},t)\phi_{ji}=\sum_{i=1}^{N}\rho C_{D}(z)A_{i} \overline{U}(z_{i})u(z,t)\phi_{ji} \tag{4}\]  
where \(q_{j}(t)\) is the \(i\)th modal generalized coordinate time course, \(\zeta_{j}\) is the \(j\)th modal damping ratio, \(\omega_{j}\) is the \(j\)th modal natural vibration angular frequency, \(F_{j}(t)\) is the \(j\)th modal generalized load, \(\phi_{ji}\) is the vibration displacement component of the \(i\)th node of the \(j\)th vibration mode, \(\rho\) is the air density, generally taken as \(1.25\) kg/m\({}^{3}\), \(C_{D}\) is the quasi-constant wind pressure coefficient, \(A_{i}\) is the windward area of the \(i\)th node, \(\overline{U}(z_{i})\) is the average wind speed of incoming flow at height \(z_{i}\), and \(u(z_{i},t)\) is the time course of incoming pulsating wind speed at height \(z_{i}\).
###### Abstract
Some studies have evaluated the performance of precipitation estimates using satellite products. They have shown that, in general, products tend to underestimate or overestimate extreme rainfall [21; 22; 23; 24; 25; 26; 27]. Validating satellite products is even more important considering that satellites are the only available source of data in many regions. Our main goal in this study was to describe the performance of different Satellite Precipitation Products (SPP) for extreme rainfall events associated with natural disasters over different climate regimes in the five main regions of Brazil and to discuss the contribution of mesoscale convective systems (MCS) to these events.
## 3 ESTCube-Luna E-Sail Payload and Experiment Operations
### E-Sail Deployment
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Tether Length (km)** & **Rot. Rate (rad/s)** & **Rot. Period (s)** & **Ang. Momentum (N m s)** \\ \hline
0.1 & 0.117 & 53.6 & 3.86 \\ \hline
0.5 & 0.0350 & 180 & 55.0 \\ \hline
1.0 & 0.0190 & 330 & 191 \\ \hline
2.0 & 0.00998 & 630 & 702 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Characteristic tether deployment parameters for various deployed lengths at 5 mN tension. Figure 6: Simulation results of deploying the first 60 m of the ESTCube-LuNa E-sail tether: spacecraft angular velocity, tether tension and length, and the centre-of-mass (CoM) shift of the spacecraft– tether system. Spin-up and deployment are performed in steps by considering the tether tension and deflection angle as safety factors. The increased tension upon the initial deployment must be analysed further in terms of safety and dynamical aspects. Figure 8 shows the dynamic tether behaviour during the beginning stages of deployment. When the tether is being unreeled, it lags behind the spacecraft rotation and starts oscillating. When reeling is stopped, the oscillation continues, but the lagging effect disappears. The oscillations die out slowly over time. The oscillation frequency depends on the length of the tether, similarly to a pendulum. The amplitude of the oscillations and the lagging effect can be limited by increasing the tether tension (i.e., the angular velocity of the spacecraft). The initial increasing of tether tension, mentioned above, is clearly visible in the top right plot. With the NanoFEEP thrusters, whose performance is used as the baseline for the simulation study above, special care must be taken to ensure that the metal plume would not hit the solar cells and the tether. As discussed in detail in Section 5, the deployable solar  
Figure 8: Simulation results of deploying the first 1 m of ESTCube-LuNa tether using an unreeling speed of 1 cm. The initial angular velocity was chosen to provide approximately 1 mN of tether tension. Figure 7: Simulation results of deploying the full 2 km ESTCube-LuNa tether. During deployment, the tether tension is maintained between 1 mN and 5 mN, except during the first step, as explained above. panels are placed at a 45\({}^{\circ}\) angle from the thruster direction in a so-called X-configuration. The panels are kept safe by limiting the nominal plume half-angle to \(<\)\(\pm\)25\({}^{\circ}\). For the tether, the safety is achieved by pointing the thrusters orthogonally with respect to the tether deployment direction and by limiting the tether deflection angle to \(\pm\)45\({}^{\circ}\), leaving a 20\({}^{\circ}\) safety margin between the thruster plume and the tether.
# Handover Scheme in LEO Satellite Networks Based on QoE for Streaming Media Services
## 2 System Model  
Considering that the flight speed of the LEO satellite is much higher than the moving speed of the user terminal on the ground, it can be assumed that the user terminal is stationary with respect to the Earth's surface. As shown in Figure 1, the communication system consists of LEO satellites, user terminals, gateway station and terrestrial networks. The terminals are distributed in open environments on the surface (deserts, oceans, no man's land, etc.), and their areas can be covered by multiple LEO satellites. Multiple users apply for multimedia services from the streaming server via terminals and satellite links. After a series of processes to establish the communication service, the server sends the data packets to the gateway station through the Intel satellite network, and then the gateway station sends the data packets to the LEO satellites linked by the users through the satellite-earth and inter-satellite links, and finally it sends them to the user's terminal via the satellite-earth link. When the user uses the satellite communication service, the terminal decides whether to handover and to which candidate satellite by periodically measuring the received signal strength indication of the satellites within the coverage area and analyzing their satellite status parameters (elevation angle, remaining visible time, satellite load, etc.) as well as combining with the user's own business status.