esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Summary and Future Work  
In this paper, a means of linking MDAO and an MBSE system model while reusing development data from the MBSE system model components was described. Starting from a system specification, solutions were identified for the system functions to fulfill. The solutions had parameters that determined their physical behavior and requirement satisfaction. Appropriate parameters of the solutions were determined using design workflows. Test workflows were then executed to test the requirements fulfillment of the selected parameters. If requirements were not met, the design variables were changed, and design and test workflows were manually executed again. In this sequence, it remained unclear which design variables had to be changed, and how, to obtain the best possible technical variant.  
To avoid the manual iteration of sequentially executing design and test workflows, an optimization workflow is proposed. It is formalized in the MBSE system model utilizing three model approaches: workflow architecture, internal behavior, and executable behavior. In the workflow architecture, the elements of the optimization workflow are described hierarchically. In the internal behavior, the internal structure, including interfaces and the execution order of the elements, are defined. Existing MBSE system model components are reused to describe the internal behavior. With the help of the model approaches of workflow architecture and internal behavior, the executable behavior of the optimization workflow is implemented. The executable behavior is provided as an action in the activity diagram. The execution takes place in an external tool, but always remains transparent concerning the models used and the principles and the requirements considered. Changes in the requirement values can directly be considered in the execution of the optimization. The optimization workflow approach is demonstrated using the example of the optimization of an electric coolant pump. A conflict of objectives arises between pump installation space and electrical energy consumption requirements, so that spatially compact pumps are less efficient.  
Currently, the executable behavior must be implemented manually from the MBSE system model in an external tool. Future work will focus on an automatic model transformation from internal to executable behavior. Furthermore, the transferability of the approach to other MDAO architectures will be investigated.  
Conceptualization, C.H., G.H., S.N. and J.B.; methodology, C.H., G.H., J.B., S.N. and G.J.; formal analysis, C.H.; writing--original draft preparation, C.H.; writing--review and editing, S.N., J.B. and G.J.; supervision, G.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare
# Matrix Method of Defect Analysis for Structures with Areas of Considerable Stiffness Differences
###### Abstract  
The paper presents an innovative matrix method for defect analysis in heterogeneous structures with significant differences in stiffness parameters. The proposed approach modifies the finite element method (FEM) by introducing a refined type of finite element capable of handling regions with varying material and geometrical properties. Unlike traditional FEM discretization, which increases computational time calculations due to mesh refinement in stiffness-variable areas, the proposed method maintains a consistent discretization while adjusting shape functions to reflect internal stiffness changes. An original functional for modifying linear shape function distributions is introduced, leading to piecewise broken-line shape functions. This enhancement significantly improves the accuracy of numerical calculations, as verified by computational calculations. The explicit formulation of the stiffness matrix enhances computational efficiency, making the approach particularly useful for periodic structures with inclusions, voids, or localized material defects. The results of numerical tests demonstrate that the proposed method provides solutions closely aligned with conventional FEM results while substantially reducing computational costs. The approach is particularly relevant for structural engineering applications where defect analysis and material heterogeneity play a critical role in design and safety evaluation.  
finite element method (FEM); heterogeneous structures; defect analysis; stiffness matrix; shape function modification +
Footnote †: journal: Applied Sciences  
doi: 10.1007/s10048-0001-9999-4
# Machine Learning Based Energy-Efficient Design Approach for Interconnects in Circuits and Systems
## 4 Analysis Results
### Single-Ended Microstrip Line  
#### 4.1.1 Design Structure  
In the interconnect design, the microstrip line is one of the most typical structures due to the simplicity in design and fabrication. However, it takes a lot of time to design and optimize this structure. This structure constitutes one dielectric layer and two metal layers comprised of a ground plane and the signal layer. The signal layer contains one or multiple parallel buses with the channel width (W) plus the distance between them (D) referred to as the physical dimensions. The ratio (\(K_{D}\)) between the space and width is typically about 2-3 to minimize crosstalk. However, the cost is the expansion of the area. They are the main parameters to determine the channel characteristics. The dielectric properties including its height (H) plus the dielectric constant have a significant impact on the channel. The structure geometry is shown in Figure 6.
