esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Challenges and Future Directions  
Even though NMR spectroscopy has its advantages, there are still some challenges when applied to polysaccharide analysis. Table 4 provides a summary of the challengesand future directions for polysaccharide research based on NMR. It focuses on the current limitations, emerging solutions, and possible future directions in the field. These tables are meant to provide a concise overview of the key points discussed in the review and make the commentaries easier to read and more useful. One significant limitation is the overlapping of signals in complex polysaccharides ride mixtures, which can hide structural details, especially in heterogeneous samples. For example, cellulose, hemicellulose, and pectin have overlapping signals. Therefore, analyzing plant cell wall polysaccharides usually requires a large amount of spectral deconvolution [2; 52]. To address this issue, advancements in multidimensional NMR techniques, such as ultra-high-field NMR and selective isotope enrichment, are being explored [9]. For example, people have started to use computational tools like machine learning to automate spectral analysis. This has begun to make manual interpretation less complex [6]. [PERSON] et al. used nuclear statistical magnetic resonance to study the component diversity of pectin polysaccharides, providing a new framework for the analysis of complex mixtures [74]. Another challenge is the limited sensitivity of NMR when dealing with dilute samples or small amounts of material, as commonly encountered in biomedical applications. However, DNP-enhanced NMR has proven effective in boosting sensitivity, making it possible to investigate low-abundance polysaccharides in biological systems [5; 8]. Similarly, scientists have used the labelling of \({}^{13}\)C or \({}^{15}\)N isotopes to improve the signal resolution and sensitivity in polysaccharide studies, especially in microbial and marine polysaccharides [11; 12; 75]. In addition, the improved ssNMR methods and other DNP techniques allow researchers to study polysaccharide interactions at the sub-molecular level in complex environments like biofilms [5; 10]. [PERSON] et al. used DNP-enhanced ssNMR to characterize natural bacterial biofilms. DNP can increase the sensitivity by about 75-fold, enabling us to quickly acquire high-resolution 1D and 2D spectra. With these spectra, we can conduct detailed structural analysis of biofilm components. These components include polysaccharides and proteins. We can perform this analysis without having to use isotope labelling or chemical modification. This technology can overcome the sensitivity limits of traditional ssNMR. It also enables us to detect the extracellular matrix (ECM) components in biofilms efficiently. In this way, it reveals new understandings in biofilm tissue [76]. Integrating complementary technologies is important for getting past the current limitations. For example, mass spectrometry can provide information about molecular weight and sequences. On the other hand, X-ray scattering can show us the structural organization. In the structural studies of bacterial capsule polysaccharides, it has been
## 4 Ransomware Detection Studies w.r.t Compromised Platforms
### Networks Based Ransomware  
Due to evolution of more sophisticated ransomwares, packet inspection method is not sufficient to rely on. To detect the ransomware, a network monitoring system should be deployed. A network detection system monitors the network to detect the malicious activity [93].  
A network based ransomware detection system was proposed in [75]. Honeypots were created on each workstation to observe the system behavior. The workstations were observed to find the malicious behavior. Once a malicious behavior was observed the network administrator disabled the user account and shut down the system to isolate it with the system. This detection system did not prevent the launch of the attack and lead to lose of data if an attack launched.  
In [93], a ransomware detection model was developed. This model performed detection of high survivable ransomware in two stages. Network connection was monitored to find patterns. The user was informed when malicious behavior was observed. The user was able to break the connection by checking the connection address. Connection breaking invoked the unsuccessful key exchange.  
A ransomware detection system for the network environment was proposed in [76]. This detection system used honeypots to find out the ransomware attack. The URLs from infected workstation were inspected to detect ransomware. This system worked in on-off fashion. Secondly, the system halted its working when honeypots were checked.  
In the study of [66], a deep neural network based ransomware detection model was introduced. It was a network-based detection system that observed the suspicious process by observing the network calls from malicious application. Model was trained by using the generated HTTP requests from malicious application. Both machine and deep learning were used to detect and prevent different variants of ransomware attacks.  
