esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Partitioning-Based Techniques
[21] proposed a multi-segment greedy rewriting method (MGRM) to address the issue of data fragmentation in data deduplication systems used in the cloud. MGRM is designed to search and rewrite containers collectively across multiple segments, then sequentially sorts containers in each segment. MGRM has two working modes: an optimal rewriting mode and a radical rewriting mode. The method achieves a balance between deduplication ratios and restore performance. However, it fails to assess the scalability of MGRM in different cloud environments and under varying workloads. The authors of ref. [22] presented a novel approach for optimizing web telemedicine database systems in large-scale networks to address the challenges of data centralization and secure access to patient data from remote locations. An Integrated Fragmentation Clustering Assignment approach was developed that considers the scalability of the system. The approach focuses on large-scale networks with a significant number of sites, providing more efficient data redistribution and improved performance for the telemedicine database system. However, the effectiveness of the proposed approach fails to describe the implementation and scalability of real-world healthcare environments. The authors of ref. [23] presented a study on the design of a distributed RDF (Resource Description Framework) database system to efficiently manage the growing volumes of RDF data. A novel approach that adapts frequent access patterns (FAPs) to capture the characteristics of the workload was proposed while ensuring data integrity and an approximation ratio. Based on these FAPs, three fragmentation strategies were introduced: vertical, horizontal, and mixed fragmentation. The approach reduces communication costs during query processing by leveraging adaptive frequent access patterns and tailored fragmentation strategies. However, it is crucial in handling even larger volumes of RDF data. In conclusion, partitioning-based techniques provide practical solutions for efficient data management in distributed systems. However, further efforts are needed to address some drawbacks in current techniques, especially in the following: (1) Scalability; most of the existing techniques have demonstrated their efficiency based on a specific dataset without taking into consideration diverse database environments. (2) Stability; the existing techniques did not show fixed performances when the volume of data changed dynamically over time. Hence, developing new data fragmentation techniques that take both challenges, e.g., scalability and stability, into consideration is essential for efficiency improvement.
# An Interactive Method for Detection of Process Activity Executions from IoT Data +
## 5 Proof of Concept Evaluation
### Conclusions from Evaluation  
The evaluation indicates that the proposed method is a viable approach for the detection of process activity executions and the discovery of process models from IoT data given basic knowledge about the domain and characteristics of the sensors and actuators. We noticed that changes within sensors and actuators can be linked to the execution of activities in the physical world. However, in our setup, the ground truth refers to the event log created by a BPM system that executed the activities. These activities are implemented via software in the form of web services [17], which may include additional computations and logic that cannot be observed in the IoT data and thus, may lead to a mismatch in the IoT data observations and events recorded by the BPM system [36].  
It is relatively simple to determine the relevance of CPS components and time frames for the analysis of the data set and to identify similar activities based on the same signatures. Difficulties might emerge in finding the exact indicators for the start and end of activities and the right level of granularity of activities, especially in the case of several sensors and actuators involved. Nevertheless, as we observed during the evaluation, potential imprecision and _ambiguities_ might be mitigated by considering the steps of the method _not in a strict sequence_. Indeed, we noticed that looking at both the process context and the already identified activities from other components (including the corresponding time stamps) helps to disambiguate specific uncertain parts of the data set. We also noticed that through annotation, the analyst incrementally acquires process knowledge, which then applies to facilitate further annotation steps. For instance, after having identified the _Burn_ activity by the oven, it was possible for the analyst to annotate the subsequent activity at the vacuum gripper robot as _Pickup from OV and transport to DPS_.  
The method does not enforce a particular order to follow for the selection of the relevant components to analyze (Step 4). However, the evaluation results suggest that it might be beneficial to start with CPS components with fewer sensors and actuators and exhibiting fewer change patterns. We observed that detecting activities for these components is easier and facilitates detecting activities for other components exhibiting more complex patterns, as these detected activities contribute to an incremental development of process knowledge and context to exploit in subsequent iterations of the method.
