esa-sceva/satcom-chunk-collection huggingface dataset:
# K-Means++ Clustering Algorithm in Categorization of Glass Cultural Relics
## 5 Model Sensitivity Analysis  
For a certain type of glass artifact, the randomness associated with the production process or prolonged exposure to the environment may cause some change in the proportions of the various chemical components, but the change is relatively minor to the extent that it should not affect our determination of that glass category. We consider such perturbations as noise added to the sample data [25]. Since the sample of data in this study is small and estimates of noise are highly susceptible to overfitting, it is useful to assume that the prior probability distribution of the noise is F = N (0, 0.0001), i.e., a Gaussian distribution with mean 0 and standard deviation 0.01. For any \(x\in F\),  
\[P(-\sigma<X<\sigma)=\int_{-\sigma}^{\sigma}f(t)dt=0.682, \tag{3}\]  
where f(t) is the probability density function of the distribution F. There is about a 68.2% probability that the noise lies in the thousandths interval and has a small effect on the weight of the chemical composition.  
The process of a random noise test is as follows: The number of random tests is initially set T = 100. In the i'th (1 \(\leq i\leq T\)) test, all original unweathered samples are added with noise sampled by the above prior distribution to obtain the noise-containing sample data, and the prediction labels are obtained by the clustering algorithm. The number of samples for which the predicted labels are the same as the original predicted labels after adding noise is counted and is denoted as t\({}_{i}\). At the end of T times of testing, it is calculated that (N is the number of samples)  
\[\text{Random noise test ratio}=\frac{\sum_{i=1}^{T}t_{i}}{N\times T}. \tag{4}\]  
As a measure, its value ranges from [0, 1], and the closer to 1, the better the noise immunity of the model.  
The K-Means++ clustering algorithm was tested 10 times for random noise and averaged to obtain a random noise test ratio = 0.980, indicating that the model is insensitive to noise under the current noise prior distribution.  
To further investigate the relationship between model sensitivity and noise standard deviation, we used the following equation:  
\[\sigma\ =0.01+0.00474t,\ 0\ \leq\ t\ <20. \tag{5}\]  
The result of increasing the standard deviation in the above random noise test for the K-Means++ model reveals the robustness of K-Means++. Compared with the K-Means algorithm, when \(\sigma\) increases from 0.01 to 0.1, the clustering accuracy of K-Means++ remains significantly higher and shows a smaller variance, which illustrates the performance improvement of K-Means++. The random noise test ratio with \(\sigma\) is shown in Figure 13.  
Figure 13: K-Means++ and K-Means clustering model sensitivity with noise standard deviation.
## 2 Computed Tomography and Aortic Valve
### CT Assessment of the Aortic Valve for Transcatheter Procedures
#### 2.3.6. Left Ventricular Outflow Tract (LVOT) and Septum  
Both self-expandable and balloon-expandable THVs extend their lower extremity into the left ventricular outflow tract (LVOT). A careful assessment of this region should be performed to minimize complications (Figure 8a,b). The proper seating of the THV and the risk of spontaneous repositioning after deployment can be affected by marked upper septal hypertrophy protruding in LVOT. Furthermore, prominent septal hypertrophy is associated with atrioventricular blocks and  
Figure 8. (**a**) LVOT calcium degree evaluation. (**b**) Preoperative LVOT measurements. Figure 7. (**a**) Sinotubular junction and (**b**) proximal ascending aorta evaluated by CT for TAVI. the need for post-TAVI PMI [55]. Accordingly, [PERSON] et al. showed that MDCT-based measurements of septal thickness predict the post-TAVI occurrence of AV blocks [56]. Septal hypertrophy causing significant LVOT obstruction is a contraindication to TAVI; however, as demonstrated by [PERSON] et al., proper modifications to THVs can allow successful implantation in this kind of patients [57]. Notably, [PERSON] et al. found that echocardiography tends to overestimate interventricular septum thickness compared with MDCT (up to 2.2 mm) [58] (Table 1). Furthermore, the risk of significative residual aortic regurgitation and second valve implantation can be affected by moderate or severe LVOT calcification. In this regard, [PERSON] et al. proposed a specific classification based on the severity of LVOT calcification: mild was detected in the presence of one nodule extending <5 mm in any dimension and covering <10% of the perimeter of the LVOT; moderate was documented in the presence of two nodules or one extending >5 mm in any direction or covering >10% of the perimeter of the LVOT; and severe was considered in the case of multiple nodules of single focus