In [82] a ransomware detection system was developed for the client server architecture. This system detected the ransomware by comparing the behavior with the normal profile. Normal profile was developed by using the normal activity data from different workstations. The detection was performed by observing the malicious behavior at user and kernel level. Once the malicious behavior was observed the server was notified to isolate the system
## 6 Computational Complexity  
The computational cost required for the operation of the ERIP algorithm consists of two components: the first includes the calculation of the normalized correlation coefficients of the real and imaginary parts of the residual interference of PAM symbols, and the second includes online operations to compensate for the real part of the interference. The complexity of the online computations of the ERIP algorithm is \(M\big{(}N_{T}^{2}n_{t}+N_{R}N_{T}\big{)}\) real multiplications, where \(n_{t}\) is the number of time shifts for which the correlation coefficient is calculated, which, for the 2 \(\times\) 2 MIMO scheme, gives 12 real additions and multiplications per symbol at \(n_{t}=2\). The complexity of calculating the correlation coefficients of the real and imaginary parts of the interference when using the pruned FFT algorithm and the symmetry property of the prototype filter uncertainty function to calculate the value of Equation (17) can be defined as:  
\[RM_{ERIP}=N_{T}N_{R}(M\log_{2}L_{h}-4L_{h})\frac{\#(p,q)-1}{4}\#(p,q), \tag{21}\]  
where \(\#(p,q)\) denotes the number of elements of the set \((p,q)\), which, based on the prototype filter ambiguity function [18] shown in Figure 1, can be limited to \((|p|<1,|q|<3)\), and the cardinality of the set \((p,q)\) will be equal to 21.  
For comparison, the computational complexity of the parallel multistage equalizer, the implementation of which for the MIMO channel was presented in [23], is:  
\[RM_{PM}=MK_{R}(N_{R}(\log_{2}M-1)+(\kappa+2)N_{R}+3N_{S}N_{R}(L_{h}-1)), \tag{22}\]  
Figure 5: FBMC-OQAM MIMO ERIP equalizer block scheme.  
where \(\kappa\) is the filter prototype overlapping factor, and \(K_{R}\) is the number of parallel stages. Thus, for the number of subcarriers equal to 128 to 1024, \(N_{T}=N_{R}=2\), \(L_{h}=37\), \(\kappa=4\) and \(K_{R}=2\), the number of operations performed depends on the number of processed symbols for the presented equalization algorithms; it can be observed that with an increase in the number of processed ERIP symbols, the equalizer shows itself to be more preferable (Figure 6) starting from 4-5 symbols, depending on the number of subcarriers. At the same time, when using the calculated correlation coefficients for more than 10 FBMC-OQAM symbols, the ERIP gain becomes approximately twofold. In this case, such a number of processed symbols corresponds to the required channel coherence time, ranging from 3.2 ms to 25.6 ms, with the number of subcarriers ranging from 128 to 1024 and a signal bandwidth of 400 kHz, which, for example, for an HF channel, does not exceed the coherence time [24].
# Functional Evaluation of a Novel Multi-Axial Alveolar Distractor--Preliminary In Vivo Animal Study
## 1 Introduction  
Alveolar distraction osteogenesis (ADO) is a new bone formation biological process that occurs between bone segment surfaces and involves the gradual, controlled displacement of surgically created bone fragments resulting in concurrent soft tissue and bone volume expansion [1, 2]. ADO has become one of the superior predictable treatment options for increasing bone height before implant placement [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12].  
In spite of the literature discussing several different types of distraction devices, the uniaxial distractor can only pull the bone linearly along the transport screw and bidirectional distractor axis with the buccal-lingual adjustable angulation intra-operatively not able to affect anatomical limitations such as the sinus/cavity, thin cortex bone region and alveolar nerve [13]. Distraction can provide free-angle bone growth (tongue/cheek, near/tele centric, occlusal direction, etc.) with precise evaluation of the planned movementand maintain the desired vector direction during the alveolar distraction process. These are the most important issues for ADO application [14; 15].  
Accurate control of the possibility for multidimensional distraction in different directions is considered desirable for a novel alveolar distractor design [15; 16; 17; 18]. A distractor designed with a ball and socket joint and the capability to control 3-dimensional movements during transport osteogenesis has been proposed in previous studies to achieve multi-axial distraction with various angle adjustments for alveolar ridge curve limitations, soft tissue pulls, inappropriate device positioning, or poor device trajectory [18; 19; 20]. Serious mechanical functional tests, including four-point bending resistance testing of the base bone plate, pull out testing of the multi-axial alveolar distractor, welding strength testing between the base bone plate and ball and socket joint mechanism and torque strength testing of the ball and socket joint, were approved to ensure the effectiveness and safety of our new multi-axial alveolar distractor [18].  
