esa-sceva/satcom-chunk-collection huggingface dataset:
# Local Differential Privacy-Based Federated Learning under Personalized Settings
## 4 Improved Aggregation Method
### Weighted Average Method  
In order to make the local models of clients with high privacy budgets contribute more to the federated model, and at the same time ensure the accuracy of the federated model, the first strategy we consider is to determine the weight of each client's contribution in the federated learning, according to the privacy budget set by each client. In the \(r\)-th round of the training, after collecting the perturbed local parameter \(\widetilde{G}_{i}^{(r)}\) from each client \(i\), the server first utilizes the privacy budget to compute a weighting coefficient \(\alpha_{i}\), for each client \(i\), and then performs a weighted average operation to obtain \(W^{(r)}\). Intuitively, \(\alpha_{i}\) represents the importance of client \(i\)'s local model in this round of learning. We compute \(\alpha_{i}\) as follows:  
\[\alpha_{i}=\frac{\rho_{i}}{\sum_{i=1}^{N}\rho_{i}}, \tag{11}\]  
where \(\rho_{i}\) denotes the inverse of the standard deviation \(\sigma_{i}\), of the Gaussian distribution of the \(i\)-th client, and \(\alpha_{i}\leq 1\). The larger the standard deviation \(\sigma_{i}\), the smaller the coefficient \(\alpha_{i}\) is, and the smaller the influence of \(\widetilde{G}_{i}^{(r)}\) on \(W^{(r)}\).  
Specifically, for independent identically distributed data, the model aggregation rule (line 7 in Algorithm 1) is now given by  
\[W^{(r)}=f(\widetilde{G}_{1}^{(r)},G_{2}^{(r)},\ldots,G_{N}^{(r)})=\frac{\sum_{i=1 }^{N}\alpha_{i}\widetilde{G}_{i}^{(r)}}{\sum_{i=1}^{N}\alpha_{i}}. \tag{12}\]  
For non-independent identically distributed data, the model aggregation rule (line 7 in Algorithm 2) is now given by  
\[W^{(r)}=sign(f(\widetilde{G}_{1}^{(r)},\widetilde{G}_{2}^{(r)},\ldots, \widetilde{G}_{N}^{(r)}))=sign(\frac{\sum_{i=1}^{N}\alpha_{i}\widetilde{G}_{i} ^{(r)}}{\sum_{i=1}^{N}\alpha_{i}}). \tag{13}\]
# Polarization-Insensitive Phase Modulators Based on an Embedded Silicon-Graphene-Silicon Waveguide
## 4 Conclusions  
To summarize, we propose a novel polarization-insensitive PM, based on an embedded silicon-graphene-silicon waveguide, and demonstrate a graphene-based polarization-insensitive phase modulator with the embedded waveguide. The phase modulator shows a 3-dB modulation bandwidth of 135.6 GHz and an optical bandwidth of over 500 nm. Moreover, it has a compact size of 60 \(\upmu\)m long and low insertion loss of 2.12 dB.  
[PERSON] and [PERSON] conceived the paper, and discussed the novel polarization-insensitive PM based on an embedded silicon-graphene-silicon waveguide. [PERSON] and [PERSON]. ([PERSON]) designed the graphene-based phase modulator model, and performed the simulation. [PERSON] and [PERSON] analyzed the data and conducted the figure drawing. [PERSON], [PERSON] ([PERSON]) and [PERSON] provided some valuable suggestions and revised the paper.  
This research was funded by National Nature Science Foundation of China (No. 61875240, 61421002, 61435010) and Science Foundation for Youths of Sichuan Province (No. 2016 JQ0014).  
The authors declare no conflict of interest.
# TASE: Task-Aware Speech Enhancement for Wake-Up Word Detection in Voice Assistants +
## 3 Materials and Methods
### Training  
Speech utterances are segmented with a fixed window length of \(1.5\) s, which is typically enough to cover the average duration of the WUW, which is \(1.0\) s, based on the SAD timestamps. Speech is combined randomly with background noises, following the procedure explained in Section 3.2, with a given SNR range. The SE model is trained to cover a wide SNR range of \([-10,50]\) dBs, whereas WUW models are trained to cover two scenarios: a classifier trained with the same SNR range as the SE model, and a classifier less aware of noise with a narrower SNR range of \([5,30]\) dBs. This way, it is possible to study the impact of the SE model regarding whether or not the classifier has been trained with more or less noise.  