## 4 Results
### Baselining Methods
#### 4.4.3 Method III  
Method III takes into account the last five days and uses the measurements of the three with the highest production. Looking at Figure 13, this method seems to be approximating the reference profile relatively well in the first few days of the week. On the 4 th of July, it can be observed that, due to the relatively low profiles of the past days, method III underestimates the reference profile.  
Looking at curtailment option 2, method III is relatively sensitive to previous flexibility activation, as this influences the historical data used to construct the baseline. This can be observed clearly by comparing Figure 14 with Figure 15. Looking at the zoom-box  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Option 1**} & \multicolumn{2}{c}{**Option 2a**} & \multicolumn{2}{c}{**Option 2b**} \\  & **MAE** & **RMSE** & **MAE** & **RMSE** & **MAE** & **RMSE** \\ \hline
1–8 June & & & & & & \\ \hline Method I & 9.00 & 31.44 & 3.47 & 18.78 & 3.47 & 18.78 \\ Method II & 15.87 & 60.75 & 8.80 & 52.65 & 8.80 & 52.65 \\ Method III & 12.31 & 44.86 & 5.27 & 29.84 & 5.64 & 30.45 \\ Method IV & 3.12 & 11.47 & 0.98 & 5.19 & 1.07 & 5.74 \\ \hline
1–8 July & & & & & & \\ \hline Method I & 30.19 & 80.34 & 1.86 & 10.05 & 2.37 & 12.03 \\ Method II & 29.27 & 75.44 & 1.92 & 10.07 & 2.43 & 12.04 \\ Method III & 19.22 & 56.71 & 2.45 & 14.83 & 3.49 & 18.45 \\ Method IV & 10.01 & 26.38 & 0.45 & 2.63 & 1.41 & 8.28 \\ \hline
1–8 August & & & & & & \\ \hline Method I & 7.61 & 40.53 & 4.71 & 36.14 & 4.71 & 36.14 \\ Method II & 9.37 & 45.37 & 4.85 & 36.28 & 4.85 & 36.28 \\ Method III & 8.54 & 43.29 & 4.34 & 35.78 & 4.34 & 35.78 \\ Method IV & 5.10 & 36.04 & 4.27 & 35.77 & 4.27 & 35.77 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Error metric for baselines in the first weeks of June, July and August, 2019. Units in [W].  
of the fourth of July, the profile of baselining method III is changed significantly in the latter figure.
## 2 Numerical Analysis of the Erosion Model
### Theoretical Analysis of Erosion Wear  
#### 2.2.1 Computational Model  
The flow of the fracturing fluid in the space pipe follows the conservation law of physics, resulting in the governing equation of the fluid as follows [18; 19]:  
1. Conservation of mass: \[\frac{\partial\rho}{\partial t}+\frac{\partial}{\partial x_{m}}(\rho v_{m})=0\] (1)
2. Momentum equation: \[\frac{\partial}{\partial t}(\rho v_{m})+\frac{\partial}{\partial x_{n}}(\rho v _{m}v_{n})=-\frac{\partial p}{\partial x_{m}}+\frac{\partial\tau_{mn}}{ \partial c_{n}}+\rho g_{n}+V_{m}\] (2) \[\tau_{mn}=\left[u(\frac{\partial v_{m}}{\partial x_{n}}+\frac{\partial v_{n}}{ \partial x_{m}})\right]-\frac{2}{3}u\frac{\partial v_{n}}{\partial x_{m}} \delta_{mn}\] (3)  
Figure 1: Schematic diagram of the model.  
Figure 2: Mesh division.  
(3) Energy equation:  
\[\frac{\partial}{\partial t}(\rho E)+\frac{\partial}{\partial x_{m}}[v_{m}(\rho E+p) ]=\frac{\partial}{\partial x_{m}}\left[\lambda\frac{\partial T}{\partial x_{m}}- \sum_{n^{\prime}}h_{n^{\prime}}J_{n^{\prime}}+v_{n}\tau_{mn}\right]+S_{e} \tag{4}\]  
Table 2 explains the parameters in the equation.
