esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In order to better remove baseline wander interference and Gaussian white noise, a BP-ADMM algorithm is proposed for ECG denoising and reconstruction through combining low-pass filtering and CS recovery. The BP-ADMM algorithm is studied on the basis of the traditional BP algorithm, which reconstructs the ECG signals with denoising. The method introduces dual variables, adds a secondary penalty term, and reduces constraint conditions through alternate optimization to optimize the original variables and the dual variables at the same time. The dual decomposition method improves the calculation speed by decomposing the objective function in parallel. The effectiveness of the algorithm is verified by the records from the MIT-BIH arrhythmia database. The experiments show that this denoising method can effectively remove baseline wander interference and random Gaussian white noise in ECG signals. The proposed framework outperforms the wavelet and average algorithms in SNR and MSE. The waveform of ECG signals is much smoother and more details are preserved.  
Conceptualization, R.L. and M.S.; methodology, R.L.; validation, R.L., C.C.; formal analysis, M.S. and R.L.; investigation, R.L.; resources, M.S. and C.C.; writing--original draft preparation, R.L.; writing--review and editing, C.C. and M.S.; supervision, M.S. and R.L.; project administration, M.S.; funding acquisition, M.S. and C.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Taishan Scholars Program: Distinguished Taishan Scholars in Climbing Plan (NO. tspd20181211), Young Taishan Scholars (NO. tsqn201909137).  
Not applicable.  
The authors declare no conflict of interest.
# Multi-Input Attention Network for Dehazing of Remote Sensing Images
## 1 Introduction
Meanwhile, adding an improved attention module to the connection part further improves feature extraction. Finally, the network can effectively remove the disturbances, including clouds and cloud shadows under non-uniform distribution. Figure 1: The visible (RGB) band and the infrared bands in the image captured by Sentinel-2.
# An Efficient Digital Channelized Receiver for Low SNR and Wideband Chirp Signals Detection
## 2 Methodology
### Proposed Algorithm Description
#### 2.2.1 WOLA Channelization Structure  
To develop the WOLA channelization structure, we begin with the low-pass channelization structure shown in Figure 2. Recall that the output signal \(y_{k}(m)\) for the \(k_{th}\) channel of the filter bank analyzer can be expressed in the form [33]  
\[\begin{split} y_{k}(m)&=\sum_{n=-\infty}^{\infty}h(mD -n)x(n)exp(-j\omega_{k}n)\\ &=\sum_{n=-\infty}^{\infty}h(mD-n)x(n)W_{K}^{-km}\end{split} \tag{6}\]  
where \(\omega_{k}=2\pi k/K\), \(W_{K}=exp(j2\pi/K)\), \(k=0,1,\cdots,K-1\).  
According to the discrete short-time Fourier transform (STFT) definition, \(y_{k}(m)\) can be regarded as the short-time spectrum at time \(n=mD\). The filter \(h(n)\) acts as a sliding analysis window that selects and weights the short-time segment of the signal \(x(n)\) to be analyzed. In a practical implementation, the data slide by in time, and the filter is invariant. Thus, it is convenient to convert the data reference point from a fixed time frame to a sliding time frame, which is accomplished by the change of variables  
\[r=n-mD \tag{7}\]  
Then, the short-time transform \(y_{k}(m)\) can be expressed as [33]  
\[\begin{split} y_{k}(m)&=\sum_{r=-\infty}^{\infty}h(- r)x(r+mD)W_{K}^{-k(r+mD)}\\ &=W_{K}^{-kmD}\sum_{n=-\infty}^{\infty}h(-r)x(r+mD)W_{K}^{-kr}\\ &=W_{K}^{-kmD}\tilde{X}_{k}(m)\end{split} \tag{8}\]  
where \(\tilde{X}_{k}(m)\) is defined as STFT, that is  
\[\tilde{X}_{k}(m)=\sum_{n=-\infty}^{\infty}h(-r)x(r+mD)W_{K}^{-kr} \tag{9}\]  
Let  
\[y_{m}(r)=h(-r)x(r+mD) \tag{10}\]  
Figure 4: The detailed flow of the proposed digital channelized receiver architecture.  