In addition, SE provides more flexible and stretchable capabilities. Therefore, it would be beneficial for researchers to compare the effectiveness of PDMS with that of SE. As the amount of cross-linker may affect the strength and stability of samples, the samples used in this study were prepared with minimal differences. To the best of our knowledge, studies on the small ratio are still limited, so we investigated the effects of small differences in the curing agent in the cross-linking process in both PDMS and SE. The PDMS network's cross-linking with base to curing agent mass ratios of 5:1, 10:1, 16.7:1, 25:1, and 33:1 had an elastic modulus ranging from 0.57 to 3.7 MPa and was linearly proportional to the amount of cross-linker [12]. A kidney phantom composed of siloxane (silicone) is the most widely used polymer for mimicking physical, optical, and thermal properties [13,14]. [PERSON] et al. successfully fabricated a kidney phantom using silicone elastomer, PDMS, and agarose, and, at the same time, constructed a collecting system and outer mold from 3D-printed models [15]. They proved that a combination of tissue-mimicking material and 3D-printed models can further improve the performance of the phantom for various imaging modalities such as CT scan, ultrasound, and endoscopy. In this study, PDMS and SE were prepared using the standard cross-linking formula by the manufacturer, and mechanical testing was performed. PDMS and SE with different thicknesses were fabricated using different cross-linking methods. Additionally, the dielectric and mean attenuation properties of the PDMS material were quantified. The aim of this study was to formulate a tissue-mimicking material for kidney phantoms because the possibility of underestimated lesions is very high in the kidney, and the radiation dose to the kidney is a primary concern. Apart from that, it is important to distinguish between PDMS and SE because each material has superior flexibility depending on the amount of cross-linker. As rigidity is controlled by cross-linker density, the threshold limit is a vital factor for observing the performance of that material. To the best of our knowledge, most of the phantoms are not tested for material sustainability and effective atomic number (\(Z_{\text{eff}}\)), instead only focusing on their imaging properties. Therefore, we focused on polymer-based phantoms with a specific mechanical property and effective atomic number to determine the most suitable material for kidney phantoms.
# Overview of Distributed Machine Learning Techniques for 6G Networks
## 6 Future Directions
### FL Hybridization with Heuristic and Meta-heuristic Techniques  
Traditionally, various heuristic and meta-heuristic approaches are used to solve complex optimization problems in various fields. The FL-based distributed learning methods are susceptible to various security and privacy-related attacks and can also be modeled as optimization problems. Additionally, the training performance can also be impacted by various factors, such as the number of users, types of datasets, and types of learning algorithms. Recently, the trend of using the heuristic or meta-heuristic approaches to solve the ML issues has been growing. Hybridization-based solutions are being proposed that consider the heuristic/meta-heuristic approaches, along with FL-inspired distributed learning paradigms [81; 82]. In [81], authors proposed a hybridization of meta-heuristic and FL frameworks for increasing detection accuracy and minimizing the vulnerability to poisoning attacks. A meta-heuristic technique to manage the FL process that includes the model aggregation, separation, and evaluation. The results from the image database showed higher accuracy and poisoning attack detection. In [82], authors modified this framework for creating a new framework for selecting image classifiers. They proposed the hybridization approach for FL-inspired distributed policy and augmentation method based upon the heuristic approach. The results show that the augmentation technique proposed can provide reliable data, which can increase the classifier's accuracy level.  
Figure 10: Distributed FL process.  
Such an approach of using heuristic and meta-heuristic techniques to manage and solve the distributed learning-based approaches could be useful in upcoming 6G networks by providing better training accuracy, higher security, etc.
# Experimental Validation of GNSS Interferometric Radio Occultation
## 4 Data Processing, Analysis and Discussion
### Conventional RO Processing
#### 4.2.2 Clean Replica with Open Loop  
GPS signals in the lower troposphere are affected by rapid phase accelerations and severe signal power fading. These signal dynamics often cause the conventional phase-locked loop to lose lock. The open-loop (OL) tracking can be used to overcome this problem.  
