esa-sceva/satcom-chunk-collection huggingface dataset:
# Systematic Literature Review of MBSE Tool-Chains
## 4 Discussion
### Detailed Analysis from Findings
#### 4.2.4 Assessment of MBSE Tool-Chains  
Figure 17 evaluates the metrics of the MBSE tool-chains. The results show that the metrics of _integrated capability_, _interoperability_, and _traceability_ are mentioned in more than 130 papers. The integration capability is the tool-chain's function for managing the tools' interconnections, interfaces, relationships and dependencies, and the processes of requirement management, architecture design, interface definition, and verification & validation. Integration capability increases the automation of system development; for example, mathematical simulation tools and system modeling tools were integrated to support automatic verification in control systems [35]. Moreover, integration capability improves design efficiency, as, for instance, in a model-driven embedded system design flow integrating the SysML and SystemC-UML profiles to realize hardware-software co-design [36]. In addition, integration capability is also important for system analysis; for example, a novel approach was presented to address functional safety requirement verification, in which  
the authors integrated functional tests as full-fledged components into a model-based architecture using OpenModelica [24].  
Interoperability emphasizes the model and information interaction in an MBSE tool-chain, which is a key impact factor when describing data in a unified way. For example, [PERSON] and [PERSON] et al. proposed a unified ontology approach for transforming product-related data and aggregated the ontologies of different data sources into a holistic product service system [37]. Interoperability is also an important attribute for developing middleware. For example, when connecting the data of system design to operational robotics, good interoperability facilitates model-driven development in robotics to promote the development efficiency based on a tool-chain [38].  
Traceability refers to the ability through which an element is associated with other related elements, such as verification models that are traced to the related requirements in MBSE tool-chains. Traceability can be used to implement project and process management and to support automation; for instance, [PERSON] et al. provided the integrated platform for the life-cycle process management of MBSE and PLM data with traceability management [39]. Moreover, traceability enables the management of requirements; for example, a recursive hierarchy was used in a requirements management system to make the system traceable from decision-making to its success criteria [40].
# Carbon Footprint Assessment on the Viability of Utilizing Brewer's Spent Grain to Produce Biochar
## 4 Discussion
### Commercial Applicability of BSG-to-Biochar Process  
The recommended BSG-to-biochar approach is primarily applicable to breweries that have the potential to expand their energy sources in their warehouse and those that do not have access to local farmers who can utilize the BSG as cattle feed.  
Ideally, this approach could be adapted by a cooperative set of craft breweries located in one condensed urban area. In this setting, farmers would be less inclined to drive into the area and pick up the BSG, making the diversion to animal feed unattainable for these  
Figure 4: GWP impact assessment.  
breweries. A centralized biochar production facility would enable the craft breweries to send their BSG for processing, ultimately reducing the cost and material expenditure associated with transporting unprocessed BSG offsite, far out of the urban area, to a landfill or composting facility. Ideally, caustic waste from the craft brewery closest to the processing facility would be utilized to enhance the biochar. Utilizing caustic waste in processing removes the need to neutralize chemical waste prior to draining and decreases the total wastewater produced in brewing facilities. Once processed, the biochar should be sent for soil amendment at a barley agriculture site because soil amendment is the most effective use of biochar for GHG reduction. The weight of BSG biochar is significantly less than that of BSG; therefore, transportation of finalized biochar offsite from the centralized processing facility to a barley farm would be less resource-intensive than transporting unprocessed BSG. Efforts should be made to minimize the distance for transporting biochar to barley growers.  
At a BSG-to-biochar processing facility, the syngas released during biochar production should be captured and utilized as an energy source. This would decrease the required energy input, thereby improving the overall energy requirements and consequently reducing the GHG emissions associated with the biochar production process. Moreover, the syngas can also serve as an energy source to clarify the bio-oil produced during BSG processing if the bio-oil is being refined within the same facility. The clarified crude oil then has applications for gasoline, diesel fuel, and heating oil production, which can be utilized in-house.
# Inertial Sensors-Based Assessment of Human Breathing Pattern: A Systematic Literature Review
## 3 Data Analysis, Results, and Discussion
### Which Classifiers Are Being Employed and Perform Best in Detecting Adult Human BPs?  
A wide range of classification techniques have been employed to detect and classify BPs using inertial sensors. Long Short-Term Memory Recurrent Neural Networks (LSTM-RNNs) showed promise in achieving high accuracy, especially in classifying the severity of breathing disorders. Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs) have also demonstrated strong performance in various studies. These findings suggest that machine learning approaches can be effective in automating the detection and classification of BPs based on inertial sensor data. However, it is essential to choose the appropriate classifier based on the specific research objectives and data characteristics.