For efficient implementation of Equation (9) with the aid of the FFT algorithm, the sequence \(y_{m}(r)\) should be time aliased into the form of a \(K\)-point sequence \(x_{m}(r)\). The sequence \(x_{m}(r)\) can be expressed as [33]  
\[x_{m}(r)=\sum_{l=-\infty}^{\infty}y_{m}(r+lK) \tag{11}\]  
So that Equation 9 becomes [33]  
\[\widehat{X}_{k}(m)=\sum_{r=0}^{K-1}x_{m}(r)W_{K}^{-kr} \tag{12}\]  
Thus  
\[y_{k}(m)=W_{K}^{-kmD}\sum_{r=0}^{K-1}x_{m}(r)W_{K}^{-kr} \tag{13}\]  
The WOLA channelization structure is obtained from the above process, as shown in Figure 5. The WOLA channelization structure implements the analysis window using a direct extraction structure, which improves the efficiency by \(D\) times. In addition, \(K\) filter channels share the same windowing process, increasing efficiency by a factor of \(K\). Moreover, the FFT is efficient. The WOLA structure has no restrictions on the number of channels \(K\) and the decimation multiple \(D\). Thus, the decimation multiple can be flexibly adjusted according to the frequency response of the prototype filter to ensure accurate frequency measurement with the lowest channel sampling rate.
## 3 PID Control of the Dynamical Model
### Proposed Control Strategy  
The existing control strategy [14] consists of blocking the motion of points \(A_{i}\)\((i=1,2,4)\) in all directions, \(x\), \(y\), and \(z\), and accordingly calculating the desired angles at the actuators \(\theta_{id}\)\((i=1,\,2,\,4)\) using Equation (3). However, this approach encounters challenges in maintaining the positions of points \(A_{i}\) at their initial values, as it fails to prevent the fluctuations in the \(x\) and \(y\) coordinates, which subsequently lead to errors in the \(z\) position. This inadequacy is clearly illustrated in Figure 5, which highlights the failure in maintaining the horizontal position of the platform and its stability.  
To overcome this issue, an alternative solution is proposed here and consists of allowing variations in the \(x\) and \(y\) positions. This solution involves computing the desired angles at the actuators using Equation (3) by considering the \(x\) and \(y\) positions at each instant, while keeping the initial value of the \(z\) coordinate constant. Implementing this solution has proven effective in stabilizing the moving plate, as evidenced by the results presented in Figure 8.  
Figure 8: Position of the upper moving plate of 3-\(\underline{\text{SRR}}\)/SRU when \(\alpha=30^{\circ}\), \(\beta=30^{\circ}\), and \(\Delta Z=0.6\) mm using the proposed strategy.  
Figure 7: Variation in the position of point \(A_{1}\) on the moving plate.  
In summary, the proposed solution acknowledges that small fluctuations in the \(x\) and \(y\) coordinates can be tolerated to some extent. However, it emphasizes the importance of effectively controlling variations in the \(z\) position to ensure the stability of the platform.  
Furthermore, with the implementation of the proposed solution, the actual angle \(\theta_{3}\) exhibited precise tracking of the newly calculated desired angle \(\theta_{3d}\), as clearly demonstrated in Figure 9.  
Nonetheless, the proposed solution has a minor drawback, which is a slight increase in variation in the \(x\) and \(y\) positions compared to the previous approach, as depicted in Figure 10.  
The results demonstrate that incorporating the resulting \(x\) and \(y\) positions at each instant in the control strategy yields superior outcomes and effectively resolves the dynamical issue at hand.  
Figure 10: Coordinates of \(A_{1}\) using the previous approach and the new approach.  
Figure 9: Comparison of the desired and actual values of angle \(\theta_{3}\) using the proposed strategy.