This study used the previously developed multi-axial alveolar distractor that has been alternative safe and effective by mechanical tests to evaluate the feasibility of the distraction function in rabbit mandbles.
# Physics-Informed Graph Neural Networks for Attack Path Prediction
###### Abstract  
The automated identification and evaluation of potential attack paths within infrastructures is a critical aspect of cybersecurity risk assessment. However, existing methods become impractical when applied to complex infrastructures. While machine learning (ML) has proven effective in predicting the exploitation of individual vulnerabilities, its potential for full-path prediction remains largely untapped. This challenge stems from two key obstacles: the lack of adequate datasets for training the models and the dimensionality of the learning problem. To address the first issue, we provide a dataset of 1033 detailed environment graphs and associated attack paths, with the objective of supporting the community in advancing ML-based attack path prediction. To tackle the second, we introduce a novel Physics-Informed Graph Neural Network (PIGNN) architecture for attack path prediction. Our experiments demonstrate its effectiveness, achieving an F1 score of 0.9308 for full-path prediction. We also introduce a self-supervised learning architecture for initial access and impact prediction, achieving F1 scores of 0.9780 and 0.8214, respectively. Our results indicate that the PIGNN effectively captures adversarial patterns in high-dimensional spaces, demonstrating promising generalization potential towards fully automated assessments.  
Keywords:attack path prediction; deep learning; physics-informed neural networks; graph neural networks +
Footnote †: journal:
###### Abstract  
The aim of this research was to prepare pH sensor films based on litmus using the sol-gel method with tetraethoxysilane (TEOS) and phenyltrimethoxysilane (PTMS) as precursors. The pH sensor film was then applied to millireactors to evaluate its performance on the intricate geometries of static mixers commonly found in millireactor designs. Millireactors were made from Formlabs High Temp resin using stereolithography (SLA) and from Anycubic Basic resin using digital light processing (DLP) technology. The performance of the pH sensor films was evaluated by tracking color changes in the pH sensor films and analyzing RGB (red, green, blue) and hue values through a smartphone application. The experiment involved mixing solutions with different pH values at varying flow rates within the millimeter channels. Furthermore, along with analyzing the hue values, characterization techniques involved measuring contact angles with water and diiodomethane. A film combining a litmus indicator with titanium dioxide (TiO\({}_{2}\)) displayed a color change within one minute and maintained this color throughout the study, confirming its reusability. Sensor films exhibited excellent reversibility (RSD = 2.4-3.3%) and stability. The findings demonstrate that the pH-sensitive films perform robustly across varying geometries, paving the way for their integration into advanced millireactor systems with static mixers and continuous chemical monitoring within Industry 4.0.  
Keywords:additive manufacturing; static mixers; millireactors; sol-gel method; litmus; pH sensor film +
Footnote †: journal: Applied sciences
## 3 Results and Discussion
### Impact of H\({}_{2}\)O/Si  
The H\({}_{2}\)O/Si ratio influences the ultimate characteristics of the membrane layer through the crystallization process and chemical reactivity (e.g., via changing the crystallization medium and concentration of reactants, respectively) [47]. In this respect, five membranes with H\({}_{2}\)O/Si ratios of 20, 40, 60, 80, and 100 were synthesized under similar conditions. N\({}_{2}\) permeance measurement revealed high non-zeolitic pathways in the membrane with an H\({}_{2}\)O/Si ratio of 100. According to Figure 2f by increasing the H\({}_{2}\)O/Si ratio, the ideal CO\({}_{2}\)/CH\({}_{4}\), CO\({}_{2}\)/N\({}_{2}\), and H\({}_{2}\)/SF\({}_{6}\) permeselectivity increases from 0.6, 0.75, and 8 (for H\({}_{2}\)O/Si = 20) to 94, 10, and 115 (for H\({}_{2}\)O/Si = 80), respectively. In addition, the SF\({}_{6}\) permeance decreases from 1.98 \(\times\) 10\({}^{-7}\) to 8.68 \(\times\) 10\({}^{-9}\) [mol/(m\({}^{2}\)sPa)]. The noteworthy point is that for membranes with H\({}_{2}\)O/Si ratios of 40, 60, and 80 the CO\({}_{2}\) permeance is higher than other gas molecules, but for a H\({}_{2}\)O/Si ratio of 20, the CO\({}_{2}\) permeance is lower than that of He, H\({}_{2}\), N\({}_{2}\), and even CH\({}_{4}\). This phenomenon can be ascribed to the presence of large size defects and cracks. The CO\({}_{2}\)/CH\({}_{4}\) permselectivity of \(\sim\)0.6 confirms that based on [PERSON]'s law \(\alpha_{\frac{CO_{2}}{CH_{4}}}=\sqrt{\frac{M_{CH_{4}}}{M_{CO_{2}}}}\)[7], the membrane with H\({}_{2}\)O/Si ratio of 20 presents a Knudsen diffusion mechanism, which is possibly due to the high concentration of the gel precursor leading to the creation of multiple nucleation sites, and as a result the formation of a thick layer and the emergence of structural defects during calcination.
