esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Detection of Marine Litter: A General Framework to Leverage Synthetic Data
## 5 Conclusions  
In this study, we tackled the issues of data scarcity and quality for marine litter monitoring from satellite by proposing a methodology based on the use of synthetic data.  
The goal is to reduce the risk of overfitting, a general goal in machine learning, which is almost impossible to avoid with the currently available data. We showed that machine learning algorithms that are trained on synthetic data transfer well to real-life conditions, thus helping to overcome the issue of data scarcity. We hope that our work will help further develop the uses of synthetic data for remote sensing applications.  
Regarding the issue of data quality, we pointed out potential aliasing effects on S2 images of targets and noted their effects on the prediction. The aliasing effect has a huge influence on images containing plastic patches whose size is lower than or close to the GSD, but is less important (or non-existent) for plastic patches of larger sizes. To be sure to collect reliable data using S2, we found that the plastic patches should not be smaller than \(25\,\mathrm{m}\times 25\,\mathrm{m}\) for rectangle shapes and of \(15\,\mathrm{m}\) radius for circular shapes. This result applies only for spectral bands with GSD = \(10\,\mathrm{m}\); for spectral bands with lower spatial resolution the size limit increases. These size limits are not strict and should be seen as recommendations.  
Finally, even though we illustrated a proof of concept implementation of our methodology on the task of macro-plastic detection from S2 data, the simulator we developed is easily extendable to other satellites and other types of marine pollution. The models presented are adaptable and versatile. We strongly believe that following the guidelines established by our methodology will enable the development of novel algorithms for marine pollution detection from satellite data. The methodology could be used not only to train classifiers but also to foresee which existing (or future) satellites are more suited for plastic detection and to answer questions such as: Are there any other interesting wavelengths for plastic detection? How does the response change based on the plastic type and what plastic types are more easily detectable?  
Conceptualization, M.N., S.T. and P.B.; methodology, S.T. and P.B.; software, M.N., L.I. and M.S.; formal analysis, M.S.; investigation, M.N.; data curation, M.N., L.I. and M.S.; writing--original draft preparation, M.N.; writing--review and editing, L.I., S.T. and P.B.; visualization, M.N., L.I. and M.S.; supervision, S.T. and P.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
See Section 2.2.1 for instructions on how to access the data that was used in this study and the full source code of the simulator.  
The authors declare no conflict of interest.
# The _E. coli_ Global Regulator DksA Reduces Transcription during T4 Infection
## 1 Introduction
Pe's, which contain a strong match for the \(\sigma^{70}\)-dependent consensus \(-10\) and \(-35\) elements, do not require any viral factors for activity and are active immediately after infection. Thus, Pe's successfully compete with host DNA for the same pool of RNAP. Pm's, which become active \(\sim\)1 min after infection at 37 \({}^{\circ}\)C, require two T4-encoded proteins, MotA and AsiA, to direct RNAP to recognize a specific element, a MotA Box, centered at \(-30\). Importantly, middle genes are also expressed through the extension of early transcription from Pe's into downstream middle genes. Finally, PI's become active \(\sim\)5-7 min after infection at 37 \({}^{\circ}\)C. PI's harbor a novel \(-10\) sequence known as the late TATA box and require the T4 proteins gp55 (late sigma factor) and gp33 (co-activator) for activity. In addition, late transcription requires active T4 DNA replication through T4 protein gp45, which is the replication sliding clamp that also serves as an enhancer for PI's [24]. As a further complication, phage T4 DNA is modified by glucosylated, hydroxymethyl 5-cytosine, a moiety that can alter RNAP activity as well as the interactions of factors that normally function with unmodified DNA templates. In this study, we report the first global transcriptome analyses of T4 wild type (wt) and a T4 _notA_ knockdown (T4_motA_m_) infections of a wild-type laboratory strain and a _dksA_ mutant host strain, and we investigate the effects of a DksA or ppGpp deletion on T4 growth and transcription. We demonstrate that a deletion of either DksA or ppGpp results in significantly larger plaques for either T4 wt or T4_motA_m_. Infections in a ppGpp\({}^{0}\) host have a marginal effect on burst size, latent period, or phage transcript abundance. However, a deletion of DksA results in a 2-fold increase in T4 wt burst size and increased levels of several Pe RNAs. As the T4 activator MotA is required for Pm activation, a T4 _motA_ knockdown results in poor phage growth. Our transcriptome analyses reveal that middle genes are affected differentially, ranging from slight to severe inhibition of gene expression. We show that the absence of DksA in the T4 _motA_ knockdown background also increases transcription from specific Pe's. This increase ameliorates the poor growth of T4_motAam_ by increasing expression of downstream middle genes needed for replication, recombination, and late transcription. As we do not observe an effect of DksA on Pe transcription in a purified in vitro transcription system, we speculate that DksA decreases T4 early transcription through other changes present in the infected _\(\Delta\)dksA_ host or by using additional host/phage factors.