# A Multi-Head LSTM Architecture for Bankruptcy Prediction with Time Series Accounting Data
## 7 Temporal Window Selection
As expected, increasing the window length led to a longer training time on average for all the models (Table 4). However, all the models required just a few seconds of training with our hardware settings. However, the ensemble models performed better than SVM and LR with more variables and a larger window length. Moreover, using the 18 accounting variables for 4 or 5 consecutive years yielded a better result in terms of AUC, in particular with Random Forest (0.759 for \(WL=4\)) and CatBoost (0.771 for \(WL=5\)). A possible consideration is that although RF and CB showed similar overall performance, the computation time required to train CB was almost six times that required for RF. However, the computation time was not relevant in our experiments since the dataset was not particularly large. In light of this, we selected \(WL=4\) and \(WL=5\) to further study time series data with Recurrent Neural Networks. Figure 5: Average AUC on the validation set over 100 runs for each model for different window lengths (WL) in years. The training set was randomly balanced for every run, while the validation set was imbalanced. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**ML Model**} & \multicolumn{6}{c}{**Average AUC**} \\ \cline{2-6}  & **WL = 1** & **WL = 2** & **WL = 3** & **WL = 4** & **WL = 5** \\ \hline Support Vector Machine & 0.635 & 0.641 & 0.594 & 0.589 & 0.587 \\ Logistic Regression & 0.731 & 0.648 & 0.676 & 0.705 & 0.702 \\ AdaBoost & 0.647 & 0.655 & 0.664 & 0.642 & 0.719 \\ Random Forest & 0.745 & 0.733 & 0.731 & **0.759** & 0.760 \\ Gradient Boosting & 0.716 & 0.702 & 0.729 & 0.685 & 0.742 \\ XGBoost & 0.678 & 0.730 & 0.695 & 0.699 & 0.726 \\ CatBoost & 0.724 & 0.729 & 0.686 & 0.749 & **0.771** \\ LightGBM & 0.751 & 0.699 & 0.671 & 0.741 & 0.736 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average AUC on the validation set depending on the number of years considered (window length, WL). The best results for \(WL=4\) and \(WL=5\) are highlighted in boldface.
# Supply Chain Data Analytics for Digital Twins:
## 3 Digital Twin Analytics Framework
### Integration with Lifecycle Analysis and Process Model Simulators  
The framework is designed as an extensible system that enables integration with other services that provide additional value. Two services, the Lifecycle Assessment (LCA) and the Process Model Simulator (PSM), have been integrated with the system, showcasing the framework's ability to enrich core Digital Twin data with advanced analytics, insights and KPIs.  
The lifecycle assessment (LCA) component is an external service that calculates on demand the environmental footprint of all different processes in the production pipeline, given the specific parameters of the production (for example the weight of oranges that are given as input). The footprint is a complex calculation that requires knowledge of the specific domain and information about the factory's infrastructure, the processes and the machines that are used. The analytics system utilizes LCA services by using their API endpoints.  
The analytics system is internally integrated with Process Simulation Tools (PSM). These tools are essential for the system because of their capability of simulating all of the processes in the production pipeline. These tools have modelled every process and component inside the factory, by analyzing them and documenting every characteristic. The analytics system is integrated with these tools and utilizes them when an anomaly is detected or when it is necessary for the engineers of the factory to change a production parameter in the process.  
By simulating every potential new parameter in the production machines, analyzing the simulation results, the energy consumption and the environmental footprint, production engineers are enabled to make the best decision for an optimized and sustainable factory process pipeline. This extensibility allows industrial users to leverage both framework and external services to maximize the value from their investments in Digital Twin software technologies and installations, as evaluated in the solution developed during the Plooto project ([[https://www.plooto-project.eu/](https://www.plooto-project.eu/)]([https://www.plooto-project.eu/](https://www.plooto-project.eu/))).  
A complete overview of the proposed conceptual architecture, including external services can be found in Figure 2. The proposed analytics framework supports the entire insights lifecycle, encompassing data ingestion, transformation, and insight generation through notifications and visualizations.  
Figure 2: Conceptual architecture of framework’s components and communication with digital twins and external services.
# The Applications of MALDI-TOF MS in the Diagnosis of Microbiological Food Contamination
## 4 Limitations and Challenges
### Inability to Detect Viable but Non-Culturable (VBNC) Organisms  
MALDI-TOF MS identification typically requires cultured isolates. As such, it is inherently limited in detecting microorganisms in a VBNC state or in environments where selective media are ineffective. Many foodborne pathogens, including _Campylobacter_, _Salmonella_, and _Listeria_, can enter VBNC states under stress conditions (e.g., cold storage, disinfectants), yet retain pathogenic potential. Their absence in culture does not equate to absence in the product. To overcome this, MALDI-TOF MS must be integrated into techniques capable of detecting non-culturable cells, such as flow cytometry, viability PCR, or molecular assays targeting active transcription [15; 28; 139].
