esa-sceva/satcom-chunk-collection huggingface dataset:
# Sensitivity Analysis and Filtering of Machinable Parts Using Density-Based Topology Optimization
## 1 Introduction
### Filtering: The Density Filter  
The density-based method employs the Finite Element Method (FEM, see step 3 in Figure 1) and the SIMP method to assign each element a relative density value ranging from 0 to 1. The classical approach is finding a black and white layout (zeros and ones) that minimizes the compliance of the structure. A maximum volume fraction in relation to the original volume of the design domain is usually utilized as the upper limit optimization constraint [6].  
Nonetheless, even with SIMP, mesh dependency, checkerboard, ill-posed, or local minima results can still arise [20]. To address these issues, the density filter [21] is employed in this study. A basic formulation of this filter is presented in Equation (2):  
\[\widetilde{\mathrm{x}}_{i}=\frac{\sum_{j\in\mathbf{N}_{i}}H_{ij}v_{j}x_{j}}{ \sum_{j\in\mathbf{N}_{i}}H_{ij}v_{j}} \tag{2}\]  
where \(\widetilde{\mathrm{x}}_{i}\) is the filtered density of an element, \(\mathbf{N}_{i}\) represents the neighborhood of a specific element, \(v_{j}\) is the volume of the element \(i\), and \(H_{ij}\) is a weight factor. The neighborhood of an element is defined as all the elements whose center is within a certain distance \(R\) from the centre of the element in analysis. This can be defined as in Equation (3):  
\[\mathbf{N}_{i}=\{j:\mathbf{dist}(i,j)\leq R\} \tag{3}\]  
where \(\mathbf{dist}(i,j)\) is the Euclidean distance between elements \(i\) and \(j\). The weight factor \(H_{ij}\) can be defined as in Equation (4):  
\[H_{ij}=R-\mathrm{dist}(i,j) \tag{4}\]  
Figure 2: Graphical representation of the SIMP interpolation power rule for penalization values between 1 and 100.  
where is element j, which is always inside neighborhood \(\rm N_{i}\). The constant R is defined as the filter radius and is the second parameter used in the sensitivity analysis throughout this paper. Now, \(\rm\widetilde{x}_{i}\) from Equation (2), known as the filtered density of the element, is substituted in the SIMP (Equation (1)) as in Equation (5):  
\[\rm E_{i}=E\left(\rm\widetilde{x}_{i}\right)=E_{min}+\rm\widetilde{x}_{i}^{ \rm P}\left(E_{0}-E_{min}\right) \tag{5}\]
# Constructing Reliable Computing Environments on Top of Amazon EC2 Spot Instances +
## 4 Evaluation Scenario
### SI Interruption Description  
In a recent study, [PERSON] et al. (2014) conducted a comprehensive experimental evaluation of Amazon EC2 SIs to characterize their performance and behavior in three different regions (each in a different continent): Frankfurt (F), North Virginia (NV), and Sao Paulo (SP). A broad study of 3840 EC2 spot requests based on a model for the life cycle of a SI resulted in a variety of findings. These findings revealed spot request fulfillment rates of 99.2%, 75.0%, and 99.8%, in F, SP, and NV respectively. Regarding the waiting time of fulfilled spot requests, the study showed that 90.5%, 80.1%, and 89.5% of spot requests were fulfilled within four seconds in F, SP, and NV respectively. In most other cases, users had to wait for more than 60 s. Additionally, F fulfilled 100% spot requests in less than 1.38 h, whereas NV required 3.96 h to fulfill all spot requests. In turn, SP had the highest rate of long waiting times (more than 60 s). Once an SI is provisioned and deployed, it can be interrupted by EC2 after no-capacity (revocations have not been directly correlated with spot prices since early 2018, when EC2 changed how the price is set such that it now only reflects long-term changes in supply/demand and not the current price). Characterizing SI interruptions, authors reported that F and NV had similar interruption rates of approximately 12.5%, while SP resulted in 34.0% interruptions. Regarding the running time of deployed SIs with interruption, half of all interrupted SIs ran at least three hours in F, less than two hours in NV, and less than 1.5 h in SP. Additional examination concerning how long SIs run until interruption indicates that 93.5% and 91.3% of SIs run at least 30 min in F and NV, respectively. In SP, 95.2% of SIs run at least the first 20 min. Based on these findings, we generated a set of reliability periods which were then assigned to SIs launched by the CSP. We also generated a set of waiting times for fulfilled spot requests. In generation of the two sets, we considered the region with the worst performance and behavior (i.e., SP) to deeply test the effectiveness of our solution.