# AI-Supported EUD for Data Visualization: An Exploratory Case Study
## 4 Phase 2: Answering RQ2
### The AI-Supported Data Visualization Prototype
If the user needs to refine the request further, adjust the visualization format, or explore additional insights, they can, enabling an iterative and dynamic data analysis experience. The user profiles described in the previous section have been employed to personalize the interaction with the application. The primary factors considered in this personalization include the type of chart generated, any additional explanations provided in the chat, and potential suggestions for alternative visualizations suitable for the requested data and the user profile. For the _basic_ profile (see Section 3.3.1), the preference for simple chart types is respected, and explanatory messages in the chat can be particularly useful. An example of interaction with the system is presented in Figure 3, where the simplest visualization type is displayed (i.e., a table), along with an additional explanation of it in the chat. No suggestions for more advanced visualizations are offered, as this profile targets users with limited data visualization experience. In the case of the _intermediate_ profile (see Section 3.3.2), Figure 4 illustrates a slightly more complex visualization type (i.e., a bar chart), along with an explanatory message. Additionally, the system presented the user with a suggestion to view the data through an alternative representation (i.e., a pie chart). Figure 3: An example of interaction with the prototype for the basic profile. Lastly, the _advanced_ profile (see Section 3.3.3) is provided with more complex visualizations and no or limited explanations in the chat. For example, Figure 5 shows a heatmap visualization of the data requested by the user. In this case, no explanations are provided in the chat, reflecting the assumption that advanced users are already familiar with the visual representation. Similarly, no alternative suggestions are offered, as these users are expected to know what type of chart they require and may request the one they consider most appropriate for their needs. In addition to the profile-based personalization just described, users always have the option to further refine their requests in a dynamic and interactive way. Any user may request a refinement of the type of visualization; for instance, by asking to view the requested data in an alternative chart. They may also request a refinement of the data itself, such as specifying a particular time interval or applying specific filters. Furthermore, users can request a refinement of the chat response, asking for more detailed explanations of the extracted data, including details on how to better understand the provided visualization. The objective of this refinement process is to allow users to tailor the interaction based on their individual goals and familiarity with the data. By adjusting the type of visualization, selecting specific subsets of data, or requesting further clarification through the chat, users can gradually adapt both the content and the form of the output to better suit their needs.
# A Dynamic Federated Identity Management Using OpenID Connect
## 1 Introduction
OIDC is considered one of the first options by software developers when it comes to application programmable interface's (API) security [7], since it provides:  
* Easy consumption of identity tokens: an end-user's identity is received by client applications in a secure JSON Web Token (JWT) called the ID token. JNTs are portable and support a range of signature and encryption algorithms [7]. * OAuth 2.0 protocol: OIDC is built on top of OAuth 2.0, meaning that clients use OAuth 2.0's various authorization grant types to obtain ID tokens and access tokens, which work with both web applications and native mobile applications [7]. * Simplicity: OIDC is relatively simple to integrate with applications, while also offering features and security options that can meet demanding enterprise requirements [7]. Through OIDC, organizations can establish a federated environment with third-party providers in order to extend their services. However, the process of establishing a federated identity is a relatively complex task. The complexity comes from the rapid need of businesses collaborations and outsourcing. This study addresses the following research question: How can a dynamic OIDC federated identity model be applied to assist organizations to scale-up to a large number of business partners? To answer this question, we propose a model that is able to achieve a dynamic identity federation between two entities leveraging OIDC. The model is based on three major OIDC specifications [8; 9; 10]. The model utilizes those specifications to allow OIDC relying parties to locate an end-user's identity providers, register with them as an OIDC relying party (RP) and establish a trust through public key infrastructure (PKI) in order to obtain the needed information for authenticating an end-user and achieving identity federation.