## 5 Conclusions
Future work could incorporate more color models, deep learning methods, and user preference data to further improve prediction accuracy. Cross-cultural heritage color regeneration: The current model is optimized primarily for the regeneration of specific cultural heritage colors. Future research could extend this method to accommodate a broader range of cultural contexts, particularly for cross-cultural heritage color regeneration, to enhance its generalizability and application scope. Optimization of user interaction and experience: Although Cultural Color IGA improves design efficiency through automation, user experience remains an area of focus. Future work could leverage technologies such as augmented reality (AR) to provide more intuitive user interfaces, further enhancing user engagement and satisfaction. Promotion and validation in practical applications: Future work can combine this method with real-world cultural heritage preservation projects to further validate its effectiveness in practical applications. Specifically, in fields like digital preservation of cultural heritage and virtual museum construction, Cultural Color IGA has vast application potential. Through these optimizations, Cultural Color IGA will better serve the protection and regeneration of cultural heritage, promoting deeper integration between culture and technology and providing sustainable momentum for the innovative design of cultural heritage. Conceptualization, Q.X., Y.H. ([PERSON]) and Z.J. ; data curation, Q.S. and J.W. ; formal analysis, Q.X. and Z.W. ; funding acquisition, B.W. ; methodology, Q.X. and K.Z. ; software, Y.H. ([PERSON]); supervision, Y.H. ([PERSON]); validation, Q.X., Z.J. and Z.W. ; writing--original draft, Q.X. ; writing--review and editing, Z.J., Z.W. and Q.X. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (Grant Number: 61906129), the China Association for Science and Technology Youth Support Talent Project (Grant Number: 2021-298), and the Hong Kong Polytechnic University GBA Startup Postdoc Programme 2022 (Grant Number: SP-22-13). Not applicable. The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors. The author would like to thank the tutors, editors, and anonymous reviewers for their helpful suggestions. The authors declare no conflicts of interest.
## 5 Case Study  
In order to study the severity and prevalence of inter-user interference, a case study has been performed in the waiting area of Emergency Department of the National University Hospital (NUH) of Singapore.
###### Abstract  
Downregulation of the ubiquitin-like containing PHD and ring finger 1 (UHRF1) oncogene in cancer cells in response to natural anticancer drugs, including thymoquinone (TQ), is a key event that induces apoptosis. TQ can induce UHRF1 autoubiquitination via the E3 ligase activity of its RING domain, most likely through the downregulation of herpes virus-associated ubiquitin-specific protease (HAUSP). In this study, we evaluated whether HAUSP downregulation and fast ubiquitination of UHRF1 are prerequisites for UHRF1 degradation in response to TQ in cancer cells and whether doxorubicin can mimic the effects of TQ on UHRF1 ubiquitination. RNA sequencing was performed to investigate differentially expressed genes in TQ-treated Jurkat cells. The protein expression of UHRF1, HAUSP and Bcl-2 was detected by means of Western blot analysis. The proliferation of human colon cancer (HCT-116) and Jurkat cells was analyzed via the WST-1 assay. RNA sequencing data revealed that TQ significantly decreased HAUSP expression. TQ triggered UHRF1 to undergo rapid ubiquitination as the first step in its degradation and the inhibition of its cell proliferation. TQ-induced UHRF1 ubiquitination is associated with HAUSP downregulation. To allow nucleophilic induced a similar dose- and time-dependent downregulation of UHRF1 in cancer cells, but UHRF1 did not undergo ubiquitination as detected in response to TQ. Furthermore, TQ decreased Bcl-2 expression without triggering its ubiquitination. A fast UHRF1 ubiquitination is an indispensable event for its degradation in response to TQ but not for its responses to doxorubicin. TQ appears to trigger ubiquitination of UHRF1 but not of the Bcl-2 oncogene, thereby identifying UHRF1 as a specific target of TQ for cancer therapy.
