esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Evaluation of Rigidity Center
###### Abstract  
It is known that when a reinforced concrete building exposed to a horizontal load is subjected to torsional moments around its center of rigidity, additional shear stresses occur in the vertical load-carrying elements, such as the columns and shear walls. Therefore, in order to estimate the additional stresses caused by the torsion, the rigidity center should be calculated precisely. It is known that there are several analytical approaches to calculating the rigidity center location. These approaches do not calculate the rigidity centers close to each other in asymmetric buildings. As significant differences were observed in the calculation of the rigidity center using analytical methods, it was decided to seek verification by conducting an experimental study. In order to calculate and verify the location of the rigidity center, an extensive experimental study was planned. A total of 20 scaled and revised buildings were built, and they were tested in the specially designed test setup. The tested buildings had square, rectangular and irregular floor plans. In addition, vertical load-carrying members were either symmetrically placed on the floor plan or kept asymmetrical to see the effect of their location on the rigidity center. All the buildings were tested under their _self-weight_, and the corresponding displacements were recorded. Additionally, all the buildings were modeled using ETABS to verify the theoretical background of the rigidity center. From the test results, it was found that the resultant shear force can be calculated by multiplying the displacements of each member of a given story found from the tests on its bending stiffness, and this will give the location of the rigidity center. The rigidity center was found to be identical to the results obtained from the 3D model analysis using ETABS, although it uses a different procedure. As the results from the experiment and 3D model are close to each other, it can be said that the rigidity center of reinforced concrete buildings can be found from simple tests using any material that has almost uniform mechanical properties.  
rigidity center; torsion; mass center; reinforced concrete building +
Footnote †: journal: Applied Sciences
# Study of Systems of Active Vibration Protection of Navigation Instrument Equipment
## 6 Conclusions  
As a result of the research, the following was established:  
* the effect of the vibration isolator parameters on its efficiency, its own dynamics, and the possibility of reducing the lower limit of the vibration isolation frequency range has been clarified;
* a linear mathematical model of an active vibration isolator was developed, taking into account the dynamics of sensitive elements and the executive mechanism;
* analytical relations were obtained for an approximate estimation of the zeros and poles of the transfer function of the active vibration isolator and the results of the analysis based on these relations;
* a well-founded schematic implementation was developed of the navigation equipment vibration isolation system using an elastic suspension on vertically arranged flat springs according to the scheme of the inverted pendulum. This makes it possible to achieve low natural frequencies of the vibration isolator in the horizontal plane (0.5-1 Hz), and also ensures the practical absence of angular rotation of the navigation equipment relative to the Earth.
* the main requirements for the technical means of implementing an active vibration isolator are determined, when justifying the need for a coordinated selection of gain coefficients and time constants of individual feedback elements of an active vibration isolator of navigation equipment.  
A technique has been developed that allows, based on the given characteristics of the elastic suspension, determination of the natural frequencies and the maximum amplitude of oscillations. The research will affect the improvement in the accuracy of determining the navigational parameters of the movement of air and sea objects. An increase in accuracy will be possible due to a decrease in the intensity of vibration of sensitive devices designed to enter initial data into the on-board navigation systems of these objects, which will lead to a decrease in the deviation of the real trajectory of the vessels from the calculated one.  
Conceptualization, I.K. and V.M.; methodology, I.K., V.M. and V.K.; software, V.K. and V.P.; validation, I.K., V.M. and V.K.; formal analysis, V.M.; investigation, I.K., V.M., V.K., V.P. and K.B.; resources, V.K., V.P. and V.K.; data curation, V.P. and K.B.; writing--original draft preparation, V.M., V.K. and V.P.; writing--review and editing, I.K.; visualization, K.B.; supervision, V.M.; funding acquisition, I.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original contributions presented in the study are included in the article material; further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
# Intelligent Identification of Trend Components in Singular Spectrum Analysis
## 6 Numerical Investigation
### Robustness of Methods to Exceeding the Signal Rank
#### 6.4.3 Case of Multiple Signal Roots  
Consider Example (9). As discussed in Section 5.6, the EOSSA algorithm is applied to time series with multiple signal roots, and therefore we can include it in the comparison.  
Figure 9 shows the dependence of MSE on the number of components \(t\). It can be observed that the autoSSA and autoIOSSA + FOSSA methods are the most robust, but both produce large errors rendering this robustness useless. Note that autoEOSSA is somewhat unstable; however, it results in the smallest errors.  