## 3 Methods
### Data Analysis
#### 3.6.1 Attention Map Generation
Unlike the other attention maps, this procedure resulted in maps with hard edges, making it unclear which pixels should be used if we wanted to limit the mask size to exactly 5% of the image. The final attention map for the drawings was thus obtained after smoothing the prior map. This was carried out via average pooling using a 3 \(\times\) 3 kernel. **Grad-CAM attention maps**. To generate CNN attention maps, Grad-CAM uses the gradients of any target class, flowing into the final convolutional layer to produce a coarse localization map that highlights the areas in the image that were important for predicting the class. For a given CNN and a selected class, the Grad-CAM algorithm generates an attention map, a so-called class-discriminative localization map (CDLM) for each input image. This is carried out by examining the activation flow from the last convolutional layer to the output (in other words: how the activation values influence the numerical score of the selected class). The result of the last convolutional layer can be interpreted as a collection of K feature maps, where in our case K = 2048. The last convolutional layer is chosen as it is expected \"to have the best compromise between high-level semantics and detailed spatial information\" ([38], p. 4). In a first step, the gradient (i.e., the relative change in the result when changing the input) of the selected class with respect to each \"pixel\" in each feature map is calculated. These matrices are then averaged over the pixel dimension to obtain K so-called \"importance weights\" alpha_k. In a second step, each feature map is weighted by these alpha_k. Any negative values in the maps are set to 0 (via so-called ReLU nonlinearity), focusing the map only on areas with a positive impact on the class decision. This results in K-weighted feature maps, which can be represented as an F \(\times\) F \(\times\) K tensor where F denotes the feature map resolution. As a final step, the pixel-wiseaverage of those maps is taken (i.e., along the last axis of the F \(\times\) F \(\times\) K tensor) to obtain the desired CDLM. Naturally, the CDLM has the same resolution (F \(\times\) F) as the output of the last convolutional layer, which is typically much lower than that of the original image, in our case 14 \(\times\) 14. To apply the CDLM to our input images, bilinear upsampling was used, resulting in an attention map with the same resolution as the original image (in our case 1024 \(\times\) 1024). Such an attention map contains relevance values between 0 and 1 for every pixel in the original image and could be visualized as a heatmap or density map but it can also be transformed into a binary map. This was achieved by choosing the threshold for the relevance score such that only 5% of the pixels (and thus of the total area) was included.
# Impact of Aerodynamic Interactions on Aeroelastic Stability of Wing-Propeller Systems +
###### Abstract  
This paper presents initial findings from aeroelastic studies conducted on a wing-propeller model, aimed at evaluating the impact of aerodynamic interactions on wing flutter mechanisms and overall aeroelastic performance. The flutter onset is assessed using a frequency-domain method. Mid-fidelity tools based on the time-domain approach are then exploited to account for the complex aerodynamic interaction between the propeller and the wing. Specifically, the open-source software DUST and MBDyn are leveraged for this purpose. The investigation covers both windmilling and thrusting conditions. During the trim process, adjustments to the collective pitch of the blades are made to ensure consistency across operational points. Time histories are then analyzed to pinpoint flutter onset, and corresponding frequencies and damping ratios are identified. The results reveal a marginal destabilizing effect of aerodynamic interaction on flutter speed, approximately 5%. Notably, the thrusting condition demonstrates a greater destabilizing influence compared to the windmilling case. These comprehensive findings enhance the understanding of the aerodynamic behavior of such systems and offer valuable insights for early design predictions and the development of streamlined models for future endeavors.  
