esa-sceva/satcom-chunk-collection huggingface dataset:
# FLGQM: Robust Federated Learning Based on Geometric and Qualitative Metrics
## 2 Materials and Methods
### Related Work
#### 2.1.2 Poisoning Attacks in Federated Learning  
In recent studies [15], it has been revealed that non-adversarial federated learning is highly susceptible to poisoning attacks initiated by malicious clients. These attacks can result in significant performance degradation of the global model. In summarizing, we categorize these poisoning attacks based on the objectives and capability of the malicious poisoning adversary. Firstly, we can categorize poisoning attacks into targeted [7; 11; 14] and untargeted attacks [6; 8; 9; 12; 22] based on the objectives of the adversary. The untargeted attack seeks to maximize the global model's inaccuracy across any test input, whereas the targeted attack seeks to minimize the accuracy specifically on certain test inputs without significantly impacting the accuracy of other test inputs.  
Secondly, we can categorize poisoning attacks into two main types: data poisoning attacks [2; 3; 4; 5] and model poisoning attacks [6; 7; 8; 9; 10; 11; 12; 13], depending on the adversary's capabilities. Data poisoning attacks leverage corrupted data of the client to indirectly manipulate the gradient or model parameters uploaded by the client. Model poisoning attacks directly change the gradient or model parameters of a malicious client to manipulate the global model. For example, [PERSON] et al. [8] formulated an attack to change the local model of the maligned client. Its objective is to maximize the divergence between the global model after the attack and the pre-attack global model. [PERSON] et al. [13] introduced a comprehensive framework for model poisoning attacks in federated learning, which enables a more comprehensive model poisoning attack.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Defense** & **Technique** & **Geometric** & **Qualitative** & **Central Dataset** & **Non-IID Data** \\ \hline Krum/Multi-Krum & Euclidean distance & ✓ & ✗ & Not needed & ✗ \\ \hline Bulyan & Krum + trimmed median & ✓ & ✗ & Not needed & ✗ \\ \hline RFA & Geometric median & ✓ & ✗ & Not needed & ✗ \\ \hline FoolsGold & Contribution similarity & ✓ & ✗ & Not needed & ✓ \\ \hline Zeno & Calculates score by clean small dataset & ✗ & ✓ & Needed & ✓ \\ \hline Fltrust & Cosine similarity + Clean small dataset & ✓ & ✓ & Needed & ✓ \\ \hline MAB-RFL & Similarity + Graph theory & ✓ & ✗ & Not needed & ✓ \\ \hline FLGQM (ours) & Cosine similarity + Euclidean distance & ✓ & ✓ & Not needed & ✓ \\  & (+ Distributed score calculation) & ✓ & ✓ & Not needed & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 1: The current defenses against poisoning attacks.
## 4 Experiment
### Dataset Construction  
We selected six real networks as our dataset sources: (1) collaboration network of Arxiv General Relativity category CA-GrQc [32], (2) collaboration network of Arxiv High Energy Physics Theory category CA-HepTh [32], and (3) a series of snapshots of the peer-to-peer file sharing network of Gnutella, among which three networks, p2p-Gnutella04, p2p-Gnutella08, and p2p-Gnutella25, were selected [32]. Related information about these networks is shown in Table 2.  
For the convenience of experiments, we ignored other properties of these networks, treated them as undirected and unweighted networks, and removed self-loops in the network. After obtaining the processed network, we divided each network into a community structure according to the Louvain algorithm, and obtained defined labels as described above. It should be noted that we may encounter two special types of nodes when dealing with the network: isolated nodes with degree 0 and small community nodes (the number of community nodes is less than 3). We directly defined these as non-bridging nodes, because such nodes have little bridging effect.  