We address data imbalance by balancing the classes in each batch using a weighted sampler. Besides, batching is done to ensure that negative samples from the OK Aura dataset are always present at each batch. This way, we increase the representation of negative samples which are phonetically similar to the WUW during training.  
The loss in Equation (1) allows us to train the models in multiple ways, and we define different SE models and classifiers based on the loss function used:  
1. Isolated classifier: we remove the autoencoder from the architecture (Figure 1) and train any of the classifiers using the noisy audio as input: \(\alpha=\beta=0\) and \(\gamma=1\).
2. Separate SE and classifier: we remove the classifier from the architecture and optimize the autoencoder based on the reconstruction losses only: \(\alpha=\beta=1\) and \(\gamma=0\).
3. Task-aware SE (TASE): operations of a frozen pretrained classifier are only backpropagated to the SE model, which is optimized with the reconstruction losses altogether: \(\alpha=\beta=\gamma=1\).
4. End-to-end TASE (TASE-E2E): autoencoder and classifier are trained jointly using the three losses: \(\alpha=\beta=\gamma=1\).  
All the models are trained with early stopping based on the validation loss with 60 epochs of patience, for a maximum number of 200 epochs. Additionally, the learning rate decreases in an order of magnitude if there is not improvement in 20 consecutive epochs. We use the Adam optimizer starting with a learning rate of \(10^{-4}\) in the E2E case and \(10^{-3}\) for the rest, always using a batch size of 50.
# Contrast-Invariant Edge Detection: A Methodological Advance in Medical Image Analysis
## 2 Preliminaries
### Bit Plane  
Bit planes occupy an important position in digital image processing. They are multiple planes formed by separating the grayscale pixel values by bit [30]. A typical 8-bit grayscale image can be split into 8-bit planes. Suppose that pixel \(P\) of an 8-bit grayscale image can be represented as Equation (1), where \(b_{i}(i=0,1,2,\ldots,7)\) is a binary digit with a value of either zero or one.  
\[P=\sum_{i=0}^{7}b_{i}\cdot 2^{i} \tag{1}\]  
For example, the binary representation of a pixel value of 206 is 11001110, which has a value of one in Bit Plane 7, one in Bit Plane 6, \(\ldots\), and zero in Bit Plane 0. \(P\) performs a bit plane decomposition into eight binary bit planes \(b_{7},b_{6},\ldots,b_{0}\), each bit plane \(b_{i}\) is formed by the \(i\)th bit of all pixels in the image. To visualize the information distribution in each bit plane more intuitively, we chose a CT image of the brain and decomposed it into 8-bit planes. Figure 2 shows the image information contained in each bit plane from high to low. It can be visualized that the high-bit planes hold a majority of the contour and structural information. The low-bit plane is barely visible as edges and contours and contains more detail and noise. The properties of the bit plane have led to extensive applications across multiple image processing domains. In image compression, image size can be reduced by selective compression based on the importance of the bit planes [31]. In image enhancement, the processing of the special localization plane allows targeted adjustment of the contrast, brightness, and other features of the image [32]. In image encryption, the transformation of specific bit planes enables secure protection of the image content by encrypting the pixel values within those planes using advanced cryptographic algorithms [33]. In digital watermarking, by selecting appropriate positions and methods to embed watermark information in bit planes, the function of copyright protection can be achieved without interfering with the normal visual effects of the image [34]. Usually, the lower bit planes are chosen to embed watermarks because the slight changes in the lower bit planes have no significant impact on the visual quality of the image. Overall,  
Figure 1: The demonstration of MSB and LSB with a pixel value of 149.  
the analysis of the bit plane helps to deeply understand the inner structure of the image, providing new perspectives and methods for image processing.
