esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Discussion  
### Collagen Peptides and GPVI-Dependent Platelet Activation  
The data obtained indicated a clear separation between the effects of triple-helical collagen peptides that contained the established GPVI recognition motif, (GPO)\({}_{\text{n}}\)[15], and peptides that had a (GPP)\({}_{\text{n}}\) backbone instead. We found that the (GPO)\({}_{\text{n}}\)-containing collagen peptides (M1-3) (i) induced high platelet Ca\({}^{2+}\) rises under stasis, (ii) accomplished a fast build-up of thrombi with aggregated and activated platelets under flow, and (iii) evoked platelet responses both under flow and static conditions that were highly sensitive to the inhibition of Syk. Accordingly, these peptides provided strong proof-of-principle evidence for potent stimulation of the GPVI-Syk-PLCy2-Ca\({}^{2+}\) pathway of platelet activation.  
Immobilized, the (GPO)\({}_{\text{n}}\) peptide CRP-XL (M2), lacking an \(\alpha_{2}\beta_{1}\) interaction motif, produced smaller-sized thrombi (low parameter values in P2-6) than the other collagen peptides did, which was in agreement with the known synergy between GPVI, integrin \(\alpha_{2}\beta_{1}\), and GPIb-V-IX receptors in thrombus formation at a high shear rate [9; 10; 11]. Synergy of GPVI and \(\alpha_{2}\beta_{1}\) could also explain why peptides containing the integrin-binding motif G(F/A)OGER evoked a faster Ca\({}^{2+}\) signal when compared to CRP-XL. Seemingly in contrast with its lower binding affinity to platelets under stasis [17], we observed higher parameters of thrombus formation with GAOGER-GPO (M3) than with GFOGER-GPO (M1). The explanation for this higher activity remains unclear.  
In contrast, the (GPP)\({}_{\text{n}}\)-containing peptides GFOGER-GPP (M4) and VWF-BP (M5) did not evoke detectable Ca\({}^{2+}\) rises in platelets under stasis. Yet, when immobilized under flow, the integrin-binding peptide GFOGER-GPP evoked low-parameter thrombus formation in terms of platelet activation and aggregation, and this activity was again suppressed by Syk inhibition. This may have reflected weak interaction of the (GPP)\({}_{\text{n}}\) motif with GPVI, reinforced by strong integrin-binding activity.  
Jointly, these results pointed to a Syk-dependent role via GPVI in the support of thrombus formation. This conclusion was supported by a reanalysis of earlier experiments, where effects of the single-chain variable fragment antibody 10B12 were studied for the surfaces M1, M5, and M6 [16]. Markedly, image reanalysis providing the parameters P1,3-6 indicated a similar effect pattern of 10B12 as presently seen with Syk-IN (not shown).
# An Approach to Industrial Automation Based on Low-Cost Embedded Platforms and Open Software
## 3 System Implementation
### Process Level  
This level comprises the DCs, whose operation involves the control and fault detection systems. These systems are implemented in the PiXtend PLC (FC-201). The control systems implemented are based on the PID and FGS-PID control algorithms. The fault detection system uses an EKF. The proposed control system is of the decentralized type, as shown in Figure 3. Each tank has a reference of the desired level (\(ref_{h_{1}}\) and \(ref_{h_{2}}\)). The error signals are processed to obtain the control signals \(u_{1}\), \(u_{2}\), and \(u_{3}\), which command the valves. Finally, the EKF receives the tank levels and the control signals as input signals to estimate the tank levels (\(\tilde{h}_{1}\) and \(\tilde{h}_{2}\)).  
In this work, two control schemes are proposed and compared: classic PID and fuzzy + PID. The classic PID scheme uses three PID controllers, one for each valve of the multi-tank system. The fuzzy + PID scheme implements the fuzzy gain scheduling (FGS)-PID algor  
Figure 3: Proposed control system.  
Figure 2: Automation system architecture.  
the controllers for valves 1 and 3 correspond to classic PIDs. This configuration shows significantly better performance than other combinations of FGS-PID applied to valves 1 and 2.  