# Lyapunov-Guided Energy Scheduling and Computation Offloading for Solar-Powered WSN
## 3 Problem Formulation
Lyapunov-Based Joint Online Optimization Algorithm for Partial Task Offloading Decisions and Resource Allocation  
Since problem P1 is a non-convex fractional programming problem, it is hard to tackle directly. We first transform problem P1 into an integer form using [PERSON]'s theorem [35], as shown in problem P2. \[ \begin{array}{l}\text{P2}:\underset{\Omega}{\max}\sum\limits_{k=1}^{|\mathcal{ K}|}\overline{\Phi}_{k}^{t}-\eta_{EE}^{t}\overline{E}_{total}^{t}\\ \text{s.f.C1}\sim\text{C9}\end{array}  \tag{13}\]  
As problem P2 has long-term average optimization included in both the objective function and constraints, it is difficult to solve by traditional optimization methods. In this paper, we use Lyapunov optimization theory [36] to solve the problem. Compared with the MDP method, Lyapunov optimization does not depend on state transfer, has lower computational complexity, which can achieve an optimized system performance objective while stabilizing the network queue, and is an effective framework for designing online control algorithms highly adaptive to problem P2. Meanwhile, since the Lyapunov function is a non-negative scalar measure for the multidimensional state of the system, the Lyapunov optimization ensures the stability of the system in different forms mainly by controlling the drift of this function to the non-zero direction. The Lyapunov drift is crucial to the study of optimal control in queuing networks, which can be widely used in network optimization involving scheduling, allocation, and other fairness problems.
# Computer-Aided Drug Design Boosts RAS Inhibitor Discovery
## 3 Application of CADD Methods in the Development of RAS Inhibitors
### Molecular Dynamics (MD) Simulation
Overall, SPRED1 enhances RAS-GTP hydrolysis [121]. The long-range allosteric regulation also exists in the catalytic KRas4B\({}^{\text{G13D}}\) (nucleotide-free)-SOS-KRas4B\({}^{\text{G13D}}\)-GTP ternary complex. According to the MD simulation result, the binding of KRas4B\({}^{\text{G13D}}\)-GTP at the distal allosteric site of SOS increases the affinity between the catalytic KRas4B\({}^{\text{G13D}}\) and SOS and promotes the removal of Switch I from the nucleotide binding site. These facts lead to a higher nucleotide exchange rate and generate more GTP-binding RAS for allosteric regulation as a positive feedback loop [122]. In general, MD simulation can firstly reveal the mechanism of the existing drugs at the atomic level, create new models for the potential drug-protein or protein-protein interactions, and design the precursor compounds. Second, the MD simulation can predict ligands as agonists or antagonists, with spatial resolution at the atomic level and temporal resolution at the femtosecond level. Third, the MD simulation can explore the allosteric sites and capture the temporal phase of the effect at the kinetic level and the receptor binding affinity. Finally, MD simulation can provide a more realistic and detailed atlas of pharmacodynamics and is more useful for drug design. Consequently, MD simulation is very helpful for the discovery of promising RAS inhibitors.
## 4 Results
### Input Validation Performance: Prompt Injection Mitigation  
The AVI PoC's Input Validation Module (IVM) was evaluated for its ability to detect and neutralize prompt injection attacks using a subset of 95 attacks from the AdvBench benchmark [53]. The baseline Attack Success Rate (ASR) for the target LLM (Grok-2-1212) against this subset was estimated at approximately 78%. When interactions were routed through the AVI PoC, the system demonstrated significant mitigation capabilities. The IVM successfully blocked or neutralized -82% of the attempted injection attacks, resulting in a reduced ASR of -14%. Detailed results comparing the baseline ASR with the ASR achieved using AVI are presented in Table 4. These findings suggest that the IVM component of the AVI framework can substantially reduce the risk of users successfully manipulating the LLM through malicious prompts.  
* 8. Mitigation Effectiveness calculated as follows: \[\frac{\left(\text{ASR\_Baseline}-\text{ASR\_AVI}\right)}{\text{ASR\_Baseline}} \times 100\%\] (17) Baseline ASR estimated based on benchmark reports.
