esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The behavior of steel-concrete composite structures is significantly influenced by the efficiency of the shear connections that link the two materials. This research examines the performance of stud shear connectors, with an emphasis on analyzing the effect of different geometric design parameters. A computational model was created utilizing Python 3.13 to enable thorough digital monitoring of the influence of these parameters on the structural performance of composite connections. Developed within the ABAQUS framework, the model integrates geometric nonlinearity and the Concrete Damage Plasticity (CDP) approach to achieve detailed simulation of structural behavior. Essential design aspects, including stud diameter, stud height, head dimensions, and spacing in both longitudinal and transverse directions, were analyzed. The Python-based parametric model allows for easy modification of design parameters, ensuring efficiency and minimizing modeling errors. The significance of stud diameter changes was analyzed in accordance with Eurocode standards and previous studies. It was found that stud length has a reduced effect on structural performance, particularly when considering the concrete properties used in bridge construction, where compressive failure of the concrete zone is more critical at lower concrete strengths. Additional factors, such as stud head dimensions, were investigated but were found to have minimal effect on the behavior of steel-concrete composite connections. Longitudinal stud spacing emerged as a critical factor influencing structural performance, with optimal results achieved at a spacing of 13d. Spacings of 2d, 3d, and 4d demonstrated overlapping effects, leading to significant performance reductions, as indicated by comparisons of ultimate load and force-displacement responses. For transverse spacing, closer stud arrangements proved effective in reducing the likelihood of slip at the steel-concrete interface, enhancing composite action, and lowering stress concentrations. Additionally, reducing the transverse distance between studs allowed for the use of more shear connectors, increasing redundancy and enhancing performance, especially with grouped-stud connectors (GSCs).  
steel-concrete composite structures; composite action; steel-concrete composite bridges; grouped-stud connectors; numerical modeling +
Footnote †: journal: Applied Sciences  
Article
## 3 Feasibility Analysis of IoT Architecture Utilizing LoRa Communications on Mars
### Analysis of the Constellation Model and the Revisit Time of Satellites over IoT Nodes  
When planning the deployment of satellite constellations for data collection, it is essential to consider the optimal constellation model and the revisit time of satellites over IoT nodes. In that sense, it is possible to propose two models: Walker Delta and Walker Star. Both are widely used in communication satellite constellations on Earth [41]. The Walker Delta model is characterized by a triangular arrangement of satellites in orbit and greater redundancy in communication. This configuration provides better coverage of areas near the equator. However, it does not adequately cover the poles, providing partial planet coverage [42]. The Walker Star model has an inclination angle close to 90 degrees [41] to provide complete global coverage, including the polar regions [43]. That is why the model that we propose, due to its greater capacity to provide coverage to the polar regions, and the one used in the study, is the Walker Star. Figure 4 shows both models, highlighting the satellites' coverage areas and orbital movements. Given the complexity of exploring the poles of Mars and the need to understand the interrelationship between polar deposits and its climate system, there is significant scientific interest in exploring Mars at high latitudes [16].  
Satellite revisit refers to the time interval that elapses between two successive passes of a satellite over an exact location on the planet's surface. This is a critical factor that influences the efficiency and frequency with which satellites in a constellation pass over a specific point on Mars and the visibility time of those satellites by IoT nodes. For its part, the factors that influence the determination of the revisit of a satellite over a specific point on the planet are the arrangement of the constellation, the orbital height, the inclination, and the latitude of the object in question [44]. The variation in revisit times and visibility of satellites over IoT nodes on Mars has significant implications for the planning and operation of space missions on the red planet. A greater revisit time allows for more frequent communication and more efficient data collection, while longer visibility times increase the window of opportunity for data transmission. On the other hand, the greater the inclination angle of the satellite to the equator, the greater the coverage at higher latitudes, as shown in Figure 4, ensuring coverage in the polar regions.
# Performance of Solar Control Films on Building Glazing: A Literature Review
## 5 Discussion of Existing Studies on the Performance of Solar Control Films  
Based on the findings of the existing studies on the performance assessment of SCFs in building glazing systems, it is possible to note that the performance and efficiency of SCFs depend on different conditioning factors such as the: type and size of the glazing; local weather conditions; installation position of the film; solar orientation of the glazing; existing shading systems; type and efficiency of existing climate control systems; and cleaning and maintenance practices of the building.  