The first step of OL tracking is also the _blind_ search of the C/A-code delay and carrier frequency for each 1-sec file. Then, the raw signal samples are cross correlated with the local replica, where the Doppler frequency determined in Section 4.1 has been used. It should be mentioned that the predicted Doppler is assumed to be constant during the processing of each 1-sec signal segment.  
With the output of the cross-correlator \(\mathbf{R}_{z}\left(t_{1},\tau\right)\), the carrier phase is estimated by Equation (14) which is insensitive to the presence of BPSK data modulation.  
\[\phi(t_{1})=\arctan\frac{\text{Im}\left\{\mathbf{R}_{z}\left(t_{1},\tau\right) \right\}}{\text{Re}\left\{\mathbf{R}_{z}\left(t_{1},\tau\right)\right\}} \tag{14}\]  
and the difference between the predicted Doppler and the measured Doppler is obtained by using the evolution of the phase as a function of time  
\[\Delta f_{\text{D}}(t_{1}+T_{i}/2)=\frac{\phi(t_{1}+T_{i})-\phi(t_{1})}{T_{i}} \tag{15}\]  
with the coherent integration time of \(T_{i}=1\) ms, the Doppler difference is then averaged for 1 s to reduce the uncertainty  
\[\overline{\Delta f_{\text{D}}}=\langle\Delta f_{\text{D}}(t_{1}+T_{i}/2+kT_{i })\rangle_{k=0,1...999} \tag{16}\]  
and the Doppler of the received signal can be estimated by  
\[f_{\text{D,obs}}=f_{\text{D,predict}}+\overline{\Delta f_{\text{D}}} \tag{17}\]  
The Doppler observations estimated with the open loop method are presented in Figure 6, in which it can be seen that the open loop tracking method prevents large deviation of the observation from the model. However, Figure 6b shows that the Doppler observations are centered near the predicted Doppler for low SNR. This can be explained by the fact that the Doppler difference in Equation (14) can be assumed to be the noise with a mean of zero, and the Doppler estimated with Equation (17) is the noise with a mean of \(f_{\text{D,predict}}\). In Figure 6 we can also see that the measured Doppler frequency is slightly above the predicted one, during the segments where the open-loop tracking is successful. This is consistent with a setting RO [13].  
Figure 5: Doppler frequency estimated with the carrier tracking loop.
## 5 Evaluation
### Experimental Setup
#### 5.1.3 Baseline Methods  
In the evaluation, we use two typical state-of-the-art frameworks and a local approach as our baselines. (1) CoEdge [37]. CoEdge achieves parallel computing by partitioning input data for each network layer. After the execution of each network layer, a synchronization  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Workers** & **Cores Number** & **Out-Bandwidth (MB/S)** \\ \hline worker 1 & 3 & 1500 \\ worker 2 & 9 & 1750 \\ worker 3 & 7 & 1250 \\ worker 4 & 5 & 2000 \\ worker 5 & 7 & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Environment parameters.  
Figure 7: This is the computing workload distribution of VGG16 and AlexNet. We use two colors to differentiate the network layers that belong to the feature extraction part and the classification part, blue and yellow. The computing workload of the feature extraction part is significantly higher than that of the classification part.  
wait is performed, and then each device transmits padding data to one adjacent device to ensure the correctness of the computation. (2) EdgeFlow [48]. EdgeFlow also performs data partitioning on each network layer to achieve parallel computing. To shorten the inference latency, it determines the data partitioning scheme layer by layer based on the partitioning results of the previous network layer. (3) Local. The local approach executes the whole inference task at the requester.