# A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems
## 1 Introduction  
In recent years, recommendation systems have become a popular tool to address information overload in many real-world fields such as news, E-commerce, and healthcare [1; 2; 3]. This requires collecting a large amount of sensitive information about users, such as user attributes, social relations, contextual information, and behaviors [5]. Unfortunately, a central server is needed to store these data, leading to the potential risks of data privacy leakage, such as selling user data to a third party without consent or malicious attackers stealing the data [6]. Additionally, due to privacy concerns and regulatory restrictions, it is difficult to integrate data from other platforms to improve the performance of the recommendation system [7]. For example, the General Data Protection Regulation (GDPR) has set strict rules for collecting user data and sharing data between different platforms, which can lead to an inadequate amount of data for the recommendation system, thus decreasing its performance [8].  
To overcome this challenge, Google introduced federated learning, a privacy-preserving distributed learning scheme that allows participants to collaborate to train a machine learning model without exchanging real data [9]. Instead, intermediate parameters such as model parameters and gradients are exchanged between participants. This has opened up a new field of application for recommendation systems known as federated recommendation systems (FRSs), which combine federated learning with recommendation systems for privacy-preserving recommendation systems [10]. In Figure 1, we present the generic training procedure of FRSs.
## 5 Conclusions  
1. The motion of the end-effector is mapped to the joint (driving limb) motion of the robot through kinematic analysis, and the path planning and trajectory planning are completed in the robot joint (driving limb) space, which effectively avoids the singularities and obstacle avoidance issues that may arise in Cartesian space trajectory planning. Planning in the robot's joint (driving limb) space allows for better control of the robot's motion and avoids situations where the robot is unable to move. Additionally, joint (driving limb) space planning can better consider the robot's motion constraints and dynamic characteristics, improving the accuracy and reliability of trajectory planning.  
2. An intelligent optimization algorithm suitable for trajectory planning is proposed: the improved quantum particle swarm algorithm, which effectively reduces computational complexity and search time by optimizing search strategies and parameter settings. Additionally, intelligent optimization algorithms can better adapt to different trajectory planning requirements by optimizing according to different objective functions and constraints, thereby improving the accuracy and reliability of trajectory planning.  
3. After trajectory planning, the error level of each driving limb position is about \(10^{-3}\) mm, which meets the actual requirements of the control process. After planning, the limit velocity and limit acceleration values of the whole motion process are within the allowable range of the parallel robot motion performance.  
4. The key common problems of the industrial application of the robot system in the in situ manufacturing system are solved, which provides support for the concrete implementation and application of the in situ manufacturing system.  
[PERSON], [PERSON] and [PERSON] conceived the presented idea. [PERSON] and [PERSON] established an overall paper research framework and the model. [PERSON] and [PERSON] conducted data calculation for the overall paper. [PERSON] supervised the findings of this work. [PERSON], [PERSON] and [PERSON] collected and provided the model data. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Young Doctor Foundation Project of higher education institutions in Gansu Province (zz2022T50200004), the Science and Technology Project of the Gansu Province Natural Science Foundation (22 JR5 RA291), open Fund of Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology (ZZ2023T50200002) and the Natural Science Foundation of Sichuan, China. (2022 NSFSC1975).  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 17: Position and posture error of moving platform trajectory planning of the parallel robot.
## 3 Experimental Results and Discussion  
### Image Segmentation Threshold Calculation and Result Analysis  
In this section, the optimal threshold for matrix and seedling region segmentation is obtained by experimental methods. From the dataset consisting of 500 images, 10 images were randomly selected as Training Set 1, and the image segmentation threshold was calculated using the maxIOU method. The experiment result is shown in Figure 11A,B: The maximum IOU of the substrate region was 0.873, and the corresponding segmentation threshold was 24. The maximum IOU of the seedling region was 0.790, and the corresponding segmentation threshold was 21. Combined with the analysis in Section 2.4 (see Figures 7 and 8), the color component interval corresponding to the segmented substrate region was [0; 24] in the G channel, and the color component interval corresponding to the segmented seedling region was [21; 255] in the excess green color space.  
Figure 10: Substrate segmentation. (**I1**) The normal cabbage seedling image; (**I2**) the substrate damaged cabbage seedling image; (**A,B**) substrate segmentation of the cabbage seedling image; (**A-1,B-1**) the upper part of the segmented substrate area; (**A-2,B-2**) the lower part of the segmented substrate area.  
A verification experiment was designed to verify the generalization performance of the maxIOU method. Fifty images that were different from Training Set 1 were randomly selected from the dataset to verify the algorithm's segmentation effect. The segmentation threshold calculated in the previous paragraph was used to segment the images. The experiment result is shown as follows: The average IOU of the substrate area was 0.854, and the average IOU of the seedling area was 0.820. Image segmentation results are shown in Figure 12. The experiment obtained the conclusion that the thresholds searched by the maxIOU method could well achieve the segmentation of the substrate and seedling region.