After ranking nodes with three bridging algorithms, we counted the top 30% of ranked nodes for each rank and took their intersection as the bridge nodes of the network. In contrast, other nodes in the network are non-bridge nodes. We used labels \"1\" and \"0\" to identify bridge and non-bridge nodes in the network, respectively. In the machine learning-based classification task, when the proportion of labels of a certain category is too large, it will easily lead to a long tail problem, resulting in poor model performance. Therefore, in order to avoid this problem, we randomly selected some non-bridge nodes and all of the bridge nodes to form the dataset, so that the ratio of the number of bridge nodes and non-bridge nodes was 1:2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Network** & **Nodes** & **Edges** \\ \hline CA-GrQc & 5242 & 14,496 \\ CA-HepTh & 9877 & 25,998 \\ p2p-Gnutella04 & 10,876 & 39,994 \\ p2p-Gnutella08 & 6301 & 20,777 \\ p2p-Gnutella25 & 22,687 & 54,705 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Information of the five selected networks  
Figure 2: BND-GCN model architecture.  
During experiments, we randomly divided the training set and testing set according to the ratio of 7:3 and, at the same time, ensured that the ratio of positive and negative labels in training and testing remained the original 1:2. We built the model to train and test on five network datasets and the obtained test results can measure the effect of the model on bridge node detection. In the experiments, we selected Accuracy, Precision, Recall, and F1 score as the evaluation metrics for our proposed deep learning model.
## 5 Discussion
### Policy Implications  
The integration of AI-driven sanitary facility planning has far-reaching implications for building policy and regulatory frameworks. By shifting restroom design from rigid, prescriptive standards toward performance-based solutions, this approach challenges traditional assumptions and necessitates a reevaluation of existing codes.  
Our findings indicate that building regulations must evolve to accommodate data-driven methodologies alongside established prescriptive standards. Rather than relying solely on fixed fixture ratios, regulatory bodies should consider performance metrics that assess restroom adequacy based on wait times and user satisfaction. The ability of AI-based tools to optimize facility design introduces new possibilities for regulatory validation, requiring clear frameworks for assessing their reliability and effectiveness. Additionally, guidelines for the collection and application of real-time occupancy data would ensure that fixture requirements remain responsive to actual usage patterns rather than static historical assumptions.  
For successful implementation, facility managers and building authorities must navigate the integration of AI-driven recommendations within existing regulatory frameworks. This process requires well-defined protocols to evaluate AI-generated outputs against established standards, ensuring that automated predictions are supplemented by human oversight. Monitoring mechanisms will also be necessary to track actual usage patterns over time, allowing models to be refined based on empirical data. A standardized approach to documenting AI-assisted design decisions would further enhance transparency and regulatory acceptance.  
Beyond regulatory adaptation, the economic and environmental impact of AI-driven planning presents a compelling case for policy reform. The study demonstrates that a 15% reduction in total fixtures translates to significant construction cost savings, estimated at approximately SGD 280,000 per building. Optimized fixture distribution reduces water consumption and maintenance expenses, aligning with broader sustainability initiatives. More efficient space utilization supports urban density goals, while decreased material usage contributes to environmental conservation efforts.  
These findings highlight the need for policies that encourage innovation while maintaining safety and accessibility as core principles. As AI-assisted design tools become more prevalent, regulatory bodies must establish clear pathways for their adoption, ensuring that technological advancements enhance rather than compromise the quality and inclusivity of built environments.
## 4 Conclusions  
We retrieved key phenological and maximum productivity variables from a 15-year time series of SPOT-VEGFATION Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) using a model-fit approach, and we determined their correlation with a proxy of seasonal biomass production (CFAPAR, the cumulative FAPAR during the growing season). Our analysis over the entire Sahel region shows that: (i) the growing season length (GSL) exhibits the strongest correlation with CFAPAR over the entire study area (mean \(r=0.78\)); (ii) the correlation with start of season (SOS) is stronger and more widespread in the agricultural band in the south (mean \(r=-0.71\)); whereas, (iii) the indicator of the maximum productivity attained during the growing season (Peak) is more important in the northern and dryer grassland band (mean \(r=0.75\)).  