aeroelasticity; whirl flutter; aerodynamic interactions; vortex particle method; multibody dynamics
## 2 Design Method of Feedback Controller
### Feedback Controller  
In Figure 1a, the secondary path \(G\) defined in the acoustic domain goes from the headphone speaker (SPK) to the feedback microphone (FB MIC) in the front cavity of the headphone, corresponding to the controller circuit \(H\) consisting of a microphone preamplifier, feedback filter, and power amplifier in the electronic domain. The external noise passing through the headphone shell into the chamber is the primary noise \(d\). After the controller is turned on, the remaining error noise is \(e\). In Figure 1b, where \(d(t)\) and \(e(t)\) are the primary noise signal and error noise signal, respectively, and \(G(\mathrm{j}\omega)\) and \(H(\mathrm{j}\omega)\) are the transfer functions of the secondary path and controller circuit in the complex frequency domain, respectively.  
The system sensitivity function is defined as the transfer function between the primary noise signal and the error noise signal, as follows:  
\[S(\mathrm{j}\omega)=\frac{E(\mathrm{j}\omega)}{D(\mathrm{j}\omega)}=\frac{1}{ 1-L(\mathrm{j}\omega)} \tag{5}\]  
where \(L(\mathrm{j}\omega)=G(\mathrm{j}\omega)H(\mathrm{j}\omega)\) is the open-loop transfer function of the system. In addition, the complementary sensitivity function of the system is defined as follows:  
\[T(\mathrm{j}\omega)=1-S(\mathrm{j}\omega)=\frac{-L(\mathrm{j}\omega)}{1-L( \mathrm{j}\omega)} \tag{6}\]  
From the point of view of noise suppression, when the primary noise exists, the error signal picked up by the error microphone should be the smallest in terms of mean square.  
\[\mathrm{E}\Big{[}e^{2}(t)\Big{]}=\frac{1}{\pi}\int_{0}^{\infty}\Phi_{e}( \omega)=\frac{1}{\pi}\int_{0}^{\infty}|S(\mathrm{j}\omega)|^{2}\Phi_{d}( \omega)d\omega=\|S(\mathrm{j}\omega)w_{1}(\omega)\|_{2}^{2} \tag{7}\]  
where \(\mathrm{E}[\;\;]\) represents the mathematical expectation, and \(\Phi_{e}(\omega)\) and \(\Phi_{d}(\omega)\) are the power spectral densities of \(e(t)\) and \(d(t)\), respectively.  
Figure 1: (**a**) Schematic drawing of feedback control headphone; (**b**) block diagram of feedback system.  
The objective function and constraint of the \(\rm H2/H\infty\) framework is set as follows:  
\[\rm min\Big{[}\|S(j\omega)w_{1}(\omega)\|_{2}^{2}\Big{]} \tag{8}\]  
\[\|S(j\omega)w_{2}(\omega)\|_{\infty}<1 \tag{9}\]  
where \(w_{1}(\omega)\) is the root mean square value of the power spectral density of primary noise, and \(w_{2}(\omega)\) is the limit of noise lift at each frequency.  
From the perspective of system stability, an open-loop transfer function should have a certain amplitude margin, defined as \(L_{\rm AM}\), and phase margin, defined as \(L_{\rm PM}\). The stability constraint of the feedback system is set as follows:  
\[\begin{split} L_{\rm AM}&=10^{(-10/20)}\\ L_{\rm PM}&=\pi/6\end{split} \tag{10}\]  
For an invariable specified secondary path, the above objective function and constraints, defined as method 1, can ensure that the system can achieve a good balance between noise reduction and waterbed lift, but the secondary path in a real system has uncertainties, so it is necessary to add an uncertainty constraint.