## 5 Results and Discussion
### Effectiveness on the Explainability Problem (Case Study)
#### 5.3.2 Influence of User's Historical Behavior  
Figure 5 illustrates the relevance between different history items \(v_{i}\) and the candidate item \(v_{c}\) from the attention score \(\mathbf{w}_{ci}\) obtained by the attention module in the user behavior-driven latent information extraction layer (HISTORY). The user discussed in Figure 5 is the same person discussed in Section 5.3.1. Note that the Movielens-1M dataset suffers a severe shortage of attributes, and there exists latent information which is not implied by the attributes. Therefore, generally, history items  
Figure 4: A heat map of attention weights for feature interactions on MovieLens-1M. The axes represent attribute fields of \(u\) and \(v_{c}\) (gender, age, occupation, zipcode, movie’s release year, genre). Influential feature interactions are highlighted with bright colors (Label = 1, Predicted CTR = 0.91).  
which have the similar genre and release year as the candidate item are given a high relevance score. It is apparent from the figure that this male user loves action and thriller movies. The reason that \(v_{13}\) and \(v_{14}\) are weighted very low is due to the fact that he has watched two romance movies and is currently tired of that same genre. He now wants to get back to the action and thriller movies again and chooses to watch \(v_{c}\).
## 6 Advantages of Including 3D Printing in the Manufacture of Prostheses
### Revolution in Customization  
One of the most significant changes that 3D printing brought to the realm of robotic prosthetics is the ability to customize at an unprecedented level. Through three-dimensional scans of the patient's body, designers can create devices that perfectly adapt to each person's anatomy, improving not only the functionality of the prosthesis but also comfort and aesthetics. This is especially relevant for children who require prostheses that can be adjusted or replaced as they grow [32]. Unlike traditional prostheses, which were often mass-produced and then adapted in a limited way to the individual needs of patients, 3D printing makes it possible to create tailor-made devices. Digital prosthetic models can be tailored exactly to the patient's dimensions and physical characteristics, resulting in a prosthesis that is not only more comfortable but also more functional [33]. This is particularly relevant for children, whose needs change rapidly as they grow, and 3D printing makes it possible to create prostheses that can be easily adjusted or replaced (Figure 6).  
Three-dimensional printing also offers the possibility of biomimetic designs, i.e., prostheses that mimic not only the shape but also the function of natural limbs. This has allowed for greater dexterity in robotic prosthetics, facilitating more precise and natural movements, something that previous technologies could not easily achieve.  
Figure 6: Three-dimensional-printed prostheses can be customized to fit the user’s size, different needs, and even personal preferences. The examples shown in the figure demonstrate the possibility of customizing the prostheses as if they were accessories [34].
## 3 Machinery Monitoring
### Dimensionality Reduction Using LDA  
In a system using triaxial accelerometers, infrared sensors, and temperature sensors, the following 5 signals would be received at one time: triaxial accelerometer (3 signals), infrared sensor (1 signal), and temperature sensor (1 signal). Extracting 7 features from each signal would result in a total of 35 features. However, this may also include irrelevant data, which could compromise recognition performance. Thus, we employ dimensionality reduction using LDA, which maps high-dimensional data onto a calculated vector, separates data into various categories, and tightens up data in each category. Dimensions with high feature value are then output as results. We adopted the two dispersion  
Figure 3: Example of feature extraction using windows.  
matrices outlined in [25], including covariance matrix \(S_{B}\) for between categories and covariance matrix \(S_{W}\) within categories. \(S_{B}\) is the predicted discrete vector values near the mixed averages, which is obtained as follows:  
\[S_{B}=\sum\
olimits_{\alpha=1}^{N}n_{\alpha}(m^{\alpha}-m)(m^{\alpha}-m)^{T} \tag{5}\]  
and \(S_{W}\) is the predicted vector dispersion sample of each category, which is obtained as follows:  
\[S_{W}=\sum\
olimits_{\alpha=1}^{N}n_{\alpha}{\sum\
olimits_{i=1}^{n_{\alpha} }}(x_{i}^{\alpha}-m^{\alpha})(x_{i}^{\alpha}-m^{\alpha})^{T} \tag{6}\]  
where \(N\) denotes the number of categories; \(n_{\alpha}\) is the number of samples in category \(\alpha\); \(x_{i}^{\alpha}\) represents sample \(i\) of category \(\alpha\); \(m^{\alpha}\) is the sample mean vector of category \(\alpha\), and \(m\) is the mean vector of all data. LDA produces mapping vector \(w\) to store the category integrity under low dimensionality, and \(w\) is used to maximize the formula below; that is, it maximizes the covariance between different categories and minimizes the covariance within a single category. In other words, the objective is to maximize \(J(w)\) in Equation (7). Once w has been calculated, the data can be mapped onto coordinate systems and new coordinates can be identified to reduce dimensionality  
\[J(w)=\frac{w^{T}S_{B}w}{w^{T}s_{w}w} \tag{7}\]  
Using Figure 3 as an example of dimensionality reduction, we can obtain the results in Figure 4, in which the points in different colors represent different lengths of springs. The blue points are the springs with their lengths greater than 194 mm. The points in red are the springs with their lengths locate between 189 mm to 193 mm. Finally, the black points are the springs with their lengths smaller than 188 mm.