This section briefly discusses the conditioning factors and compares research approaches and performance assessments conducted by the research studies reported in the literature review.
## 2 Methods
### Reconstruction Procedures of 2D Compressive Sensing Interferometric Synthetic Aperture Microscopy  
The detailed processing steps of method combining 2D CS-SDOCT with ISAM are summarized as follows [10]:  
1. Reconstruct \(\mathbf{S}(x_{c},k)\) from \(\mathbf{S}(x_{c},k_{c})\) (step 1 shown in Figure 2i).  
(1) Initialization: residual signal \(\mathbf{r\_n}=\mathbf{S}(x_{c},k_{c})\), reconstructed signal \(\mathbf{a}(x_{c},z^{\prime})=\mathbf{0}\), and measurement matrix \(\mathbf{T}=\mathbf{M}_{\mathrm{k}}\ \mathbf{F}\).  
(2) Apply IDFT to \(\mathbf{r\_n}\) to get spatial-domain signal \(\mathbf{S}_{1}\).  
(3) Find signal peaks with intensity higher than preset value \(\mathrm{I}_{1}\) in \(\mathbf{S}_{1}\).  
(4) Update residual signal \(\mathbf{r\_n}\) and reconstructed signal \(\mathbf{a}(x_{c},z^{\prime})\) using the least squares method [10].  
(5) Repeat step (2) for the updated \(\mathbf{r\_n}\) and \(\mathbf{a}(x_{c},z^{\prime})\).  
(6) Repeat step (3) and use a lower light intensity threshold \(\mathrm{I}_{2}\) to find smaller signals.  
(7) Repeat steps above until the \(\varepsilon\) is less than the set value, and then obtain the reconstructed signals \(\mathbf{a}(x_{c},z^{\prime})\), from which we can get \(\mathbf{S}(x_{c},k)\) and \(\mathbf{S}(x_{c},z^{\prime})\).  
2. Reconstruct \(\mathbf{S}(Q_{//}k)\) from \(\mathbf{S}(x_{c},k)\) (step 2 shown in Figure 2i).  
(1) Initialization: residual signal \(\mathbf{r\_n}=\mathbf{S}(x_{c},z^{\prime})\), reconstructed signal \(\mathbf{a}(Q_{//},z^{\prime})=\mathbf{0}\), and measurement matrix \(\mathbf{T}=\mathbf{M}_{\mathrm{x}}\ \mathbf{F}^{\mathrm{H}}\).  
(2) Repeat steps 1. (2)-(7) to get reconstructed signal \(\mathbf{a}(Q_{//}z^{\prime})\), from which we can get complete spectral matrix \(\mathbf{S}(Q_{//},k)\).  
3. Apply a filter to \(\mathbf{S}(Q_{//},k)\) to get \(\mathbf{a}(Q_{//},\beta)\)[16] (step 3 shown in Figure 2i).  
4. Interpolate the \(\beta\) domain of \(\mathbf{a}(Q_{//},\beta)\) to get the sample function \(\mathbf{a}(Q_{//},\beta^{\prime})\) uniform in \(\beta\) domain. (step 4 shown in Figure 2i).  
5. Apply 2D _IDFT_ on \(\mathbf{a}(Q_{//},\beta^{\prime})\) to reconstruct complete OCT image \(\mathbf{a}\) (\(x\), \(z\)) (step 5 shown in Figure 2i).  
In Step 5, the coordinate transformation formula between the spatial coordinates and the spectral coordinates is \(dz=1/(N_{\beta^{\prime}}d\beta^{\prime})\), where \(N_{\beta^{\prime}}\) and \(d_{\beta^{\prime}}\) are the number and spacing of the elements of coordinate \(\beta^{\prime}\), respectively.
## 7 Conclusions  
A journal bearing test bench was the research object of this paper. A THL model of the journal bearing was established based on the Reynolds equation, the viscosity temperature characteristics, and the Greenwood/Tripp asperity theory. The correlation coefficient of the trajectory was used to evaluate the accuracy of the model. It was found that the correlation coefficient of the calculation results and the measurement results increased in relation to the rotating speed. The correlation exceeded 0.8 under a working condition of 3000 r/min at the different loads, verifying the model's accuracy.  