# Discrete-Time First-Order Plus Dead-Time Model-Reference Trade-off PID Control Design
## 2 Control System  
The continuous time FOPDT transfer function is described as follows:  
\[P(s)=\frac{K}{Ts+1}e^{-Ls} \tag{1}\]  
where \(K\) is the gain, \(T\) is the time constant, and \(L\) is the dead time. In the present study, the discrete time control model is designed with sampling interval \(T_{s}\), and hence, the continuous time model is expressed as follows:  
\[P_{d}(z^{-1})=\frac{b_{0}+b_{1}z^{-1}}{1-a_{1}z^{-1}}z^{-(d+1)} \tag{2}\]  
where \(a_{1}\), \(b_{0}\), and \(b_{1}\) are the coefficient parameters, \(d\) is the dead-time in the discrete time, and \(z^{-1}\) denotes the backward shift operator. The discrete time plant parameters correspond to the continuous time plant parameters as follows:  
\[a_{1} =e^{-\frac{T_{s}}{T}} \tag{3}\] \[b_{0} =K\left(1-a_{1}e^{\frac{L_{0}}{T}}\right)\] (4) \[b_{1} =K\left(a_{1}e^{\frac{L_{0}}{T}}-a_{1}\right)\] (5) \[d =L_{1} \tag{6}\]  
where a non-negative integer \(L_{1}\) satisfies the following equation:  
\[L=L_{1}T_{s}+L_{0}\ \ (L_{0}<T_{s})\]  
Therefore, the input/output relationship in discrete time is given as follows:  
\[y(k)=P_{d}(z^{-1})u(k) \tag{7}\]  
where \(y(k)\) is the system output (plant output) and \(u(k)\) is the control input.
## 5 Implementation and Evaluation
### Results  
#### 5.2.1 Model Performance  
Figure 2 illustrates the global accuracy progression of FedProx and FedAvg over 50 training rounds. FedProx demonstrates faster convergence, stabilizing around 89.4% accuracy after 25 rounds, compared to FedAvg's 84.7%, which stabilizes after 35 rounds. The proximal term in FedProx helps mitigate the impact of data heterogeneity, allowingit to outperform FedAvg in non-IID settings. FedAvg shows slower initial improvements, highlighting its limitations in handling diverse datasets. Overall, FedProx's stability and higher final accuracy make it more suitable for decentralized IoT-edge systems.  
Table 1 compares the local accuracy achieved by FedProx and FedAvg across five edge servers with non-IID datasets. FedProx consistently outperforms FedAvg, with accuracy improvements ranging from 4% to 6% per server. Edge server 5 achieves the highest accuracy for both methods, reflecting more balanced data distribution. Conversely, edge server 20, with highly localized and heterogeneous data, shows the largest performance gap, highlighting FedProx's robustness in handling non-IID environments. This consistent improvement across all servers demonstrates FedProx's ability to generalize better than FedAvg in decentralized systems.
# Gyro Error Compensation in Optoelectronic Platform Based on a Hybrid ARIMA-Elman Model
## 3 Related Knowledge
### Lift Wavelet Filtering  
The lifting wavelet was proposed by [PERSON] in 1996, and has been widely used in signal processing. The basic idea of the lifting algorithm is to gradually construct a wavelet with better properties through a basic wavelet. A canonical lifting algorithm has three steps; namely Split, Prediction, and Update, which is shown in Figure 5.  
Split: The original sequence is divided into two disjoint subsets, each of which is half of the original sequence length. Typically, these two subsets are odd and even sequences, as follows:  
\[x_{0}(k)=x(2k+1) \tag{8}\]  
\[x_{e}(k)=x(2k) \tag{9}\]  
Prediction: The wavelet coefficient \(d(k)\) is generated by using the prediction operator \(P\). It can be expressed by the following formula:  
\[d(k)=x_{0}(k)-P[x_{e}(k)] \tag{10}\]  
Update: A better subset of data \(c(k)\) can be generated with the operator \(U\), so that it can maintain the characteristics of the original data sequence \(x(k)\). The update process is expressed, as follows:  
\[c(k)=x_{e}(k)+U[d(k)] \tag{11}\]  
At this point, one time of decomposition of the signal is completed with the wavelet. During reconstruction, the inverse update step is first operated to recover the even sequence, and then the inverse prediction step is operated to restore the odd sequence. Finally, the two sequences are placed, overlapping, to reconstruct the original signal.  
Due to the fact that gyro output contains white noise and drift error, it can be assumed that after a suitable \(p\)-time lifting wavelet decomposition, the drift error will be decomposed into \(c(n)_{p}\) and the white noise will be mainly decomposed into \(d(n)_{1-p}\). Therefore, if the lifting wavelet reconstruction algorithm is used to reconstruct \(c(n)_{p}\), the drift error \(d_{r}(t)\) can be obtained and the white noise \(\sigma n(t)\) can be obtained by reconstructing \(d(n)_{1-p}\). Thus, the drift error and white noise are separated. This paper chooses the improved Haar wavelet as the wavelet base. When the decomposition order is 4, the decomposition and reconstruction results are shown in Figures 6 and 7, respectively.  