It can be seen that the autoSSA and autoIOSSA + FOSSA methods do not work correctly. The autoEOSSA method extracts the trend well, with errors significantly smaller than those of the autoFOSSA method. Likely, the large errors of the autoIOSSA + FOSSA  
Figure 10: The extracted trends and the original trend. Example (9).  
Figure 9: MSE (log) vs number of components \(t\). Example (9).  
method are due to the two elementary components corresponding to the trend have such a small contribution that they are mixed with the noise.
###### Abstract  
With the development of satellite-terrestrial network (STN), mobile edge computing (MEC) servers are deployed at low orbit earth (LEO) satellites to provide computing services for user devices (UEs) in areas without terrestrial network coverage. There is symmetry between satellite networks and terrestrial networks, but there is asymmetry between their resources. Computing resources of satellites' MEC servers may not be enough. The satellite-terrestrial cooperation is promising, where a satellite migrates tasks to a base station (BS) in an adjacent area, thus utilizing computing resources of the BS's MEC server. Although there are some studies on computation offloading in STN, few studies consider a satellite as both a relay and a computing unit to assist UEs in computing tasks. This paper proposes a joint offloading decision and resource allocation scheme in MEC-enabled STN, which minimizes the completion delay of all UEs' indivisible tasks. Firstly, the optimization problem is formulated and decomposed. Then, the proposed scheme based on potential game and the Lagrange multiplier method makes UEs' task offloading decisions and allocates the satellite's and the BS's computing resources, thus obtaining the optimal solution through continuous iterations. Finally, the simulation results validate that the proposed scheme can obtain better gain than other baseline schemes.  
s +
Footnote †: journal: Computer Science  
14564[[https://doi.org/10.3390/sym14030564](https://doi.org/10.3390/sym14030564)]([https://doi.org/10.3390/sym14030564](https://doi.org/10.3390/sym14030564))
# Integrated Hydrological Analysis of Little Akaki Watershed Using SWAT-MODFLOW, Ethiopia
## 3 Results and Discussions
### Groundwater Modeling
Figure 15: Predicted groundwater map under different pumping scenarios (**a**) 10% increase in pumping, (**b**) 25% increase in pumping, (**c**) 50% increase in pumping, (**d**) 100% increase in pumping, and (**e**) 200% increase in pumping. scenarios were analyzed with model estimated results. The pumping scenarios result clearly indicates that increasing the pumping rate considerably altered groundwater dynamics and fluxes. Aquifer outflow from the head-dependent boundary and groundwater flux from the wells is the biggest change observed in response to the increased abstractions. Equal percentage increasing to pumping wells has a different effect on every well because of the abstraction rates are different and well proximity to constant head boundary and rivers. In some wells having a high rate of abstraction at the baseline scenario, imposing additional increasing scenarios significantly change the flow system, and a noticeable decline of groundwater level and drawdown were observed. Considering the possibility of the recharge rate can be increased (if managed well) or reduced due to the changing climate and human effect on the recharge area, both increasing and decreasing recharge scenarios were considered. The considered recharge scenarios are increasing the current recharge rate by 10%, 25%, and 50%, and if the current recharge rate reduced by 10%, 25%, and 50% from the recharge rate estimated (Table 5). The result indicates that increasing and reducing the recharge rate considerably changed groundwater dynamics and fluxes in a reverse way. The result indicated that increasing pumping significantly affect the baseflow out of the watershed, which is equal to aquifer outflow from the head-dependent boundary. Similarly, low recharge also significantly affects the stream baseflow at Little Akaki gauging site.
## 4 Use Case: The Puzzle Pattern Applied to Incremental Refactoring
#### Achieve Interoperability Using Puzzle Pattern, Facade Pattern, and Adapter Pattern  
In complex software systems, achieving seamless interoperability between different components and architectural styles is crucial. The integration of the Adapter and Facade patterns [2] with the Puzzle Pattern addresses this challenge by providing mechanisms to bridge gaps between modern, modular components and legacy systems. These design patterns play complementary roles in ensuring that disparate parts of the system can communicate and function together effectively.  