The relationship between the minimum oil film thickness and the asperity contact percentage was analyzed. The calculations showed that when the minimum thickness of the bearing oil film was less than 5 \(\upmu\)m, asperity contact would occur in the bearing and cause an increase in the friction consumption of the bearing, leading to an increase in the oil film pressure. Thus, the lubrication condition of the bearing worsened. Therefore, the lubrication of the journal bearing was greatly affected by the asperity contact.  
The measured contact potential followed the same rule as the calculated asperity contact percentage. Asperity contact occurred when the potential exceeded 75 mv. It increased in relation to an increase in load. The measured contact potential decreased, followed by an increase in rotational speed. Therefore, it is possible to monitor whether the bearing experiences asperity contact using the contact potential. Thus, the contact potential could be used to monitor the lubrication condition of a journal bearing.  
**Author Contributions:** B.W.: Conceptualization, Writing-Reviewing and Editing; J.Y.: Data curation and Funding acquisition; S.S.: Formal analysis. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Acknowledgments:** The authors are grateful to the Wuhan University of Technology for providing experimental facilities. We also appreciate all the teachers and postgraduate students who made great efforts in this project. This work is supported by the Ministry of Industry and Information Technology of the People's Republic of China via the project grant number 2019-360.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
**List of Nomenclature**  
M Mass matrix of a flexible body E2 Elastic modulus value of the surface materials of the shell Surface roughness value of the bearing journal Surface roughness value of the shell Thermal conductivity of the journal material Convective heat transfer coefficient between the lubricating oil and the journal surface Thermal conductivity of the bearing shell material Convective heat transfer coefficient between the lubricating oil and the shell pad surface Beaming shell temperature Oil film temperature Oil film temperature Oil film setting along the radial direction of the bearing
## 3 Results
### RQ3. How Has IoT impacted Pet Wellness?
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
Regarding the actions aimed at providing pet well-being in the home, the results demonstrate a significant positive impact on pet well-being in several key areas. In terms of health, and specifically in the monitoring of health (e.g., S6, S7, S33, S34, and S35) and the activities performed by pets (e.g., S6, S7, S11, S17, S33, and S35), there was a focus on leveraging technology to enable owners to constantly track the physical condition of their pets, resulting in the early detection of health problems and a greater prevention of chronic diseases. This constant monitoring ensures that pets receive the necessary care in a timely manner, improving their quality of life in the long term. Pet surveillance is another kind of strategy of monitoring that has been implemented (e.g., S2, S18, S19, S25, S42, S45, S48, S49, S51, S56, S62, S70, S77, S80, and S81). Automatic feeding had a crucial impact by ensuring that pets receive the appropriate amount of food, even in the absence of owners (e.g., S8, S9, S13, S30, S34, S49, S55, S59, S61, S72, and S78). This not only helped to prevent obesity and other nutritional problems, but also provided a greater peace of mind for owners, who could adjust and control feeding remotely (e.g., S9, S12, S27, S30, S47, S52, S60, and S74). Precision and regularity in feeding contribute to the overall well-being of pets by maintaining a balanced diet. Access to the pet's habitat is another aspect that has been worked on to improve in homes. In this sense, smart doors improved the mobility of pets within safe environments, allowing them to move freely without compromising their safety (e.g., S15, S16, S21, S22, S39, S46, S56, and S59). These doors are especially useful for allowing pets to have access to different areas of the home in a controlled manner, promoting a more dynamic and stimulating environment for them. Likewise, environmental and comfort monitoring ensured that the conditions within the home were optimal for pets, ensuring a safe and suitable environment for their well-being (all proposals). Finally, automatic cleaning and waste management systems, present in three studies (e.g., S12, S20, S22, S41, S51, S59, S60, and S62), allowed for maintaining a clean and healthy habitat for pets, improving their environment and reducing the risk of hygiene-related diseases.