## 4 Test Results and Interpretation
### Alteration Mineral Assemblages and Alteration Zones  
Based on the large-scale lithofacies mapping and alteration mineral mapping, this study adopted TSG spectral geological analysis software (version 8.0) to identify alteration of the core and tunnel samples extracted from the northern mining area. It selected ZK13'03 to investigate the middle section of the horizontal alteration zone at 200 m depth,  
Figure 5: Absorption characteristics of standard minerals.  
ZK20(0)1'02 to look into the middle section of the horizontal alteration zone at 0 m, and ZK2115-1 to examine the vertical alteration zone. Based on the alteration mineral assemblages, the analysis can be shown as follows:  
(1) Borehole ZK19 (200) 13'03 can be divided into four alteration zones (Figure 6): (1) muscovite + illite + chlorite + calcite + smectite + ankerite, (2) muscovite + chlorite + calcite + smectite, (3) muscovite + illite + chlorite + kaolinite, and (4) chlorite + calcite + smectite + ankerite. Mg-chlorite was mainly distributed at the upper part of the borehole, while Fe-chlorite was mainly concentrated in the lower part.  
(2) Borehole ZK20(0)1'02 can be divided into three alteration zones (Figure 7): (1) chlorite + calcite + ankerite + kaolinite, (2) muscovite + illite + chlorite + calcite, and (3) chlorite + ankerite. No Mg-chlorite was found in this borehole, and Fe-chlorite was mainly distributed in the lower part. Sericiite illite was surrounded by ankerite on both sides.  
(3) Borehole ZK2115-1 can be divided into five alteration zones: (1) illite + chlorite + calcite + smectite + kaolinite, (2) muscovite + illite + chlorite + calcite + smectite, (3) muscovite + illite + chlorite + calcite + smectite, (4) chlorite + calcite + smectite, and (5) muscovite + illite + chlorite + calcite + smectite. Based on the content and frequency, we found that core samples were infilled by illite, Fe-Mg chlorite, calcite, and smectite (Figure 8). Mg-chlorite alteration occurred mainly in the upper part of the borehole, and Fe-chlorite was concentrated in the three blocks in the upper part of the borehole.  
Figure 6: Alteration mineral zones in ZK13’03.  
Figure 8: Alteration mineral zones in borehole ZK2115-1.  
Figure 7: Alteration mineral zones in ZK20(0)1’02.
## 2 Bit-Interleaved Sigma-Delta-Over-Fiber (BISDoF)
Since the output signals are no longer bi-level, digital-to-analog converters (DACs) are required. If analog filters are added (Figure 3b), digital up-conversion is not possible and an analog up-converter must be included. In this option, \(f_{\Sigma\Delta}\) can be chosen freely based on the signal quality requirement, thus simply denoted as \(f\) in the figure. Either way largely increases the complexity. It is a design trade-off between the optical bit-rate efficiency and the remote unit complexity. For the UL, the de-interleaved signals are only down-sampled at the central site. It is indeed possible to lower \(f_{\Sigma\Delta}\) and interleave more I-Q pairs in one fiber. For the ease of implementation, the proposed network has the same architecture for both directions.
###### Abstract  
The results obtained for the measurements of acoustic emission (AE) signal parameters emitted in three dielectric liquids are presented in this paper. In particular, the velocity of AE wave was calculated based on the time of arrival of the AE signal. A frequency modulated signal was generated by a piezoelectric transducer and measured at various distances by a hydrophone. The changes in velocity values at particular distances and for different frequencies were investigated. The analyses include the determination of the dependency of the velocity values from the distance between the communicating devices. A nonlinear regression model was calculated, and the differences between AE velocities propagating in the considered dielectrics were determined. Similarly, the influence of modulation frequency on the AE velocity was determined using nonlinear regression. Based on the calculation data, it can clearly be stated that the velocity of AE wave depends significantly on the frequency and distance at which it is registered. These two factors may have an important influence on the localization of partial discharges (PD) occurring in these types of dielectric liquids.  