Control schemes, including the fault detection and diagnosis module (FDD) system based on the EKF, are designed from the process model, determined by  
\[\begin{split}\frac{dh_{1}}{dt}&=\frac{1}{A_{1}}\left(u _{1}q_{0}-u_{2}a_{2}\sqrt{2 gh_{1}}\right)\\ \frac{dh_{2}}{dt}&=\frac{1}{A_{2}}\left(u_{2}a_{2} \sqrt{2 gh_{1}}-u_{3}a_{3}\sqrt{2 gh_{2}}\right)\end{split} \tag{1}\]  
where  
\begin{tabular}{l l} \(h_{1},h_{2}\): & Liquid level at tanks 1, 2 \\ \(u_{1},u_{2},u_{3}\): & \% of opening of valves (\(0\leq u_{1,2,3}\leq 1\)) \\ \(A_{1},A_{2}\): & Cross-section area of tanks 1, 2 \\ \(a_{2},a_{3}\): & Cross-section area of valves 2, 3 \\ \(q_{0}\): & Nominal valve flow (\(q_{0}=4\) lt/min) \\ \(g\): & Gravity constant (\(g=9.81\) m/s\({}^{2}\)). \\ \end{tabular}  
Faults to be detected correspond to leaks in tanks 1 and 2. A leak is modeled as an outflow caused by a hole located at the bottom of the tank, as follows:  
\[\begin{split}\frac{dh_{1}}{dt}&=\frac{1}{A_{1}}\left(u _{1}q_{0}-u_{2}a_{2}\sqrt{2 gh_{1}}-l_{1}\right)\\ \frac{dh_{2}}{dt}&=\frac{1}{A_{2}}\left(u_{2}a_{2} \sqrt{2 gh_{1}}-u_{3}a_{3}\sqrt{2 gh_{2}}-l_{2}\right)\\ l_{1}&=k_{11}a_{11}\sqrt{2 gh_{1}}\\ l_{2}&=k_{12}a_{2}\sqrt{2 gh_{2}}\\ k_{11,2}&=\left\{\begin{array}{ll}0,&\text{no leak}\\ 1,&\text{leakage}\end{array}\right.\end{split} \tag{2}\]  
where  
\begin{tabular}{l l} \(l_{1},l_{2}\): & Flow due to leaks in the tanks 1, 2 \\ \(a_{11},a_{2}\): & Leak hole area in tanks 1, 2 \\ \(k_{11},k_{12}\): & Fault indicator for tank 1, 2. \\ \end{tabular}  
Table 1 presents the physical values considered for the calculation and deployment of the system.
## 5 Results
Further, we shift our focus to the BER performance comparison of the Gaussian-approximation-based MP algorithm and the hybrid MP with different values of \(P\). The outcomes of this analysis are illustrated in Figure 8, maintaining an \(\text{SNR}=18\) dB and fixing \(N_{T_{i}}=2\). It is evident from the results that the hybrid MP consistently outperforms the GA-based MP when the system involves more than four paths. For scenarios with precisely four paths, both the Gaussian-approximation-based MP and the hybrid MP exhibit similar performances, characterized by a BER of approximately \(5\times 10^{-4}\). However, when there are more paths, the performance of the Gaussian-approximation-based MP deteriorates, while the hybrid MP's performance remains robust. This observation underscores the superior effectiveness of the hybrid MP in mitigating multipath interference. The underlying reason for this disparity lies in the convergence behavior of the two algorithms. The Gaussian-approximation-based MP tends to converge towards a local  
Figure 8: The BER performance under different values of \(P\). Figure 7: The BER performance concerning various values of \(N_{T_{i}}\). optimum due to the presence of a substantial number of loops. In contrast, the hybrid MP algorithm efficiently leverages diversity gain, leading to its superior performance in such scenarios. In Figure 9, we present the BER performance of the hybrid MP algorithm across various SNRs with the same \(P=8\) and \(N_{T_{i}}=2\). We compare this performance with that of the MMSE detector, the GA-based MP, and the hybrid MP without any damping factor. The results indicate that the hybrid MP exhibits robust performance across the entire SNR range. Particularly noteworthy is the performance of the hybrid MP without the \"damping factor\", which closely matches that of the Gaussian-approximation-based MP. The inclusion of the \"damping factor\" significantly improves the performance, reducing the BER from \(5\times 10^{-4}\) to \(2\times 10^{-5}\). This enhancement is especially notable. It is worth mentioning that a previous study suggested that a \"damping factor\" might not be necessary for the hybrid algorithm. However, the presence of the fractional Doppler leads to interference energy being spread across more paths, necessitating the incorporation of a \"damping factor\" to enhance effectiveness.