# Digital Shearography for NDT: Phase Measurement Technique and Recent Developments
## 4 Recent Developments of Shearographic NDT
### Directed Acoustic Shearography for Subsurface Crack  
With respect to the difficulty of using digital shearography to measure flaws away from the object surface, a directed acoustic shearography system using a ring-shaped phased array transducer was developed to measure subsurface depth flaws. In 2010, [PERSON] proposed the concept of directed acoustic shearography [53]. In 2017, [PERSON] et al. demonstrated that it was feasible to use acoustic waves as stress loading for shearography testing with a piezoelectric disc transducer [54; 55], thus demonstrating the  
Figure 12: **(a)** The modified shearography system for specular surfaces; **(b)** experimental result of the metal plate with a cone-shape deformation; **(c)** experimental result of the metal plate with an irregular deformation.  
feasibility of such a system. The directed acoustic shearography system consists of a phased array transducer, laser light source, and shearography sensor as shown in Figure 13. The phased array transducer provides pressure loading to the object, then the object is measured by a shearography measuring device.
## 2 UAV Communication  
As highlighted in the preceding paragraphs, this section and its sub-sections cover first category, the Performance Group (PG). Unmanned aerial vehicles (UAVs) have enormous potential in the universal domains including civil, defense, media and public domains [25]. They have unprecedented and useful applications where human lives can otherwise be in danger. In addition to this, multi UAV systems are jointly mission capable and can accomplish the same but better economy, precision and efficiency compared with solo UAV systems. However, there are many concerns that are required to be resolved before the effective use of unmanned aerial vehicles for the provision of reliable, assured and context-focused networks. In view of the distinctive characteristics of the UAV networks and the need to address related issues, considerable work is yet to be carried out in this area. Current progress in the domain of mobile adhoc networks (MANETs) and vehicular adhoc networks (VANETS) is not sufficient to address the peculiar nature of UAV networks. UAV networks may vary from slow dynamic to dynamic as some have intermittent links and topology that is relatively fluid. Meanwhile, it is considered that adhoc mesh networks may be one of the most appropriate domains for UAV networks. Moreover, it is a pertinent fact that the topology of multi UAV networks has been a less researched area.  
Figure 2: Structure and organization of this paper.  
Software defined networking (SDN) can be the answer to enable flexible deployment and management of future applications [26; 27]. Moreover, the stated technology may achieve benefits such as availability and security in networks and cost reduction [28; 29]. Moreover, fundamental characteristics such as flexibility and reach demands of UAV networks go beyond the scope of MANETS and VANETS. Furthermore, future UAV network application necessitates protocols that would cater needs of intermittent links, high mobility, power constraints, dynamic topology and varying link quality [30]. UAVs may be utilized for different profiles and missions depending upon their size. Smaller UAVs may be used in swarms while large UAVs may be used in solo missions. Stated roles of UAV networks have diverse as well as enormous potential in civilian applications. The authors of [31] state that UAVs are going to possibly be an invaluable addition in the operations of security organizations including fire brigades and police departments. However, future progress in the domain of sensors and electronics technology has enhanced the scope of UAV network applications [32]. This will likely include applications such as remote sensing, remote damage assessment, traffic monitoring and diverse traffic monitoring [33; 34].  
The primary challenge in all the distributed systems such as UAVs, drones or mobile IoTs is a robust authentication mechanism. We characterize the UAV communication in the following paragraphs for presenting a holistic understanding of authentication requirements in diverse domains of UAV systems.
# Interoperability between Building Information Modelling (BIM) and Building Energy Model (BEM)
## 2 Method
### BIM Exporting Formats
#### 2.1.2 Industry Foundation Classes (IFC)  
The industry foundation classes (IFC) were developed by international association interoperability (IAI), but are now administrated by the buildingSMART alliance. They are the only 3D object-oriented open standards that use BIM. In contrast to gbXML, the aim of the IFC format is to provide a single basis for the exchange of information in the field of construction and facility management [43]. The IFC schema is more extensive and complex than the gbXML format, and supports, in addition to a good geometric representation, semantic data enrichment [44].  
Figure 3: Analytical model of the residential and warehouse buildings.  
Figure 2: Building information modelling (BIM) models of the residential and warehouse buildings.  
Although there is an automated IFC export from Revit using the BIM collaboration plug-in developed by CYPE, the IFC files for both case studies were exported manually. In order to guarantee proper interoperability, in the Revit export interface, the phase where the thermal zones were created must be selected. Otherwise, the room data will not be transferred.