Figure 5: Structure of the lifting algorithm.
# Federated Learning for Edge Computing: A Survey
## 6 Challenges
### Scalability  
The FL process must be highly scalable. In an IoT environment, many clients can participate in model training. In FL communication, it is also necessary to ensure good scalability of the whole system. With a large number of clients, it can happen that some clients lose connection to the server, for example, due to poor quality and weak signal or due to low battery as it can be in the case of mobile devices. It is necessary to ensure that such a system can deal with these situations. In this case, model training can be performed through efficient client selection [170]. The scalability and effectiveness of FL training and client selection have been the subject of several studies that have proposed different types of quality client selection. Clients who communicate wirelessly with a connection that is not very reliable can sometimes be problematic. In this case, only trustworthy clients are sought for training [171]. [PERSON] et al. [172] developed FL framework based on a combination of models selected by clients for computing vehicle edges. Instead of randomly selecting FL clients, they use a client selection method based on contract theory. The scalability of FL systems is the subject of further studies. [PERSON] et al. [173] mainly solve scalability problems and low communication latency between the client and server. The authors propose an FL framework based on a cooperative, mobile edge network that can reduce latency compared to cloud communication. Distributed edge servers, which facilitate communication with other edge servers, manage the coordination of mobile devices in this case. Moreover, in the article [174] the authors describe and discuss the problems arising during local training of the model on the client side and the scalability of the server side. In this case, their goal is to reduce scalability problems, describe individual advantages and disadvantages, and propose the most usage scenarios.
# Earthquake-Tolerant Energy-Aware Algorithm for WDM Backbone Network
## 4 Conclusions  
In this paper, we proposed a new algorithm that optimizes the energy consumption of a backbone WDM network while simultaneously achieving the key objectives of earth-quake resilience, network connectivity, and enhanced energy efficiency. The proposed algorithm accomplished its goals by removing or disabling less crucial links that contribute to higher energy consumption. With this innovative algorithm, it becomes possible to improve the design of new backbone networks and reduce the energy expenditures of existing ones. All the traffic that was previously passing through the removed or deactivated links changes path and uses the remaining links that are still active on the network. There is no need to introduce additional links to the network topology to compensate for these changes, as the number of removed or deactivated links constitutes only a small percentage of the total network topology. The proposed algorithm carefully assesses current seismic data, configuring the optimal network topology for the most efficient energy consumption results. In the event of seismic data changes in the future, the algorithm needs to be re-executed with the updated data as input. The backbone network remains fully functional and operational for all nodes, even in the scenario of removed or deactivated links. The algorithm primarily focuses on complete links rather than WDM channels. The assumption is that links consist of an identical set of WDM channels, and the deactivation or removal of links is treated as a single entity without considering the WDM level.  
For future work, we are planning to study if it is possible to produce an even further reduction in energy consumption of backbone WDM networks by using ZR+ optics.  
Conceptualization, G.A.B.; Methodology, D.N. and G.P.; Software, D.N.; Validation, D.N., G.A.B., G.P. and P.N.; Formal analysis, G.A.B., A.V., K.K., G.P. and P.N.; Investigation, D.N., G.A.B., A.V., K.K., G.P. and P.N.; Resources, D.N., G.A.B., A.V., K.K. and P.N.; Data curation, A.V. and K.K.; Writing--original draft, D.N.; Project administration, G.A.B. All authors have read and agreed to the published version of the manuscript.
## 5 State-of-the-Art
### Applications and Evaluation
The approach was implemented and evaluated on a suite of real-world Wasm binaries, and its efficiency was compared to previous works. In [110], the authors addressed the limitations of existing container-based serverless computing systems, such as cold-start issues and complex architecture for stateful services and multi-tenancy. The proposed solution involves positioning serverless functions according to data locality and executing them as a web-assembly sandbox. This approach results in improved execution latency and reduced network usage compared to existing architectures. The serverless runtime designed for this solution provides resource isolation (CPU, memory, file-system) and supports multi-tenancy executions. The architecture's effectiveness was assessed using IoT workloads with various performance metrics.
## 3 Results and Discussion
### Comparative Experiments  
The NAMRTNet in this paper consists of the modified ResNet-34, the NAM attention mechanism, and the TCN network. Two comparative studies were carried out to analyze the effectiveness of the network modules of NAMRTNet in this paper. The first group was ResNet+TCN (ResNet 50) and ResNet+TCN (modified ResNet 50). As ResNet-TCN does not support a 34-layer network structure, the exact condition of network level 50 was chosen for validation in order to verify the effectiveness of the modules in this paper.  