In order to increase the confidence in the analysis, we tested these correlations against field measurements of herbaceous biomass production in Senegal. For this specific dataset, we verified that: (i) there is a significant relationship between actual biomass production and CFAPAR; and (ii) the correlation between phenological variables and CFAPAR is consistent with the correlation between such variables and measured biomass.  
Our results show that there is potential for the use of the SOS anomaly, which is detectable during the first stages of vegetation development, as an early warning indicator of end-of-season biomass production and, hence, crop yield. The use of Peak, detectable in a later stage of the season, appears to have more potential for monitoring grassland rather than cropland biomass production.  
However, a large heterogeneity exists in the strength of the relation between CFAPAR, on the one hand, and SOS and Peak, on the other. This implies that both variables should be used with care as indicators of seasonal pasture production or crop yield, that is, only where a significant correlation could be ascertained. Under this condition, the use of such phenological variables can provide relevant early warning information and improve risk management systems.  
Further analysis at the local level is needed to identify the characteristics and processes that determine the spatial patterns of the observed correlations, as well as to further validate the results in different areas of the Sahel.
# Performance Analysis of a 3D Wireless Massively Parallel Computer
## 3 Simulation Results
### Link Utilisation Analysis
#### 3.2.1 Overall Link Busy Time  
OLBT is measured in ABC (1000 and 2000 nodes) with FFTTM (see Figure 11). The general trend of a decrease in OLBT with an increase in data rate is observed for most data points; however, the slope of this decrease varies from point to point. In some data points corresponding with low processor capabilities, OLBT does not change much as data rate increases (e.g., parts of blue plots in Figure 11a-c  
Figure 10: Processor utility for FFTTM on a 5D grid with two sizes: 1000 nodes (**a,c,e**) and 2000 nodes (**b,d,f**). Data sizes are 100 KB (**a,b**), 1 MB (**c,d**) and 10 MB (**e,f**). Blue, red, green, orange and black graphs represent computational ability of 0.01, 0.1, 1, 10 and 100 GIPS respectively.  
and red plots in Figure 11a,b,f). A reduction in OLBT can be caused by several factors. A majority of potential factors that may contribute to the behaviour of OLBT is fixed in ABC. Hardware design (e.g., antenna design and amplifying system) and the link distance do not change and cannot influence on OLBT. This means that the communication protocol (the store-and-forward algorithm in particular) used in the simulation cannot take advantage of very high comm/comp ratios. It is anticipated that a cut-through or wormhole mechanism will perform better.  
OLBT is also measured for a 5D network (Figure 12). The same inverse relationship between the data rate and OLBT is observed in most cases. Compared with a 5D topology, the ABC has a slightly sharper decrease in OLBT when the data rate increases. More analysis is needed to identify why in some odd cases the OLBT remains unchanged (or has a slight increase) as the data rate increases (e.g., blue plots in Figure 12a-d, but, so far, this effect can be related to the very high data rates. This means that the links are close to their hard limits of performance and an even faster transfer rate has little chance to make a real difference. OLBT has a direct relationship with the overall power consumption of a network like ABC as data transfer over radio links is an energy-demanding process. The role of OLBT in the overall execution time will be discussed later in this section by comparing the simulation results to a hypothetical non-overhead communication mechanism.  
Figure 11: Link busy time (OLBT in ms) for FFTTM on ABC with two sizes: 1000 nodes (**a**,**c**,**e**) and 2000 nodes (**b**,**d**,**f**). Data sizes are 100 KB (**a**,**b**), 1 MB (**c**,**d**) and 10 MB (**e**,**f**). Blue, red, green, orange and black graphs represent computational ability of 0.01, 0.1, 1, 10 and 100 GIPS respectively.