# Similarity Calculation via Passage-Level Event Connection Graph
## 5 Experiments and Analyses
### Experimental Results
#### 5.2.1 Testing on Threshold  
In Section 4, we provide a vector tunning-based method to find semantically similar triggers and link them to form a comprehensive event connection graph. Two triggers whose semantic similarity value is beyond the threshold (set as 0.8) can be connected in the graph. The following figure just demonstrates and explains the rationality of the threshold setting. It shows \(F1\) values when the threshold changes from 0.1 to 1.0. This experiment is designed to see the rationality of the chosen of the threshold setting.  
As shown in Figure 5, the calculating results change along with the variety of the value of the threshold. All the curves have the similar trend across different corpora. They all reach the perk at the value of 0.8 (or close to it). The reason can be explained based on the principle behind word embedding. For most of word pairs, if two words are semantically similar, their pre-trained vector representations are close. The vector representation of trigger is initialized via GloVe, one kind of word embedding; thus, we can take the value between two triggers measured by vector distance to decide whether two triggers are semantically similar or not. Trigger decides event type. Two similar triggers just indicate two related or similar events. However, as indicated in Section 4.1, due to the situation that there are some commonly used collocations in linguistics, the previous conclusion (similar triggers have close vectors) is not always true. Thus, in Section 4.1, we tune the pre-trained vector representations of the triggers via the training data sampled from synonym dictionaries to make semantically similar triggers own close vectors. Based on the tuning operation, finding a threshold to decide whether two triggers are similar becomes feasible. As shown in Figure 5, 0.8 is a reasonable choice, where the performance curves reach the peak through all the testing corpora. When the threshold exceeds 0.8, the performance curves even drop. This is because when the threshold enlarges, some similar triggers are missed to be connected. The relations among events, especially the relations among the semantically similar events, cannot be fully covered by the event connection graph. It finally causes that the extracted event may not be the core event. Furthermore, missing the connections between similar events, the extracted event cannot integrate the information carried by the entire event connection graph. These two situations lead to the drop of the performance curves.
# Intrinsic Interference Use for FBMC-IOTA Systems
###### Abstract  
In this paper, the intrinsic interference of filter bank-based multicarrier systems (FBMC) systems with isotropic orthogonal transfer algorithm (IOTA) pulse-shaping is analyzed and used. Such intrinsic interference is treated as a parity symbol, and an iterative soft-in-soft-out (SISO) detector, which is based on message-passing algorithm (MPA), is proposed to exploit the useful information of the intrinsic interference. The performance of the intrinsic interference user (IIU) is investigated.  
Keywords:filter bank multicarrier; isotropic orthogonal transfer algorithm; intrinsic interference +
Footnote †: journal: Applied Sciences
## 5 Experiments and Analysis of Results
### Chinese and English Punctuation and Choice of Symbol Types
symbols in English punctuation has a positive effect on the model without affecting the semantics of the sentences, while the choice of too many English symbols still does not provide any additional benefit to the model. In summary, the introduction of Chinese punctuation does not improve the model performance as much as English punctuation and may even cause trouble for the model to understand the semantics of sentences, such as in the roberta-base model, while English punctuation such as exclamation marks and question marks, which have a strong tone of voice, does not have a negative impact on the model.
# The Key Role of Laser Ultrasonics in the Context of Sustainable Production in an I 4.0 Value Chain
## 3 Quality Control Using In-Situ Methods
### In-Situ Methods for R&D Purposes  
Approaches for the in-situ investigation of materials on a laboratory scale are widely existent. In order to characterize microstructural changes, the limiting factor is primarily the solution of applying measurement methodologies at elevated temperatures. One of the most prominent instruments in this case is dilatometry, which reproduces transformation kinetics via the abrupt change in thermal expansion. Phase fractions can also be determined by this technique. More comprehensive data can be obtained by structural analysis or high-resolution imaging instruments. Although the classical ultrasonic method is often used for microstructure characterization, the necessity of contacting the material restricts its use to low temperatures. The situation is similar for methodologies based on the eddy current principle. There are some approaches that equip established analytical methods like these with a heating chamber to apply the methodologies in-situ during heating, isothermal holding, or cooling. These are considered promising, as many are capable of simultaneously detecting and analyzing different changes in microstructure, but have certain limitations. Many of these methods can only be performed under vacuum or an inert gas atmosphere, are limited to surface examination, or can only be operated at a very low measurement frequency. Other methods, for example, based on synchrotron radiation or neutron scattering, have the disadvantage of limited accessibility. In the following, some promising methodologies for in-situ microstructure analysis are presented, which show great potential to analyze time- and temperature-resolved microstructural changes at high temperatures.