## 3 Structure of MRAS
### Classical PI Adaptive Law  
Here, the identification of the inductance parameters is taken as an example. Substituting the PI adaptive law into Equation (6), we have  
\[\left\{\begin{array}{l}B(t)=e_{d}\left(R_{s}\hat{I}_{d}-U_{d}\right)+e_{q} \left(R_{s}\hat{I}_{q}-U_{q}\right)\\ \int_{0}^{t_{1}}\left[b+k_{i}\int_{0}^{t}B(t)dt+k_{p}B(t)-\hat{b}(0)\right]B(t )\geq-{r_{1}}^{2}\end{array}\right. \tag{7}\]  
where \(k_{p}\) is the proportional gain; \(k_{i}\) is the integral gain; \(\hat{b}(0)\) is the nominal value of the reciprocal of the inductance. Equation (7) is [PERSON]'s inequality of inductance identification based on the PI adaptive law, which satisfies the stability condition when the values of \(k_{p}\) and \(k_{i}\) are greater than zero [8]. By combining Equation (3) with Equation (7), it can be found that when the reference model voltage is invariable, Equation (3) is an integral series system with input \(\hat{b}\) and output \(\hat{I}\), while Equation (7) is a zero-order linear system with input \(\hat{I}\) and output \(B(t)\). Therefore, an integral series system with input \(\hat{b}\) and output \(B(t)\) can be obtained. Its system structure is shown in Figure 3, where PI is controller; \(\hat{b}\) is the reciprocal of the estimated inductance; Equation (3) is the adjustable model; \(\hat{I}\) is the estimated current; \(U\) is the stator voltage.  
Figure 2: Error of the state space equation system block diagram.  
The classical PI adaptive law has many advantages, such as simple parameter adjustment and fast response speed [21] but it cannot achieve a good anti-disturbance performance. Due to the proportional element, it is difficult to meet the balance between the tracking speed and the anti-disturbance performance. Therefore, it is necessary to determine a suitable \(k_{p}\) to balance the response speed and the anti-disturbance performance, which is not an easy task.
# Thermal Fatigue Properties of Ultrasonically Bonded Copper Joints
## 1 Introduction  
The electronics industry continuously demands miniaturization combined with high integration in electronic devices, e.g., power modules and printed circuit boards [1; 2; 3]. Meeting these demands can lead to an increase in the current density of devices [4; 5]. This miniaturization and the increase in current density are likely to increase heat generation and result in temperature rise [1; 6]. Therefore, using soldering for bonding in such high-temperature environments would lead to risks involving the melting of the solder and the formation of Kirkendall voids owing to diffusion.  
Ultrasonic bonding is one of the solid-state bonding methods, and forms strong joints at relatively low temperatures in a short time and limits these risks [7]. Copper is a valuable material for application in electronic packaging because of its better electrical and mechanical properties than Al, and studies on the ultrasonic bonding of copper in electronic devices have recently been conducted [8]. For example, the copper/copper joints in electronic devices are composed of a copper substrate and wiring material such as a terminal copper wire. Further, the substrate is sometimes composed of pluralmaterials. In this case, the coefficient of thermal expansion (CTE) of the substrate can be expressed using Equation (1):  
\[CTE=\frac{\sum_{i=1}^{n}CTE_{mat-i}\times E_{mat-i}\times V_{mat-i}}{\sum_{i=1}^{n }E_{mat-i}\times V_{mat-i}} \tag{1}\]  
where \(CTE_{mat-i}\) is the CTE of material \(i\), \(E_{mat-i}\) is its Young's modulus, and \(V_{mat-i}\) is its volume ratio [9]. Thus, problems in electronic devices pertain not only to high-temperature environments but also to thermal fatigue caused by the thermal stress occurring in copper/copper joints, i.e., the Cu substrate and Cu terminals. Although several studies involving thermal cycling tests of ultrasonically bonded copper joints have been conducted, no study discusses the decrease in the strength of the copper/copper joint before and after the thermal stability test [10; 11]. This is because a sound joint was formed via ultrasonic bonding using high input energy in conventional studies, which has complicated the evaluation and understanding of the mechanism of thermal fatigue failure in ultrasonically bonded joints.  
Therefore, in the present study, we investigated the thermal fatigue properties of ultrasonically bonded copper joints. In order to clearly understand the thermal fatigue behavior, the energy input during ultrasonic bonding was low compared to that used in conventional studies. Further, two copper alloys with different hardness and grain sizes (C1020 and C1940) were used to evaluate the improvement in the thermal fatigue resistance of the ultrasonically bonded copper joint.