## 2 Materials and Methods
### Transmission Electron Microscopy and Particle Size Analysis  
TEM was used to investigate the microstructure of ACM and soya milk emulsions stabilized by ACM using modifications of a method described by [58]. The microstructure of ACM and soya milk emulsions stabilized by ACM was investigated using an FEI/Tecnai T20 TEM with a high-resolution camera and electron energy loss spectroscopy (EELS) system using modifications of methods described by [58; 59]. An accelerating voltage of 200 kV was used in investigating the ACM dispersion and each emulsion sample. One droplet of each sample dispersion was transferred over the surface of the carbon-coated copper grid, negatively spotted with a 2% (_w/w_) solution of uranyl acetate and then dried at room temperature [58; 59]. Digital image analysis was used to determine the particle size. The TEM images were acquired using the FEI/Tecnai T20 TEM and a charge-coupled device (CCD)camera size of 2048 \(\times\) 2048 (Gatan UK Milton Park Innovation Centre Abingdon OX14 4 RY United Kingdom Gatan, USA) [58; 59]. The images at 50 and 100 nm were taken to quantify the particle size of each soya milk globule and its distribution in each emulsion sample using the method described by [60]. The particle size distribution of ACM-stabilized emulsions was elucidated by calculating Sauter mean diameter (D\({}_{(3,2)}\)) using Equation (1) while the standard deviation i.e., value of the width of the milk goblets distribution in the ACM-stabilized emulsion was calculated using Equation (2). The average droplet size is given by Equation (3) where \(n_{i}\) is the number of goblets in each size class, \(d_{i}\) is the particle diameter, \(\sum ni\) is the total number of droplets, and \(d_{n}\) is average droplet size [61].  
\[\text{D}_{(3,2)}=\frac{\sum n_{i}d_{i}^{3}}{\sum n_{i}d_{i}^{2}} \tag{1}\]  
\[\sigma=\frac{\sum n_{i}(d_{i}-d_{n})^{2}}{\sum n_{i}} \tag{2}\]  
\[\text{d}_{n}=\frac{\sum n_{i}d_{i}}{\sum n_{i}} \tag{3}\]
# Formation Control Algorithm of Multi-UAV-Based Network Infrastructure
###### Abstract  
This paper addresses the analysis and the deployment of the network infrastructure based on multiple Unmanned Air Vehicles (UAVs). Despite the unprecedented potential to the mobility of the network infrastructure, there has been no effort to establish a mathematical model of the infrastructure and formation control strategies. We model the generic dynamics of the network infrastructure and derive the network throughput of the infrastructure. Through the parametrization of the model, we extract the generic factors of the network protocols and verify our model through the Network Simulator 3 (ns-3). By exploiting our network analysis model, we propose a novel formation control algorithm that determines the location of the UAVs to maximize the efficiency of the network. To achieve the objectives of the infrastructure, we define the formation-shaping effect as forces and elaborately design them using the generic factors. The formation algorithm continuously approaches to the optimized formation of a fleet of UAVs to enhance the overall throughput of the terrestrial devices. Our evaluations show that the algorithm guarantees remarkably higher throughput than the static formations. Through the dynamic transformation of the UAV formation, we believe that the multi-UAV-based network infrastructure could expand the boundary of the existing infrastructure while reducing the network traffic.  
multi-UAV application; wireless access network; ad hoc network; UAV formation control +
Footnote †: journal: Applied Sciences
# Improving Passband Characteristics in Chebyshev Sharpened Comb Decimation Filters
## 3 Passband Compensation of Chebyshev Sharpened Combs from [8; 10; 11]  
We present the compensation of the Coleman Chebyshev comb [8] and Chebyshev combs in [10; 11].