## 2 Background  
### Discretization Error Due to Image Processing  
The geometric micro-scale-models play an important function when a FEA is performed with the objective to estimate the mechanical behavior. The microscale-models are usually obtained from the nodules contours of a DCI micrograph (Figure 1a) using image processing techniques, as shown in Figure 1b, and from this data, the geometric models are generated, as it can be observed in Figure 1c, in this figure the model was generated using commercial software called img2 CAD (Img2 CAD LLC., Cologne, Germany). As it can be seen in Figure 1c, some regions of the contour of the model are completely straight due to the contour that is computed at the pixel level; besides, other sections have corners with angles of 45\({}^{\circ}\) and 90\({}^{\circ}\), which can produce stress concentrators in a FEA. Furthermore, these sections do not represent the geometric characteristics of graphite nodules, since a DCI is characterized by the semi-sphericity of its graphite nodules, as they have a _CSF_ value of between 0.9 and 1 [15]. These geometric characteristics are a part of the errors that are produced by the discretization at the pixel level at the time of obtaining the contours through image processing. Although the geometric variations may be too small to observe, they have an effect on the estimation of the mechanical properties in a FEA using the models obtained. Therefore, new alternatives are required to create methodologies capable of generating models, which reduce the possible errors due to geometric discretization.
# Monitoring Water Area Dynamics in Kashgar (2003-2023) Using Multi-Source Remote Sensing Data
## 5 Conclusions  
This study investigated the spatiotemporal dynamics of water bodies in Kashgar, Xinjiang, China, from 2003 to 2023, utilizing Landsat and Sentinel-2 imagery. Water bodies were delineated using a random forest algorithm. The results revealed a fluctuating trend in the total water area, exhibiting a slight overall decrease. The seasonal variations mirrored this trend, displaying a fluctuating downward trajectory. Conversely, the permanent water bodies demonstrated an upward trend. The statistical analysis indicated a positive correlation between water area and both precipitation and temperature, and a negative correlation with evaporation. Notably, precipitation in Taxkorgan and Yecheng counties, influenced by topographic factors, exerted a significant impact on water area changes, exhibiting strong correlations (R2 = 0.5687 and 0.5461, respectively). This highlights the crucial role of regional climatic factors in shaping the overall dynamics of water bodies in Kashgar. These findings provide a robust scientific foundation for developing effective regional water resource management policies and ecological conservation strategies.  
Conceptualization, C.D. and C.R.; methodology, C.D.; software, C.D.; validation, C.D. and C.R.; formal analysis, C.D.; investigation, C.D.; resources, C.D.; data curation, C.D.; writing--original draft preparation, C.D.; writing--review and editing, C.R.; visualization, C.D.; supervision, C.R.; project administration, C.R.; funding acquisition, C.R. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (Grant No. 42064003). The author is located at the Guilin University of Technology.  
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.
# Rolling Bearing Fault Diagnosis Based on CEEMDAN and CNN-SVM
###### Abstract  
The vibration signals collected by acceleration sensors are interspersed with noise interference, which increases the difficulty of fault diagnosis for rolling bearings. For this reason, a rolling bearing fault diagnosis method based on complete ensemble empirical model decomposition with adaptive noise (CEEMDAN) and improved convolutional neural network (CNN) is proposed. Firstly, the original vibration signal is decomposed into a series of intrinsic modal function (IMF) components using the CEEMDAN algorithm, the components are filtered according to the correlation coefficients and the signals are reconstructed. Secondly, the reconstructed signals are converted into a two-dimensional grey-scale map and input into a convolutional neural network to extract the features. Lastly, the features are inputted into a support vector machine (SVM) with the optimised parameters of the grey wolf optimiser (GWO) to perform the identification and classification. The experimental results show that the rolling bearing fault diagnosis method based on CEEMDAN and CNN-SVM proposed in this paper can significantly reduce the noise interference, and its average fault diagnosis accuracy is as high as 99.25%. Therefore, it is feasible to apply it in the field of rolling bearing fault diagnosis.  
rolling bearing; complete ensemble empirical model decomposition with adaptive noise; convolutional neural network; support vector machine; fault diagnosis +
Footnote †: journal: Applied sciences