Integrating the Adapter and Facade patterns with the Puzzle Pattern provides a robust solution for achieving interoperability between disparate system components. The Adapter Pattern plays a crucial role in translating the interfaces of the Puzzle Pattern components to match those expected by legacy systems. This translation ensures seamless interaction and data flow between modern, modularized components and older, monolithic ones. In the Java code 7 in Appendix A (refer to Userjava 4 in Appendix A in order to get an overview of User class), the UserServiceAdapter class exemplifies this by adapting the UserService interface, which follows the Puzzle Pattern, to work with the legacy LegacyUserManager class. The adapter ensures that calls to getUserById and updateUser are correctly routed to the legacy methods, thus bridging the gap between the new and old parts of the system. The Facade Pattern, on the other hand, simplifies the interaction with the system by providing a unified interface that abstracts the complexities of multiple underlying components. In the example, the UserFacade class consolidates operations from both the legacy and the new Puzzle Pattern-based systems. This facade hides the intricacies of whether the underlying implementation is from the legacy system or the new modular system. Clients interact with the UserFacade to perform user-related operations without needing to understand the underlying architecture. This abstraction is particularly beneficial in large systems where different parts may evolve at different rates or where different teams manage different components.  
By combining these patterns with the Puzzle Pattern, we ensure that the system is both flexible and maintainable. The adapter handles the compatibility issues, allowing new components to be integrated without altering the legacy code. The facade simplifies the API exposed to clients, promoting ease of use and reducing the learning curve associated with understanding the system's internal structure. This integration strategy not only enhances modularity but also ensures that the system can evolve gracefully, accommodating new features and technologies without disrupting existing functionalities.
# Design of Chaotic Interleaver Based on Duffing Map for Turbo Code
## 3 Interleaver
### Random Interleaver  
Random interleavers are based on the permutation that originates from the sequence described by a random generator. A random generator forms a noise vector with length L in which the order of the sequence to be permuted is imprinted. This random noise vector can be built by a pseudorandom generator.  
Regarding the use in a turbo code scheme, the interleaver plays an essential role in its operation since, in general terms, its error rate is considerably reduced if the length or the size of the interleaving increases. The reverse is true when the size of the interleaver is small. This problem is directly related to the applications of turbo codes since, although a size increase in the interleaver allows a turbo code to reduce its error rate significantly until converting it into one of the most efficient error control techniques known, this effect is harmful for the system operation because it produces a delay that is unacceptable in several applications [32].
## 6 Conclusions  
This paper investigates the optimization of the throughput for hybrid services in the AloT and proposes an HSCRS strategy based on multi-agent reinforcement learning. Firstly, the channel model in the coexistence scenario of URLLC and mMTC service is analyzed. Secondly, the network throughput of URLLC services is described under a limited code length, and a throughput weighting model is constructed. The proposed method models the throughput optimization problem as a multi-agent reinforcement learning model and iteratively finds the optimal policy in a complex environment. Experiment results show that HSCRS can effectively improve the network throughput performance for mixed services in the AloT. However, this paper focuses on the throughput performance index of URLLC and mMTC mixed services and does not consider the low latency and high reliability of URLLC services. The latency and reliability of URLLC services are also important indicators. In the future, an AloT multi-objective optimization resource scheduling strategy will be designed. Moreover, the current resource scheduling strategy assumes that the channel  
Figure 6: mMTC transmission traffic in HSCRS and RAC algorithm. (**a**) mMTC transmission traffic in HSCRS algorithm. (**b**) mMTC transmission traffic in RAC algorithm.  
quality presents a certain regularity and does not consider the resource optimization scheduling problem of mixed services when the channel quality suddenly changes. In the future, we also plan to introduce a backup mechanism to improve the robustness performance of the resource scheduling strategy.  
Conceptualization, S.L. and P.Z.; methodology, S.L.; validation, S.L., Y.Y. and L.W.; formal analysis, W.P.; investigation, Y.Y.; resources, Y.J.; data curation, Y.J.; writing--original draft preparation, P.Z.; writing--review and editing, L.W.; visualization, P.Z.; supervision,W.P.; project administration,W.P.; funding acquisition, Y.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key Research and Development Project of the State Grid Chongqing Electric Power Company (2022 Chongqing Electric Science and Technology 2\(\#\)) and National Natural Science Foundation of China (Grant 61901071).  
Not applicable.  
The authors declare no conflict of interest.
## 4 Testbed Design
### ML Backends and Models  
PyTorch and TensorFlow are popular deep learning frameworks with distinct features capable of serving as the ML backend for FL deployments. Both libraries are open-source and utilize data flow graphs, where nodes represent mathematical operations and edges represent tensors (multi-dimensional arrays) carrying data. However, they differ in their programming interfaces. TensorFlow, developed by Google, initially adopted a declarative programming model with a static computational graph. On the other hand, PyTorch follows an imperative programming model, allowing computations to be defined and executed dynamically. Despite this distinction, both frameworks enable the seamless deployment of models on CPUs or GPUs without needing code modification.  