# Challenges and Future Trends in the Treatment of Psoriasis [PERSON] and [PERSON]
## 4 Future Aspects in the Treatment of Psoriasis
### Biomarker Development and Application of Multi-Omics  
Identification and validation of reliable biomarkers are crucial for optimizing psoriasis treatment. Biomarkers can aid in disease diagnosis, predict treatment response, and guide therapeutic decisions. Ongoing research focuses on identifying biomarkers associated with disease activity, therapeutic response, and the development of comorbidities, enabling more personalized and effective treatment strategies [9]. A recent review described potential biomarkers of disease progression in psoriasis. The study identified several potential candidate biomarkers that show promise for predicting disease severity in psoriasis. These include LCE3D, IL23R, IL23A, NFKBIL1 loci, and HLA-C*06:02 at the genomic level. In the proteomic realm, candidates such as IL-17A, IgG aHDL, GlyCA, I-FABP, and kallikrein 8 were highlighted. Additionally, metabolomic candidates like tyramine were also considered. Regarding the prediction of type 2 diabetes mellitus, the study pointed out genomic variations in the IL12B and IL23R loci as potential indicators. However, it is important to note that none of these biomarkers had sufficient evidence to be immediately employed for clinical decision making. Further validation is required to establish their clinical utility effectively [79].  
The integration of multi-omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, holds great potential for unraveling the complex molecular mechanisms underlying psoriasis. This multi-dimensional approach may lead to the development of more effective therapies and personalized treatment strategies.
# A Reduced-Order Model for the Vibration Analysis of Mistuned Blade-Disc-Shaft Assembly
## 1 Introduction
Hence, a number of model order reduction techniques were developed, including the generalized component mode synthesis methods [25; 26; 27] and some other effective methods for specific structures, such as the subset of nominal modes (SNM) [28], the fundamental mistuning model (FMM) [29], and the component mode mistuning method (CMM) [30] for mistuned bladed discs. The substructure-based methods are more general approaches for reduced-order modeling. Nevertheless, they are often not that efficient in analyzing mistuned blade discs, as a large number of interface DOFs may be retained, resulting in the model not being concise enough. SNM, FMM, and CMM are more efficient methods, as they were proposed with respect to mistuned bladed discs. In these investigations, however, the shafts were commonly assumed to be rigid and their flexibilities tended to be neglected. For the analysis of mistuned a blade-disc-shaft assembly, the flexibility of the shaft should not be neglected, as it may lead to non-negligible coupling vibration between the shaft and bladed disc. Therefore, an effective model order reduction technique for a mistuned blade-disc-shaft assembly remains to be developed. In this paper, a reduced-order model for the vibration analysis of mistuned blade-disc-shaft assemblies is presented, where the rotordynamic effects are all taken into account. Firstly, 3D finite element models of blade-disc-shaft assemblies are employed to obtain the governing equations of motion. Then, an efficient model order reduction technique based on the coordinate projection by normal modes of tuned assembly and cyclic symmetry analysis is developed. During the model order reduction, the mistuning in blade and disc is taken into account and dealt with independently. Finally, the effectiveness of the presented method is validated by an example of a blade-disc-shaft assembly. This paper is organized as follows: in Section 2, the governing equations of motion are presented. Section 3 addresses the formulations of model order reduction. Section 4 contains the validation of the presented method. Conclusions are given in Section 5.
# Recent Developments in Plasmonic Sensors of Phenol and Its Derivatives
## 3 Plasmonic Resonance-Based Fluorescence Sensor for Phenolic Compounds
Adapted from [84]. Other authors have successfully developed a novel Zn porphyrin MOF-based fluorescence sensor of bisphenol A detection using luminescence quenching. The experimental fluorescence data indicated that these MOF structures exhibited excellent monitoring of bisphenol A thanks to their electrostatic interaction, causing high sensitivity and selectivity in fluorescence quenching. In contrast, there was no significant quenching when the MOFs encountered the other phenolic substances, including \(p\)-cinnamyl phenol nonylphenol, octylphenol, 4-tert-butylphenol, 2,4-ditert-butylphenol, and diphenyl carbonate [86].
## 5 Simulation and Experimental Verification
### Simulation Verification
Although the load voltage experienced a brief change during load switching, it quickly recovered to the voltage level before switching, verifying that the proposed inverter could achieve CV to the load and ensure CC of the transmission coil when connected to the LCC-S compensation network. For the S-S resonant compensation network, the transmitting coil current showed a significant decrease when the load was switched. It stabilized at a new value in a short period of time. At the same time, the load voltage also decreased synchronously. Although the load current fluctuated slightly after resistor switching, it quickly recovered, indicating that the proposed inverter can effectively maintain a CC to the load when connected to the S-S compensation network. Figure 16: Simulation waveforms corresponding to each output of the inverter when \(D=0.5\) and \(D_{1}=D_{2}=0.25\). (**a**) The first output. (**b**) The second output. (**c**) The third output. (**d**) The fourth output. Figure 17: Simulation waveforms of the load switching. (**a**) LCC-S; (**b**) S-S.