## 3 Method
### Loss Function and Judgment Method  
The proposed model is able to effectively capture the distribution of normal data through three jointly optimized loss functions. To that end, the first part is adversarial loss \(L_{adv}\), which employs feature matching to ensure that the distribution of reconstructed spectrogram closely matches the original one by computing the \(L_{2}\) distance between them in the discriminator's latent space. Its definition is  
\[L_{adv}=\mathbb{E}_{X\sim p}\big{\|}\mathcal{D}(X)-\mathcal{D}(\hat{X})\big{\|} _{2}. \tag{16}\]  
Second, the spectrogram loss \(L_{s}\) computes \(L_{1}\) distance between the reconstructed and original log-mel spectrograms, given by  
\[L_{s}=\mathbb{E}_{x\sim p}\big{\|}X-\hat{X}\big{\|}_{1}. \tag{17}\]  
Lastly, the latent representation loss \(L_{l}\) ensures latent space consistency through a dual-encoding mechanism in normal data, which can be expressed as  
\[L_{l}=\mathbb{E}_{x\sim p_{X}}\big{\|}G_{E}(x)-E(G(x))\big{\|}_{2}. \tag{18}\]  
The total generator loss is a weighted sum of these three components, given by  
\[L_{G}=w_{1}L_{adv}+w_{2}L_{s}+w_{3}L_{l}. \tag{19}\]  
In our experiments, the weights are set as \(w_{1}=1\), \(w_{2}=50\), and \(w_{3}=1\), following the previous work [10].  
During the inference phase, the model computes an anomaly score to detect abnormal samples via forward propagation. The anomaly score is defined as the \(L_{1}\) distance between two latent representations:  
\[\mathcal{A}(X)=\big{\|}Z-\hat{Z}\big{\|}_{1}. \tag{20}\]  
The core to optimize the model to detect anomalous samples despite being trained only on normal samples lies in its explicit learning for the distribution of normal data through adversarial training and a dual-encoding mechanism. The generator is optimized to reconstruct the normal samples with a low error while maintaining consistency in the latent space. In contrast, anomalous samples, which deviate from the learned distribution, result in significant distortions in the reconstructed feature map and show notable encoding discrepancies in the latent space. By monitoring the reconstruction error in the latent space, the model detects anomalies when this metric simultaneously exceeds a predefined threshold, which essentially leverages the \"misfit\" of the model to unknown distributions as an anomaly signal, analogous to how humans identify irregularities based on experience.  
To evaluate the model's overall anomaly detection capability on the whole test set, we follow the methodology established in prior research [10]. For each test sample \(X_{i}\) in the dataset \(\mathcal{P}\), an anomaly score \(s_{i}=\mathcal{A}(X_{i})\) is computed, generating a score set \(S=\{s_{i}:\mathcal{A}(X_{i})|X_{i}\in\mathcal{P}\}\). These scores are then normalized to a \([0,1]\) interval using min-max scaling:  
\[s_{i}^{\prime}=\frac{\mathcal{A}(X_{i})-\min(S)}{\max(S)-\min(S)}. \tag{21}\]This rescaling ensures probabilistic interpretability. A predefined threshold \(t\) is subsequently applied to classify samples as anomalous or normal.
## 2 Theoretical Background
### Magnetostriction Noise  
Magnetostriction is a physical phenomenon characterized by the dimensional alteration of ferromagnetic materials when excited by magnetic fields [9]. It occurs in magnetic circuits of electrical machines in general and, in particular, in power transformers. More specifically, in the case of power transformers used in high-voltage electrical substations, the acoustic noise is generated by the periodic expansion and contraction of the steel plates that form the laminated core of the transformer, due to the magnetic field that is induced by the flow of alternating electric current through its coils.  
This phenomenon of expansion and contraction occurs with double the frequency of the alternating current. For the operating conditions of the Brazilian electrical network, acoustic noise is generated with a characteristic frequency of 120 Hz, in addition to some harmonics (typically, 240 Hz and 360 Hz), and frequencies in the audible range (there may be a variation of these frequencies around nominal values, typically \(\pm\)0.2 Hz). Although the magnetostriction noise from transformers can be attenuated by means of a specific project for restructuring the transformer, this phenomenon is intrinsic to its operation and cannot be completely eliminated.  
Thus, the noise of interest is continuous, so \(L_{Aeq,sp}=L_{95}\) in the cases that the background noise is not high enough to mask the specific noise from the substation. In scenarios with a background noise too high, no assessment can be made. A corrected noise level, \(L_{R}\), is then defined based on \(L_{Aeq,sp}\), taking into account the possible penalty due to the presence of tonal noise, so that that \(L_{R}=L_{Aeq,sp}+K_{T}\) if there is tonal noise, and \(L_{R}=L_{Aeq,sp}\) in the absence of tonal noise.