To assess their performance on the MNIST dataset, we construct a Convolutional Neural Network (CNN) with connected layers using both PyTorch and TensorFlow. The network architecture includes multiple layers, with six trainable layers: two 2D convolutional layers, two 2D dropout layers, and two linear layers. Additionally, there are non-trainable  
\begin{table}
\begin{tabular}{c c l} \hline \hline
**Metric** & **Category** & **Description** \\ \hline Accuracy & FL/ML & The model accuracy per round \\ \hline Loss & FL/ML & The model loss per round \\ \hline Round Duration & Performance & The overall round duration \\ \hline Overall Duration & Performance & The overall FL duration \\ \hline CPU Utilization & Utilization & The CPU utilization of FL client or server \\ \hline Memory Utilization & Utilization & The memory utilization of FL client or server \\ \hline Network I/O & Utilization & The network data (both incoming and outgoing) \\  & & of FL client or server in bytes \\ \hline Disk I/O & Utilization & Disk I/O of FL client or server in bytes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental Testbed Metric Description.  
layers for activation and transformation operations, such as a ReLU activation layer after the first and second convolutional layers, a MaxPool2D layer following the second ReLU layer, and a Log-Softmax activation function for generating the final output. For CIFAR datasets, we implement the well-known MobileNetV2 model architecture in both TensorFlow and PyTorch. In brief, MobileNetV2 adopts a CNN model commonly used for image classification, comprising 53 layers. More details about the MobileNetV2 architecture can be found in [42].
## 2 Principles and Methodologies  
### Arch-Dam-Reservoir-Foundation Simulation Method  
#### 2.1.1 Westergaard Added Mass Model  
The Westergaard added mass model is generalized by [PERSON] to apply to dam faces of arbitrary shape and to seismic accelerations in all directions [34; 35]. It is formulated as follows:  
\[M_{i}=\frac{7}{8}\rho A_{i}\sqrt{H_{0}h_{i}}l_{i}^{\dagger}l_{i} \tag{1}\]  
where \(M_{i}\) is the added mass at point \(i\) on the dam surface, \(\rho\) is the density of water, \(A_{i}\) is the area of influence at point \(i\), \(h_{i}\) is the water depth at the point \(i\), and \(l_{i}\) is the vector normal to the point \(i\).
## Materials and Methods
### Methods  
#### 2.2.1 Verification of Self-Healing Performance  
In this paper, the self-healing performance of the composite against structural damage was verified from two aspects: mechanical damage and electrical damage. Furthermore, the effect of the microcapsules on the tree discharge and insulation strength of polyethylene were explored.  
In terms of mechanical damage, scratch damage was used to simulate the mechanical structural damage in polyethylene insulating material. Several groups of scratches were carried out with the Tianchuang QHZ scratch tester. Then, the samples were placed in a heating oven to ensure the repair effect. The morphological characteristics of the scratches was observed by optical microscope (OM). Moreover, the alternating current (AC) breakdown strength of samples was carried out to verify the self-healing performance for mechanical damage in electrical insulation more clearly. In addition, the samples were heated at different temperatures and times, to explore the appropriate conditions for the repair reaction. According to the optimum polymerization temperature (about 45 \({}^{\circ}\)C) of the repair agent (DCPD), curing temperature (above 60 \({}^{\circ}\)C) of the reaction product (PDCPD), and normal operating temperature (less than 90 \({}^{\circ}\)C) of the cable, temperatures of 45 \({}^{\circ}\)C, 60 \({}^{\circ}\)C, and 90 \({}^{\circ}\)C were selected in this paper. The heating time was 10 min, 30 min, and 60 min, respectively.  
In terms of electrical damage, the self-healing performance of the composite was verified by electrical tree. The needle-plate electrode system was used to initiate the electrical tree, and the distance between electrodes was 5 mm. The needle electrode was inserted into the sample during the preparation process, while the curvature radius of the needle tip was 5 \(\upmu\)m. The plate electrode contacted the sample through conductive adhesive. Moreover, the sample was immersed in transformer oil to prevent surface flashover. In the experiment, an AC voltage of 7 kV/50 Hz was applied for 90 min. After that, the samples were placed in suitable conditions for heating. The morphological characteristics of the electrical tree were observed by OM.  
Figure 1: Microstructure of the microcapsule/LDPE (low-density polyethylene) insulating composite samples. (**a**) Original technology. (**b**) Modified technology.