## 3 IEEE 802.15.6: The Key-Enabling Standard for WBANs
### Security  
The IEEE 802.15.6 standard provides both secure and unsecured communications. A node and a coordinator follow several different states at the MAC level for secured communication. In secured communication there are three states that include Orphan, Associated and Secure states whereas, only Orphan state in unsecured communication. More details on these states can be seen in section 4.5 of the standard [9]. All nodes and coordinators are offered three security levels:  
* _Level 0--unsecured communication_: Messages are transmitted in unsecured frames, which provide no measures for message authenticity and integrity validation, confidentiality and privacy protection, and replay defence.
* _Level 1--authentication but not encryption_: In this level, messages are transmitted in secured authenticated but not encrypted frames, which provide measures for message authenticity and integrity validation and replay defence but not confidentiality and privacy protection.
* _Level 2--authentication and encryption_: The messages are transmitted in secured authenticated and encrypted frames, which provide measures for message authenticity and integrity validation, confidentiality and privacy protection, and replay defence.  
During the association state, a node and a coordinator should jointly select a suitable security level for their subsequent secured frame exchanges. In addition, based on their respective security requirements and certain information specific to each other, they should check whether authentication is required. For unicast secured communication, the node, and the coordinator shall activate a pre-shared Master Key (MK) or establish a new MK via an unauthenticated or authenticated association, and shall create Pair-wise Temporal Key (PTK) via a PTK creation procedure. For multicast secured communication, the coordinator shall distribute a Group Temporal Key (GTK) to the corresponding multicast group.  
The node and the coordinator shall follow the security hierarchy shown in Figure 16 to perform security key generations and provide message security services. A session indicated in this figure, refers to a time span in which a temporal key remains valid.  
After elaborating the necessary and key features of the WBANs standard, in the next section a comprehensive and latest study on communication stack ranging from mobility and radio channel modeling to MAC and Networking algorithms and protocols is presented.
# Multiport Single Element Mimo Antenna Systems: A Review
## 3 Isolation Techniques
### Metamaterials  
Metamaterials are useful for the reduction of mutual coupling implemented between adjacent antenna elements and the presence of a band gap in their frequency response [33]. The decoupling component is made of sub-wavelength metal-air layers, which can be treated as a singular medium operating over a broad frequency band [41]. Four element substrate integrated cavity-backed slot antennas distributed orthogonally comprise two-layer mushroom structure and the isolation are improved by placing a wall between each antenna element. MIMO antenna with reversal composite right-left-handed configuration is discussed in [42]. Here, the slit and interdigital capacitors are jointly inverted by cascading the metamaterial transmission line. Hence, by this configuration, the current produced inside the antenna is reversed. This configuration reduces the mutual coupling, and a good isolation is achieved.  
The minimum value of mutual coupling achieved in the above-discussed techniques is between \(-15\) dB to \(-20\) dB. The values lower than this can affect the antenna mismatches and embedded radiation efficiencies of the MIMO antenna systems.
# A Grey-Box Ensemble Model Exploiting Black-Box Accuracy and White-Box Intrinsic Interpretability
## 3 State of the Art Machine Learning Interpretability Methods
Shapley value is the contribution of each feature for an instance of interest to the model's prediction. The contribution of each feature is computed by averaging the marginal contributions for all possible permutations for all features. The marginal contribution of a specific feature is the value of the joint contribution for a set of features being this specific feature the last in order, minus the joint contribution for the same set of features with this specific feature being absent. The main innovation of SHAP, is that it computes the Shapley values in a way to minimize the computation time, since all possible permutations for a big number of features would lead to a computationally unattainable task. One disadvantage of this method is that the explanation of a prediction for a new instance requires access to the training data in order to compute the contributions of each feature. Interpretable mimic learning [19] is a Grey-Box intrinsic interpretation method which trains an accurate and complex Black-Box model and transfers its knowledge to a simpler interpretable model (White-Box) in order to acquire the benefits of both models for developing an accurate and interpretable Grey-Box model. This idea comes from knowledge distillation [20] and mimic learning [21]. More specifically the interpretable mimic learning method trains a Black-Box model. Then the Black-Box model makes predictions on sample dataset and its predictions with this sample dataset are utilized for training a White-Box model. In other words, the target values of the student (White-Box) model, are the output predictions of the teacher (Black-Box) model. One reason that this technique works is that the Black-Box eliminates possible noise and errors in the original training data which could lead to the reduction of the accuracy performance for a White-Box model. This Grey-Box model can have improved performance, comparing to a single White-Box model trained on the original data, being at the same time interpretable since the output predictor is a White-Box model and that is the reason why this model falls in the intrinsic interpretation category. The obvious disadvantage of this method is the same with all intrinsic interpretation models and this is the lower accuracy comparing to post-hoc models, since the performance of these models is bounded by the performance of their output predictor which is a White-Box model.