###### Abstract  
Moving load and structural damage assessment has always been a crucial topic in bridge health monitoring, as it helps analyze the daily operating status of bridges and provides fundamental information for bridge safety evaluation. However, most studies and research consider these issues as two separate problems. In practice, unknown moving loads and damage usually coexist and influence the bridge vibration synergistically. This paper proposes an innovative synchronized assessment method that determines structural damages and moving forces simultaneously. The method firstly improves the virtual distortion method, which shifts the structural damage into external virtual forces and hence transforms the damage assessment as well as the moving force identification to a multi-force reconstruction problem. Secondly, a truncated load shape function (TLSF) technique is developed to solve the forces in the time domain. As the technique smoothens the pulse function via a limited number of TLSF, the singularity and dimension of the system matrix in the force reconstruction is largely reduced. A continuous beam and a three-dimensional truss bridge are simulated as examples. Case studies show that the method can effectively identify various speeds and numbers of moving loads, as well as different levels of structural damages. The calculation efficiency and robustness to white noise are also impressive.  
structural health monitoring; moving force identification; structural damage identification; load-shape function method; virtual distortion method +
Footnote †: journal: Applied Sciences  
1
# A Multipath Data-Scheduling Strategy Based on Path Correlation for Information-Centric Networking
## 5 Evaluation
### Basic Performance Evaluation
#### 5.1.2 The Effect of Loss Rate on Performance  
In this subsection, we set the default bandwidth and delay of the path E1-R3-R4-E2 to 100 Mbps and 5 ms, respectively. In addition, we set the default packet loss rate of E1-R3-R4-E2 as 0.01%, 0.02%, 0.03%, 0.04%, and 0.05%, respectively, to analyze the impact of packet loss rate differences on transmission performance. Similarly, we have source node A establish a multipath connection with destination node B and transfer 100 chunks.  
Figure 7 shows the relationship between the average throughput and packet loss rate differences of the four scheduling strategies. As the packet loss rate of the path E1-R3-R4-E2 increases, the average throughput of the methods decreases gradually, and the throughput of ARDS is always lower than that of the other three methods. FDPS estimates the path state by using the forward delay, thus increasing the throughput compared to LRFDS; however, the improvement is not significant. In addition, compared with the other methods, the proposed PSDS can maintain the highest average throughput. This is because PSDS considers both RTT and packet loss rate so that it can accurately perceive the real path state.  
Figure 8 shows the proportion of out-of-order chunks under the different packet loss rates of the four scheduling strategies. As the packet loss rate of path E1-R3-R4-E2 increases, the proportion of the methods increases gradually. This is because a high packet loss rate will result in a large number of retransmissions and a reduction in throughput. Compared with other methods, the proposed PSDS can maintain the proportion of out-of-order chunks at a low level when the path packet loss rate differs greatly.  
Figure 6: Proportion of out-of-order chunks under different link delays.  
Figure 7: Average throughput under different packet loss rates.
## 1 Introduction
#### Contributions  
* This paper proposes a new precoding-companding technique to reduce BER and OOB radiation.
* The proposed technique outperforms state of the art precoding-companding techniques in terms of BER, EVM, and OOB-radiation reduction.
* The proposed technique reduces EVM by 15 dB and increases HPA efficiency by 11.4% in contrast with the best-known technique (i.e., SRC-\(\mu\)) that reduces EVM by 10 dB and increases HPA efficiency by 9.88%.
* The proposed technique also achieves PAPR reduction gain better than PTS, the most powerful PAPR reduction technique with a 99% reduction in the required computational complexity.  
The paper is organized as follows: Section 2 discusses the proposed technique's background and problem description. Section 2.1 highlights the importance of PAPR reduction in OFDM systems. Section 2.2 describes criteria for PAPR reduction techniques. Section 3 presents the proposed technique for PAPR reduction (i.e., SRC-Log), and simulation results are presented in Section 4. Finally, Section 5 concludes the paper.  
_Notations:_ Scalars are represented by small italic letters. Small and capital letters refer to time and frequency domains, respectively. Matrices are represented by bold normal letters. Subscript \(n\) and \(k\) refer to the time sample and subcarrier index, while superscript \(c\) and \(e\) denote companded and expanded signals, respectively. Variable \(t\) and \(f\) denote the time instant and frequency component in the continuous domain, respectively. Operators \(|\cdot|\), \(\angle\), \(\widehat{\cdot}\), \(\mathbf{E}\{\cdot\}\), and \(Re\{\cdot\}\) denotes absolute, angle, estimated value, average value, and real part of complex value, respectively. \(P\{\cdot\}\) is used to denote probability density functions (PDF). Finally, \(j=\sqrt{-}1\).