## 4 Discussion
### Responses of Corn Yield to Integrated Straw Return and Tillage Practices  
Different tillage practices can affect corn yield by altering soil properties and hydrothermal conditions [68]. The results of this study showed that all straw incorporation treatments increased corn yield regardless of tillage method. However, the NTS treatment did not achieve significant yield improvement because straw mulching reduced soil temperature, which may negatively affect early crop growth, particularly in the cold climate of Northeast China [69]. In contrast, both RTS and PTS treatments significantly increased yield. This can be attributed to the following: (1) the abundant nutrients in straw that became available for microbial uptake after decomposition, especially carbon, thereby participating in nutrient cycling and increasing soil organic matter content; (2) the significant correlations between SOC component (ROC, DOC, and MBC) and the fungal and bacterial diversity indices (Chao1 and Simpson), which were also verified in this study; and (3) the slow release rate of straw-derived nutrients, which can continue to provide nutrients during the later growth stages of corn.  
The integrated straw return and tillage practices affected corn yield through multiple pathways. Correlation analysis revealed that SOC, its labile fractions, and soil microbial diversity collectively influenced corn yield. Specifically, SOC, ROC, and DOC contents were positive factors affecting yield, while the bacterial Simpson diversity index showed a significant negative correlation with yield. These findings are consistent with [PERSON] et al. [70], who reported that straw return increases both total SOC and the stability of labile organic carbon, thereby enhancing soil carbon sequestration capacity and improving fundamental soil productivity. Previous studies have demonstrated a linear relationship between SOM content and crop yield [71]. Tillage practices optimize microbial community composition by altering bacterial abundance and diversity. The negative effect of the Simpson index may be related to the uneven distribution of microbial populations and competition among microorganisms.  
In Northeast China, where corn accounts for 31% of the nation's total planting area [29], the experimental results demonstrate the importance of straw return for yield improvement. When combined with appropriate tillage practices, straw return can effectively enhance corn productivity.
# Prediction of Blade Tip Timing Sensor Waveforms Based on Radial Basis Function Neural Network
## 1 Introduction
[24] combined RBF NN with nonlinear BTT missile controllers, and introduced RBF NN to simulate nonlinear vectors, which utilizes the property of approximate convergence of RBF NN to converge the output signals to a small neighborhood to correct the uncertainties of aerodynamic parameters and smooth function vectors. This paper utilizes that property of the RBF NN to predict the BTT sensor waveforms based on experimental data to compensate for the waveform data under each BTC. [PERSON] et al. [25] proposed a high-dimensional aerodynamic acoustic optimization method based on the Kriging model and hovering rotor. [PERSON] et al. [26] proposed a hierarchical Kriging model that can be used for the agent model problem with variable fidelity, providing a derivation of a self-contained hierarchical Kriging model with more reasonable mean square error estimates. [PERSON] et al. [27] used a multi-fidelity optimization technique based on a single low-fidelity model of the Kriging method for aerodynamic optimization design using a Co-Kriging proxy model. [PERSON] et al. [28] constructed an efficient multi-fidelity agent model by utilizing the Co-Kriging method. The approach uses more low-fidelity information to raise the precision of the fidelity model agent and greatly reduces the computational cost of constructing the agent model. [PERSON] et al. [29] used the Kriging method to construct the local deviation and the radial basis function to construct the global model, and proposed a multi-fidelity optimization method based on the hybrid agent model. So, the main purpose of this article is to use the RBF model and static calibration data to predict the waveform of the BTT sensor, the connection between the Output Voltage (OV) of the eddy current sensors measured by the experimental bench with the BTC and the angle at which the blade cuts the magnetic susceptor, to predict the collected waveform data by using the RBF model, and then comparing and analyzing other models to draw the model with the highest prediction accuracy. The structural arrangement of the article is as follows: in Section 2, the output voltage, the clearance and the angle by the established static calibration experimental bench are measured. Section 3 outlines the establishment of the RBF model, Kriging model, BP model and polynomial fitting equations. In Section 4 we compare and verify the prediction accuracy as well as the prediction error of the RBF model, Kriging model, BP model, and polynomial fitting function. Finally, Section 5 draws the conclusion of the article.