## 3 Frequency Domain Spectral Finite Element Method (FDSFEM)
### Wave Propagation in 2D Elements
Further scientific development of this method directed into a commercial implementation of a User Element based on WSFEM into ABAQUS(r) (UsersManual, vol.6.12, Dassault Systemes Simulia Corp., 2012) for the wave propagation analysis in 2D composite plate structures [149; 150]. As the process of modelling structural elements with FDSFEM includes the analysis in a frequency domain, this method may by utilised for free vibration analysis of for example stepped thickness rectangular plates, which has been performed by [125]. The authors have used FDSFEM to obtain closed-form solutions for an uniform rectangular plate with arbitrary boundary conditions except the Levy-type plates. The variation of plate thickness was a serious complexity added to the problem. In summary, an efficient and accurate FDSFEM model is developed to conduct free vibration analysis of stepped thickness rectangular plates with various boundary conditions. By the use of the boundary splitting method, a new frequency domain spectral element model for the analysis of finite rectangular plate elements with arbitrary boundary conditions has been developed by [126]. The high solution accuracy and computational efficiency of the proposed new spectral element model are validated by comparison with exact theory, the standard FEM, and the ANSYS (Release 11.0 Documentation for ANSYS, ANSYS, Inc., Canonsburg, PA, USA, 2006). The conclusions drawn from our results say that the proposed new spectral element model can be applied to any finite rectangular plate element with arbitrary boundary conditions. Through numerical studies, the authors show that the proposed spectral element model provides highly accurate solutions by using a relatively small number of finite elements with smaller computational effort in comparison with traditionally used commercial software dedicated to such analysis. Certain attention has been paid to the analysis of shallow shell displacement subjected to impact load, which has been calculated using the another modification of FDSFEM--Spectral Strip Method, developed for analysis of vibration of thin rectangular planform shallow shells [127]. The spectral element matrix in this case has been obtained using the force-displacement relationship method in the frequency domain. Accuracy of the developed method has been assessed in comparison to other common methods available in the literature for calculation of non-dimensional frequencies of shallow shells with various geometrical properties.
# Toward Integrated Satellite Operations and Network Management: A Review and Novel Framework
## 3 Integrated Management Framework  
This paper presents an integrated management framework, named the CMS, designed to unify the satellite and network operations of a NTN. The foundation of this framework is a task-scheduling approach chosen for its robustness and extensive validation through years of space missions, aligning with the recommendations of the CCSDS. This ensures broad acceptance within the space sector. The need for this approach becomes evident when
## 3 Results and Discussion
### Analysis on Cultural Perception-Intensity of Speech  
A one-way MANOVA analysis was conducted to explore the significance of differences between the three intensities in perceiving the four emotions. The results showed statistically significant differences between the different intensities (F (8, 68) = 37.45, \(p\) < 0.001, Wilk's \(\Lambda\) = 0.034, partial \(\eta\)2 = 0.815). Figure 4 shows that higher intensity could yield a better perception rate. A post-hoc analysis reported that all the emotions were perceived correctly at intensity 1 and intensity 1.25 decibels statistically significantly higher than at intensity 0.5 decibel (at \(p\) \(\leq\) 0.001). Though the perception means at intensity 1.25 decibels were higher for _sadness_ and _surprise_ and lower for _happiness_ than perception means at intensity 1 decibel, the differences were not significant. Only _anger_ was perceived correctly at intensity 1.25 decibels statistically significantly higher than at intensity 1 decibel (\(p\) < 0.05). In general, the results projected that the higher intensity gave a higher perception rate for all ethnic groups.  
Figure 3: The overall mean for emotion perception by Asian ethnics based on speech duration.