As shown in Figure 8, the accuracy, F1 score, and Cohen's Kappa (\(\kappa\)) of the improved ResNet network were 84.8, 77.7, and 0.79, respectively, which were higher than the results of the original network (85.8, 79.0, and 0.804). According to the confusion matrix results, N1, N3, and REM are the main stages with a facilitative effect. Thus, the improved network can effectively improve the problem of inaccurate information transfer, and the one-dimensional convolution is more suitable for such long time series of EEG data. Placing Max-pool between the Conv3x and Conv4x layers also reduces the length of the feature sequence by half, reducing the parameters and enhancing the expressiveness of the network.  
Figure 8: Confusion matrix of ResNet-TCN and improved ResNet-TCN. (**a**) ResNet 50-TCN confusion. (**b**) MResNet 50-TCN confusion.  
Figure 7: Comparison results of recognition rate, F1 score, and Cohen’s Kappa (\(\kappa\)) under different numbers of channels in TCN hidden layer.  
The second set of experiments was designed to verify the effectiveness of TCN. The comparison results of ResNet+NAM+LSTM (improved ResNet 34) and ResNet+NAM+TCN (improved ResNet 34) are shown in Figure 9. The application of TCN network structure improves the final recognition rate by 2.2%, and the prediction of each stage is enhanced to a certain extent, indicating that TCN can capture the time correlation of long time series well, and the improved network structure can overcome the imbalance of PSG data to a certain extent.
## 3 Results and Discussion
### Role of Vegetation Phenology on Eddy Fluxes  
The magnitudes of vegetation phenology (i.e., EVI) showed strong correspondence with the magnitudes of eddy fluxes (NEE and ET, Figure 8). Like EVI, the magnitudes of NEE and ET did not decline during grazing in 2015. The maximum 8-day average ET approached 6.4 mm d\({}^{-1}\) and NEE reached \(-\)3.2 g C m\({}^{-2}\) (sink of carbon) during grazing in 2015. As with EVI, the magnitudes of ET and NEE in 2016 were also smaller during grazing, as 8-day average ET was generally \(<\)3-4 mm d\({}^{-1}\) and the pasture was near carbon neutral. Strong correspondence between eddy fluxes and EVI suggests that changes in vegetation phenology or ecosystem structures with respect to management practices will influence carbon and water budgets in tallgrass prairie. These results were consistent with the findings of many studies that reported strong correspondence between seasonality of satellite-derived vegetation indices (e.g., EVI) and EC-measured/derived fluxes (e.g., ET and GPP) in tallgrass prairie [11; 35; 36; 37]. The strong correspondence between eddy fluxes and EVI resulted in strong linear relationships of EVI with ET and GPP (Figure 9). The result indicated that the EVI alone explained 75% and 83% of variations in ET in pastures 13 and 18, respectively. Owing to such strong relationships between EVI and ET in tallgrass prairie, [PERSON] et al. [35] developed a statistical model (ET = 0.11 PAR + 5.49 EVI - 1.43, R\({}^{2}\) = 0.86) using data from multiple AmeriFlux tallgrass prairie sites in the central United States to predict daily ET at 8-day intervals.  
There was also a strong relationship between EVI and GPP (Figure 9). However, the EVI at finer scales of spatial resolution showed stronger relationships, most likely due to less heterogeneity within the pixels (i.e., reduction in numbers of mixed pixels). Pasture 18 was divided into nine different paddocks that were grazed at different times during May to July, resulting in more heterogeneous conditions within the pasture. Landsat-derived EVI extracted for the paddock which contained the flux tower had the strongest relationship, followed by MODIS-derived EVI at 250 and 500 m spatial resolutions. The results illustrate that mismatch on spatial resolution of remote sensing and  
Figure 8: Patterns of the enhanced vegetation index (EVI), 8-day sum precipitation, and 8-day average net ecosystem CO\({}_{2}\) exchange (NEE) and evapotranspiration (ET) for pasture 13 in 2015 and 2016. Negative NEE indicates sink of carbon and positive NEE indicates source of carbon.  
EC footprints, and spatial heterogeneity of the flux measured area, can introduce uncertainties in modeling/upscaling of ground-measured eddy fluxes at large spatial scales using coarser spatial resolution remote sensing data.