## 1 Introduction
A faulty node could produce abnormal results which could intern bring down the entire network. In critical infrastructure applications these types of anomalies are as dangerous as those caused by intruders and should also be detected by proposed schemes. Several anomaly detection surveys which give comprehensive overviews of the relevant literature exist [23; 24; 25; 26]. These surveys however do not specifically consider resource constrained IWSN, which have conflicting priorities to conventional computer networks. To the best of our knowledge, there is also no survey which considers the applicability of anomaly detection to critical infrastructure applications (specifically water systems) taking the above into consideration. Intrusion detection systems specifically designed for WSNs are surveyed by the authors in [16; 27]. These schemes are however distinguishable from their anomaly detection counterparts which provide greater flexibility in these dynamic environments. The authors in [17; 28] surveyed the state of the art anomaly detection schemes with the former presenting a guideline for the selection of appropriate schemes depending on the specific application requirements. A brief review of anomaly detection in traditional SCADA systems was presented by the authors in [29] but this did not specifically consider IWSNs. This paper presents a brief overview of the work done in the field in recent years and highlights the practical considerations of the proposed schemes in these resource constrained environments. Critical water system infrastructure was chosen as the primary application of concern because of the increased number of cyber-attacks against water utilities in recent years [30]. The importance of protecting these systems is paramount and this paper looks at the challenges and limitations of this within the broader IWSNs anomaly detection application environment. Anomaly detection schemes have been classified based on different indicators (Section 2), for example, based on the detection model they can be classified into parametric (Section 3) and non-parametric (Section 4) models. This paper focuses more on the latter and investigates several non-parametric schemes which are more suitable for IWSNs because of their dynamic nature. Critical water system infrastructure is used as a case study to examine the applicability of these schemes to SCADA systems (Section 5) and some applications challenges are then discussed (Section 6). The schemes are then compared using several different performance measures as well as the inherent challenges and limitations (Section 7). Some open issues for future work are also discussed in the paper. This paper highlights important issues that need to be addressed in order for the field to evolve from being primarily concept-based, to one that is practically viable.
# Real-Time Contrail Monitoring and Mitigation Using CubeSat Constellations
## 6 Conclusions  
In conclusion, the study demonstrates that a 3 \(\times\) 3 satellite constellation system in highly eccentric orbits is an effective and practical solution for continuous contrail monitoring over the North Atlantic Corridor. Through simulation analysis, it was shown that increasing the number of planes and satellites per plane reduces blackout durations, maximizes the number of visible satellites, and extends contact periods, optimizing coverage for contrail monitoring. The configuration proposed in Section 4.3 provides uninterrupted signal availability across the target region of interest--a notable improvement compared to the 20% availability a single satellite can provide. This constellation design ensures that the northern hemisphere, particularly high-traffic regions like the North Atlantic, benefits from almost constant satellite availability. However, further research and refinements are recommended, particularly in optimizing handover timing and improving communication systems, to address the inherent challenges of signal transmission in eccentric orbits. This approach paves the way for improved atmospheric data collection and contributes to more sustainable aviation practices.  
Conceptualization, N.P., C.C. and V.P; methodology, N.P. and L.C.; software, N.P. and L.C.; validation, N.P., L.C., C.C. and V.P; writing--original draft preparation, N.P.; writing--review and editing, N.P., L.C., C.C. and V.P.; visualization, N.P. and L.C.; project administration, N.P., C.C. and V.P. All authors have read and agreed to the published version of the manuscript.  
[PERSON] is funded by the EPSRC Centre for Doctoral Training (CDT) in Geospatial Systems (ref EP/S023577/1).  
Not applicable.  
Data Availability Statement: The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.