## 3 Results
### Characterization of Activated SPCE Surface  
The surface of the GCE was previously studied [20], whereas the characterization of the surface of the SPCEs, before and after the electrochemical activation, was performed in this study by the Field Emission Scanning Electron Microscope-FE-SEM-TESCAN (Kohoutovice, Czech Republic) S9000G. FE-SEM images showed important morphological changes upon electrode polishing with alumina-water dough. After the treatment, the surface appears remarkably smoothed and less porous (Figure 3). This outcome is in accordance with the observation that the voltammetric signals obtained by the polished SPCEs are significantly more stable and well-shaped than those obtained without the surface treatment. The electrochemical activation process does not significantly change the surface morphology or the elemental composition, as is possible to note by comparing with Figure 3d,e and by the EDS results reported in Figure 4. This behavior is quite different from that observed in the case of GCE, for which the XPS spectra showed a massive increase in the oxygen bound to the electrode surface after the activation [20]. This may be the reason why the activation process performed on the SPCE shows a lower impact on the electrochemical signal of APAP if compared with that obtained with the GCE.
# MOIRA-UNIMORE Bearing Data Set for Independent Cart Systems
## 3 Experimental Campaign  
In this study, data collection was conducted over several months, involving multiple iterations of experiments. Initially, various test runs were performed, and some data sets were discarded due to inadequate fault injection, insufficient resolution of the data acquisition system, or weak vibration signals that did not provide meaningful insights. Additionally, different experimental conditions were explored, including variations in the number of carts and loading mechanisms. Based on these observations, the data acquisition system was eventually upgraded to a 24-bit resolution with a 50 kHz sampling frequency. Faults were later introduced using laser-based precision techniques to ensure repeatability and accuracy in the fault injection process. The experimental campaign could be categorized into two main aspects, fault injection and data acquisition under varying conditions, including different cart configurations, speed profiles, fault sizes, and fault locations. Fault injection experiments were further classified into two types: non-invasive fault injection, where the bearings remained intact, and invasive fault injection, where the  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Part Number** & **Manufacturer** & **Type** & **Location** & **Description** \\ \hline
356B21 & PCB Piezotronics. Depew, NY, USA & Tri-Axial & Left curved module &
\begin{tabular}{c} Mounted on the left \\ curved module of the \\ guide rail \\ \end{tabular} \\ \hline
356A02 & PCB Piezotronics. Depew, NY, USA & Tri-Axial & Right curved module &
\begin{tabular}{c} Mounted on the right \\ curved module of the \\ guide rail \\ \end{tabular} \\ \hline
353B18 & PCB Piezotronics. Depew, NY, USA & Mono-Axial & Upper straight section &
\begin{tabular}{c} Mounted midway \\ along the upper \\ straight section of the \\ guide rail \\ \end{tabular} \\ \hline
353B18 & PCB Piezotronics. Depew, NY, USA & Mono-Axial & Lower straight section & \
\begin{tabular}{c} Mounted midway \\ along the lower \\ straight section of the \\ guide rail \\ \end{tabular} \\ \hline VSP001 & ifm efector, inc. Malvern, PA, USA & Mono-Axial &
\begin{tabular}{c} Mounted on an \\ aluminum rod \\ attached to the guide \\ rail, midway along the \\ upper straight section \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 2: Accelerometer configuration in the experimental independent cart system.  
bearings were disassembled before introducing faults. A summary of the experimental phases, challenges encountered, and improvements implemented is presented in Table 3. The fault injection methodology is discussed in detail in the next section.
# Experimental Validation of Ellipsoidal Techniques for State Estimation in Marine Applications
###### Abstract  
A reliable quantification of the worst-case influence of model uncertainty and external disturbances is crucial for the localization of vessels in marine applications. This is especially true if uncertain GPS-based position measurements are used to update predicted vessel locations that are obtained from the evaluation of a ship's state equation. To reflect real-life working conditions, these state equations need to account for uncertainty in the system model, such as imperfect actuation and external disturbances due to effects such as wind and currents. As an application scenario, the GPS-based localization of autonomous DDboat robots is considered in this paper. Using experimental data, the efficiency of an ellipsoidal approach, which exploits a bounded-error representation of disturbances and uncertainties, is demonstrated.  
state and disturbance estimation; uncertainty modeling; bounded uncertainty; ellipsoidal enclosures