mean velocity; acoustic emission; mineral oil; natural ester; synthetic ester +
Footnote †: journal: Applied Physics Letters
## 4 Computational Features and CPU Parallelisation
### Node Renumbering Algorithm  
A remark on the importance of adequate node renumbering is briefly stated hereafter. Its importance is evaluated in terms of time costs and required RAM to complete a linear elastic analysis. Such costs are influenced by the bandwidth dimension of the global stiffness matrix [K]. The minimisation of the latter through sparse matrices is well-documented [103; 104] and several algorithms have been developed [105; 106; 107; 108]. A brief test is performed on a masonry wall subjected to an out-of-plane surface load. Results are found for linear elastic analysis and through a direct sparse solver. A refined mesh of \(35\times 65\) rigid quadrilateral elements represent the masonry wall. Two algorithms  
Figure 11: Bi–directional cyclic loading. (**a**) Cauchy stress \(\sigma_{11}\) for flexural trusses. (**b**) Evolution of the damage parameter, d, for the flexural trusses. (**c**) Vertical bending moment–curvature (first cycle). (**d**) Vertical bending moment–curvature (second cycle).  
have been employed: (i) a geometric-based algorithm, in which mesh nodes are numbered according to a defined direction vector (length with higher dimension); and (ii) the so-called Approximate Minimum Degree (AMD) sparsity algorithm, in which the number of non-zero entries in the global stiffness matrix is reduced through a heuristic approach [109]. Results gathered in Table 3 were found through a 64-bit Windows 10 computer with 16 Gb RAM and Intel i7 CPU running at 3.4 GHz.  
Although the absolute computational time difference may be negligible for the present benchmark, the use of an algorithm may reduce the relative CPU time by around 60% and around 44-49% of the required optimum physical memory. Such differences could be more relevant within a nonlinear quasi-static analysis since several steps (and iterations) need to be solved. Exploring the issue of the most efficient algorithm available in the literature is especially convenient for very large-scale structures, in which powerful pre-processors software as ANSA [110] can be used. Here, even if the performed test is simple, it aims to raise the awareness (or the discrete-model FE user) over the importance of a renumbering strategy. To cope with the concern of improving computational efficiency, ABAQUS offers a renumbering algorithm based on a geometrical method. This seems suitable for the dimension of the structural problems involved in the present study and in cases where a dominant length direction exists.
# Effect of Baffle Board on Aerodynamic and Stealth Performance of Double S-Duct Caret Intake
## 3 Results and Analysis
### Corrugation Shape Optimization of Baffle Board
The double S-duct diffuser could effectively suppress the radar echo intensity of the caret intake under both polarization conditions, but there were several boundary layer separations in the diffuser. The curved baffle board could further improve the stealth performance of the intake, and the effect of the baffle board in the front diffuser was more obvious than that of the board in the rear diffuser with the same orientation. However, the board can aggravate the separation, which could increase the outlet distortion and even reduce the flow coefficient. By comparison, the vertical board in the front diffuser caused the least loss of aerodynamic performance, but the improvement in stealth performance under the horizontal polarization condition was obviously worse than that under the other condition. II. By using the corrugated board instead of the curved board, the difference between the improvements in stealth performance under the two conditions was reduced. Until the corrugation number of the corrugated board reached eight, the aerodynamic performance of intake changed insignificantly with the corrugation number. The improvement in the stealth performance under the vertical polarization condition can be enhanced with an increase in the corrugation number. Under the other condition, the improvement in the stealth performance was the best when the corrugation number was six. III. By partly changing the curvature of the corrugated baffle board, the RCS characteristics and the aerodynamic performance of the intake can be both optimized. The curvature of the middle part of board had only a slight influence on the improvement in the stealth performance under the horizontal polarization condition. The closer the large-curvature zone was to the upper wall of the intake, the better the stealth performance under the vertical polarization condition, but the worse the aerodynamic performance. In conclusion, while the corrugated baffle board improved the stealth performance of the intake, it could also inevitably cause losses in aerodynamic performance. The specific effects may be dependent on the position and the configuration of the baffle board. Therefore, how to make a comprehensive and detailed optimization of the board parameters could become the focus of future research. Conceptualization, B.W. ; methodology, B.W. ; validation, B.W. ; formal analysis, B.W. and S.L. ; investigation, B.W. ; resources, B.W. ; data curation, S.L. ; writing--original draft preparation, B.W. ; writing--review and editing, Q.W. ; visualization, B.W. ; supervision, B.W. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. The authors declare no conflicts of interest.