## 1 Introduction
In this paper, the failure time data and the performance degradation data are fused, and the failure rate and the standard deviation of life characteristics are introduced as the residual life prediction criteria to reflect the failure of a component or system at a certain time. To meet the current demand of adaptive residual life prediction data with small samples, this paper adopts the statistical matching method and establishes the adaptive matching preprocessor model of life characteristics.
## 4 Methodology: Problem-Solving Methodology
### Two-Stage Adaptive Large Neighbourhood Search (Two-Stage ALNS)
#### 4.1.3 Destroy Operator  
Six destroy operators were customised in the proposed two-stage ALNS method. The destroy operator removes cells/parts from the current solution until a predefined number of removed cells/parts is achieved. The number of removed cells per part can be expressed as follows:  
\[\rho=\textit{des}\cdot R \tag{43}\]  
where \(\rho=\) Number of removed cells/parts. \(\textit{des}=\) Degree of destruction. \(R=\) Number of total cells/parts.  
At every predetermined iteration \(i_{\textit{check}}\), \(\textit{des}\) is tuned when the current solution shows no improvement. The destroy operators of Stage1 and Stage2 are as follows:Random cell removal \(D_{1-1}\): The operator randomly removes the cell until the predefined number of removed cells is reached.
2. Worst cell removal \(D_{1-2}\): The operator removes the cell that contributes the least to the Model B1 objective value one at a time until the predefined number of removed cells is reached.
3. Shaw cell removal \(D_{1-3}\): This removal operation was originally proposed by [PERSON] [57]. The operator randomly selects the cell and calculates its relatedness \(\Phi_{ij}\) with the other cells. Next, the operator randomly removes one cell from the pair with the highest relatedness score. The cell relatedness can be determined using Equation (44). If there is no relatedness between all pairs, the operator randomly removes the cell from the current solution. \[\Phi_{ij}=A_{i}^{T}\cdot A_{j}\] (44) where \(\Phi_{ij}\) = Relatedness value of cell/part \(i\) and cell/part \(j\). \(A_{i}^{T}\) = Transposed array of the first selected cell/part \(i\). \(A_{j}\) = Cell/part array \(j\).
4. Random part removal \(D_{2-1}\): The operator randomly removes parts until the predefined number of removed parts is met.
5. Worst-part removal \(D_{2-2}\): The operator removes the parts that contribute the least to the Model B2 objective value one at a time until the predefined number of removed parts is met.
6. Shaw part removal \(D_{2-3}\): This operator works similar to \(D_{1-3}\) operator.
# A Secure Link-Layer Connectivity Platform for Multi-Site NFV Services
## 2 Background on Multi-Site VNF Communications
### Layer-2 Inter-Site Communications
The size of MAC tables, and thus their capacity to store the MAC addresses of new VNFs, is orders of magnitude lower than that of IP tables, for example. **(2)**: **Provisioning at the edge router**. Although it has been commented that layer-2 communications could be based on switches and dark fibers, from a practical point of view that type of solution is simply not affordable. Multi-site communications at layer-2 always imply the use of provisioned connectivity services, i.e., layer-2 VPNs, each of them having to be configured at each of the involved nodes. This strong configuration effort tends to be solved via automated provisioning systems. However, as of today, the degree of automation that has been achieved in NFV sites to provide connectivity via VXLAN [18], for example, has not been achieved in WANs for the provisioning of layer-2 VPN services. It must be noted that networks interconnecting NFV sites are normally multi-service networks, not green field isolated scenarios, so evolution with regards to provisioning systems must always be aligned with the rest of the services that such networks are offering. For example, layer-2 VPNs supporting multi-site NFV scenarios may coexist in the same router with layer-2 VPNs providing high-grade connectivity services for big enterprises, such as banks. That certainly slows down the adoption of such new provisioning systems, to avoid potential impacts in operational services, and despite multiple initiatives which are working on automating these procedures. For example, the MUST working group at the Telecom Infra Project (TIP) [21] works to accelerate the adoption of the Software Defined Networking (SDN) paradigm, which would potentially enable the replacement of slow provisioning systems (weeks) with newer SDN controllers (minutes). Other approaches include the definition of a WAN Infrastructure Manager (WIM) [22] at ETSI NFV. However, to the best of our knowledge there are no mature implementations of WIM modules that could be used to support large-scale provisioning scenarios among multiple NFV sites.