# XBeats: A Real-Time Electrocardiogram Monitoring and Analysis System
## 2 Background and Related Work
### Standard 12-Lead ECG  
Standard 12-Lead ECG provides a complete and comprehensive analysis of the heart and enables a better thorough diagnosis that cannot be obtained otherwise. The authors in [22] presented a 12-lead module to capture ECG vital signals and transmit them to a backend server for analysis via a mobile phone connected to the ECG device through Bluetooth. However, the Bluetooth link used the classical Bluetooth protocols to communicate with a mobile device. It didn't optimize the BLE protocol stack for sending the data, which increases the packet size and overhead of the transferred data rather than using BLE standard services and characteristics. Besides, the device depends on the results coming from the backend regarding the heart condition of the patient because it only works if the device is connected to a mobile phone with internet connectivity. Moreover, the Bluetooth module used is not optimized for low power consumption, reducing the device's operation time and compromising the patient experience. Accordingly, their system mainly focuses on the data acquisition part of the ECG test. In contrast, the authors in [23] use a similar monitoring architecture but add active electrodes attached to the patient's body without adhesive materials. However, the device only supports offline ECG monitoring. The data is stored on a flash drive and then transferred to a PC for further processing and analysis of the collected ECG data.  
While in [24], the authors developed a prototype for EEG/ECG data acquisition, the hardware utilizes the TI ADS1298/9 Analog Frontend chip for digitizing analog EEG/ECG signals. The authors incorporated multiple components to make it suitable for remote EEG/ECG monitoring like Bluetooth, WIFI, and local storage (i.e., SD) modules. However, the platform for operating these modules to perform ECG/EEG monitoring is notmentioned. Besides, the authors were interested in validating the integrability and operability of the hardware modules in terms of the available bandwidth between the different components to perform a successful EEG/ECG test.  
Lastly, the authors in [7, 8] conclude that with the massive diversity of work proposed in ECG monitoring systems, these systems still lack completeness and comprehensives. Despite the significant overlapping in the literature about the number of recorded ECG leads, accuracy, usability, and mobility, these systems cannot provide real-time data streaming and heart condition diagnosis.
# Several Cases for the Validation of Turbulence Models Implementation
## 3 Numerical Method
### Spatial Discretization  
The UNS3D flow solver uses a finite volume method for its spatial discretization. The UNS3D and the NASA's FUN3D codes use a dual-mesh cell-vertex approach, while the NASA's CFL3D code uses a cell-centered approach. The cell-vertex approach has better adaptability with mixed element grids. In addition, as for large grids the number of nodes is smaller than the number of elements, the cell-vertex method is computationally more efficient than the cell-centered method.
# Bayesian Compressive Sensing Based Optimized Node Selection Scheme in Underwater Sensor Networks
## 1 Introduction
### Paper Organization  
The rest of the paper is organized as follows. The compressed sensing theory, Bayesian method, and the underwater channel model are introduced in Section 2. We propose the optimization problem about the MSE and the energy limit in Section 3. Section 4 provides the simulation results to show the superior performance of the proposed scheme. A conclusion of the paper is given in Section 5.
# Future Industrial Applications: Exploring LPWAN-Driven IoT Protocols
## 5 Industrial Application Perspective
### Terminals for Retail Sales  
Since they deal with regular contact, sale point systems demand guaranteed quality of service [29; 106]. There is no limit on battery life because these devices have a constant electrical power source. Low latency is also crucial; otherwise, it limits the transactions a store can process within a given time [29]. As a result, NB-IoT and LTE-M are more appropriate for this application. Cost is also a consideration for retail point-of-sale terminals, making NB-IoT preferable over LTE-M.