## 4 Results
### Analysis and Description of Usage among the Pilot Groups
In a second place, by measuring the usage through the factor 'number of activities', results showed significance at pilot groups in all the worlds: Artistic, F(2) = 52.550, \(p\) = 0.001 (\(<\)0.05), np2 = 0.053, \(p\) = 0.921; Emotion, F(2) = 5.304, \(p\) = 0.006 (\(<\) 0.05), np2 = 0.042, \(p\) = 0.834; Logic, F(2) = 10.525, \(p\) = \(<\) 0.005, np2 = 0.079, \(p\) = 0.988; Science, F(2) = 7.452, \(p\) = 0.001 (\(<\) 0.05), np2 = 0.058, \(p\) = 0.940; Literacy, F(2) = 4.055, \(p\) = 0.019 (\(<\) 0.05), np2 = 0.032, \(p\) =.718; Spatial, F(2) = 8.828, \(p\) = \(<\) 0.005, np2 = 0.067, \(p\) = 0.970; Multiplayer, F(2) = 9.557, \(p\) = \(<\) 0.005, np2 = 0.073, \(p\) = 0.980. In this sense, significance can be observed in the groups of classes with the Logic world, F(2) = 3.103, \(p\) = 0.047 (\(<\) 0.05), np2 = 0.023, \(p\) = 0.594. These results show differences in the activities performed along the worlds between different regions. This could be explained by the differences in usage time according to the activities done. Similarly, another explanation for this result could be the preference (or form of use) of the different activities by teachers in the classroom. To conclude this point, there appeared to be neither significance for the factor'scores' among games and the number of games played by pilot groups, nor by 1 st and 2 nd cycles. That is to say:there were no differences in student performance based on the percentages of successes achieved in the activities. In general, a high percentage of successes was observed, which affected the motivation for the correct performance, both in games and in quizzes. This homogeneity showed a constant use between different schools and regions. However, there was a significant difference for the factor'scores', F(2) = 4,429, \(p\) = 0.013 (\(<\)0.05), \(\eta\)p2 = 0.027, \(p\) = 0.760; and number of quizzes performed, F(2) = 3,359, \(p\) = 0.036 (\(<\)0.05), \(\eta\)p2 = 0.020, \(p\) = 0.632; among the three pilot groups. This may indicate that those teachers with more traditional teaching methods preferred to analyze the progress of their students by quizzes of specific questions.
## 5 Field Data Analysis  
In this section, an actual data analysis is conducted for static and dynamic scenarios to validate the proposed SQM profiles and examine the sensitivity of the SQM approach in real multipath environments. These scenarios were carefully designed and considered to provide controlled conditions on the received signals affected by multipath corresponding to the discussion presented in Section 4.
# Multi-Object Detection for Inland Ship Situation Awareness Based on Few-Shot Learning
## 3 Detection and Tracking for Inland Ship Based on Few-Shot Learning Framework
This profound fusion of technologies equips us with the capability to better adapt to the multifaceted challenges posed by ship tracking across diverse environmental conditions. Furthermore, we employ transfer learning, harnessing the knowledge gleaned from extensive annotated datasets, and seamlessly integrating it into our ship detection and tracking paradigm. This strategic approach serves to enhance the model's generalization prowess, resulting in superior performance across specific task domains. The amalgamation of the DeepSORT algorithm further augments our system's capabilities, enabling real-time target tracking within streaming video feeds, while concurrently affording the provision of highly accurate trajectory information. Our proposed framework demonstrates a remarkable ability to achieve high precision and recall rates even with a limited dataset, attributed in part to its foundation on the YOLOv5s network, renowned for its swift inferencing capabilities and relatively compact parameter size. It is worth noting that in comparison to LiDAR-based detection and tracking, our image-based approach is less susceptible to errors arising from water surface reflections. However, it is prudent to acknowledge that image-based detection may encounter reduced precision and recall rates under deteriorating lighting conditions, stemming from inherent hardware limitations within image sensors. Nevertheless, this limitation can be ameliorated through the application of various image processing techniques and technologies in subsequent phases.