# Reducing Defense Vulnerabilities in Federated Learning:
## 4 Attack Scenarios and Results
### Generalization Capabilities and Limitations  
The NC-FLD approach demonstrates promising generalization capabilities beyond the specific examples tested in our experiments. The fundamental principle of identifying high-impact neurons through gradient variance analysis is architecture-agnostic, making it applicable to various neural network structures beyond CNNs and ResNet architectures. This adaptability stems from our approach's focus on neuron-level behavior rather than specific architectural features.  
Our experiments across diverse datasets (CIFAR-10, F-MNIST, MNIST, and GTSR) with varying complexity, image characteristics, and class distributions demonstrate NC-FLD's robustness to different data domains. Furthermore, the defense mechanism's effectiveness against multiple attack types (label-flipping, sign-flipping, and LIE attacks) indicates generalization across threat models.  
However, several theoretical limitations could potentially constrain generalization in certain scenarios. Firstly, extremely deep architectures with hundreds of layers may exhibit more complex interactions between neurons, potentially requiring hierarchical neuron selection strategies beyond our current implementation. Secondly, while our approach performs well on moderate non-IID distributions, extremely skewed distributions where clients have access to only a single class may challenge our detection mechanisms, as gradient patterns from legitimate training on such restricted data could resemble poisoning patterns. Finally, the effectiveness may be limited in scenarios where malicious participants significantly outnumber honest ones (>50%), as our approach assumes majority-honest participation.  
Despite these limitations, the neuron-centric paradigm offers a flexible foundation that can be extended to additional learning architectures, federated optimization algorithms, and emerging attack variants through minor adjustments to the neuron selection criteria and classification boundaries.
## 1 Introduction
Many researchers indicate that the performance of ASAG systems is closely tied to the volume of training data available [13]. [PERSON] et al. [14], argued that, while the BERT and RoBERTa language models have a new state-of-the-art performance in sentence-pair regression tasks like semantic textual similarity, enabling the input of all the sentences into the network, the computational overhead is considerable. Though ASAG is crucial, implementing these expensive models may pose challenges. In this research, we explore some other Sentence-Bidirectional Encoder Representations (SBERT) models as mentioned in [15], and then propose a simpler model by fine-tuning certain hyperparameters to optimize the ASAG performance. The available datasets exhibit an imbalance in the distribution of data across different labels. For instance, The SciEnts Bank (SEB) dataset [16] has a correct answer ratio of 39.9%, with label 4 representing the correct answer, while labels 0-3 are considered incorrect answers. And in contrast, the Mohler dataset from the University of North Texas predominantly contains correct answers, with approximately 78% coming from labels 4-5 only. As mentioned in Ref. [17], achieving a balanced dataset is crucial for developing optimal models, although this is challenging in practice. Augmentation, a method within oversampling, involves enhancing an existing dataset by adding supplementary data. Various methods used for augmenting data in NLP include random deletion, synonym replacement, random swap, and back translation [13; 17]. In this research, we augment the dataset by utilizing GPT to paraphrase the answer, serving as an additional strategy of synonyms replacement. Despite GPT's inability to directly augment data, prompts can be utilized to generate synonyms and antonyms of words. This paper is organized as follows: Section 2 reviews all works related to ASAG. Section 3 presents the proposed methods, including the datasets, evaluation metrics, and experiment setup used in this research. Section 4 presents a proof-of-concept implementation of the system and discussions of the experimental results. Section 5 concludes with all of the achievements from these experiments.