# Towards a Reliable Evaluation of Local Interpretation Methods
## 1 Introduction
(**B**) shows the results of different interpretation methods for the same model and the same prediction result (From left to right: Grad-cam, Mask, Rise). Different interpretation algorithms give different reasons for prediction, which explanation should users trust and which one is more convincing? friendly to humans, because no matter how the explanation method is designed, if the explanation is unreasonable or meaningless to the user, then this explanation method loses its value. Note that in this paper we mainly focus on human-based metric. In order to evaluate human satisfaction with different interpretation methods, we design an \"object recognition game\" to measure the degree of user satisfaction with different interpretation methods by occluding the saliency maps. In this paper, we first define three attributes that a human-friendly explanation should satisfy: high accuracy, high persuasibility and high class-discriminativeness. We then manually construct a set of datasets so as to comprehensively evaluate the performance of different interpretation methods. In order to bridge the gap without ground-truth, we use the idea of human annotation to solve the problem. First, the input samples are preprocessed by super-pixel segmentation, and then the user is allowed to select important feature blocks. To the best of our knowledge, we are the first to calibrate key feature blocks in the pixel space of the input image by manual annotation method. For persuasive performance, we design an interesting object recognition game to evaluate different interpretation methods by the user study method. In addition, we use point games to measure the class-discriminativeness of interpretation methods. To summarize, the main contributions to this paper are:  
* We innovatively constructed an evaluation data set with three branches to comprehensively measure the performance of different visual interpretation methods. The dataset and human-grounded benchmark are available at this link: [[https://github.com/muzi-8/Reliable-Evaluation-of-XAI](https://github.com/muzi-8/Reliable-Evaluation-of-XAI)]([https://github.com/muzi-8/Reliable-Evaluation-of-XAI](https://github.com/muzi-8/Reliable-Evaluation-of-XAI)), accessed on 10 March 2021. * Using the manual feature annotation method, we measure the correctness of the visual interpretation method through the important feature blocks obtained. * Through carefully designed object recognition games, we effectively measured the persuasiveness of the three visual interpretation methods. The reminder of this paper is organized as follows. The two lines related works are introduced in Section 2. The proposed evaluation method is presented in Section 3. Section 4 demonstrates implementation details and experimental results. Section 5 discusses and analyzes the experiment results. The conclusion is drawn in Section 6.
# Heuristic Global Optimization for Thermal Model Reduction and Correlation in Aerospace Applications
## 5 Discussion
### Implications for Future Research  
This work establishes a robust global heuristic framework for thermal model reduction and correlation. To broaden its applicability and validate its performance under realistic conditions, future investigations should explore the following directions:  
* Laboratory Validation. Develop and instrument a representative thermal test setup (e.g., a multi-plate panel or electronics mock-up) equipped with temperature sensors. Conduct experiments in a controlled environment and compare the measured temperature profiles with predictions from both the DTMM and RTMM to evaluate accuracy and convergence behaviour.
* Larger Model Scaling. Apply the reduction methodology to more complex aerospace thermal networks, such as complete satellite buses or payload assemblies with hundreds of nodes, to assess scalability, accuracy, and computational efficiency.
# A DQN-Based Multi-Objective Participant Selection for Efficient Federated Learning
## 3 System Model  
To improve the model training rate of IoT devices and reduce the communication cost of model aggregation in federated learning (FL), a deep reinforcement learning (DRL)-assisted FL framework is proposed, which leverages existing artificial intelligence (AI) techniques. The DRL algorithm is well-suited for solving high-dimensional decision problems, which makes it ideal for selecting high-quality local IoT device models for aggregation based on their decision-making capabilities [30]. In [31], the authors leverage the Deep Deterministic Policy Gradient (DDPG) to find the optimal solution for node selection in the asynchronous federated learning. In [32; 33], the authors propose a node selection algorithm based on Distributed Proximal Policy Optimization (DPPO) to solve the optimization problem. These methods have limitations in exploring non-deterministic policies and ensuring stability in discrete action spaces. In this paper, we propose a DRL-assisted FL algorithm for federated learning devices that balances data privacy and efficiency. We use MNIST and CIFAR-10 datasets to represent the data generated by industrial IoT, leveraging the data features of federated learning devices. Unlike traditional FL distributed training architectures, we improve the model aggregation module by incorporating DRL-based node selection, which enables us to select devices with strong computational power and high training quality for model aggregation before weight aggregation, thereby improving the FL performance. The system architecture we propose is depicted in Figure 1.  
Figure 1: DRL-based FL architecture.