# The Effect of Blue Noise on the Optimization Ability of Hopfield Neural Network
## 4 Results and Discussion  
### Blue Noise Hopfield Neural Networks in Optimization Problems  
Optimization problems are divided into two kinds of function optimization and combinatorial optimization. Many practical problems can be categorized into one of these for solving [37; 38]. To verify the immunity of Hopfield neural network to colored noise, blue noise was selected to perturb the input and output, respectively, and the effect of noise on Hopfield function optimization and combinatorial optimization problems was explored.  
The essence of solving optimization problems with Hopfield is in the process of solving the extremum of the energy function E of that neural network, so the constraints of the problem need to be expressed in the form of a function of that energy.  
The algorithm flow is as follows:  
1. The problem to be solved is described in computer language, where the output of the neural network corresponds to the solution of the problem.
2. Construct the energy function of the neural network so that its minimum value corresponds to the optimal solution of the problem.
3. The initial states of the neurons are generated, and the weights W and bias inputs I between the neurons are corrected via the output values of each iteration after noise perturbation.  
In the case that the energy function has converged, and the output accuracy is satisfied, the steady state of its operation is the optimal solution under certain conditions.
## 3 System Overview
### Software  
The ESP32-DevKitC module was programmed using ESP32-IDF [50]. For each combination of cipher suite, signing algorithm and frequency, a different version of the firmware was uploaded to the ESP32 by using the official flashing tool provided by IDF. Specifically, an Hypertext Transport Protocol Secure (HTTPS) server was implemented for the ESP32 using mbedTLS [51]. The server provides remote users a 512-byte JSON file randomly generated with the Python library Faker v0.73 [52]. The test procedure was started by the Debian 9 virtual machine, which executed a Python script that defined the cipher suite to be used, started the energy measurements using the Application Programming Interface (API) that runs on the Orange Pi PC, and then requested the 512-byte JSON file from the ESP32 a number of times. The time taken by each of the HTTPS transactions was measured and  
Figure 3: Implemented energy measurement testbed.: (**A**) ESP32-DevKitC; (**B**) INA219; and (**C**) Orange Pi PC.  
registered. When all transactions were finished, it requested the accumulated energy consumption value from the Orange PI PC and stored all the data into a JSON file. When all the tests were finished, an HTML page used Javascript to parse the collected JSON files and generate charts and tables that show the relevant test data to ease their analysis.  
To obtain the current values from the INA219, a Python script was developed. This script first sampled the INA219 sensor by accessing the I2C bus, then collected the obtained values and finally reported the final energy consumption values. Several tests and optimizations to the Python code in charge of sampling the I2C bus were performed, which led to achieving a final sampling rate of 1000 Hz. The script was launched from a Python Hypertext Transport Protocol (HTTP) server that runs on the same Orange Pi PC. The HTTP server provided a REST API, which enables the current measurement procedure to be managed remotely by the testbed coordinator implemented on the Debian 9 virtual machine.
## 1 Introduction
### Paper Motivation  
Although such technological innovations have shown promising results, network operators still search for a better integration of 5G to unveil its full potential and consolidate 5G driven applications across multiple vertical industries. This is due to the fact that new application areas such as real-time wireless video streaming services demand multiple requirements from the use of 5G networks at the same time. As an example, it requires networking resources to broadcast video, computational resources to process it, and storage resources to locally cache it [10]. Similarly, some applications, such as ultra-high definition video and augmented reality need high-speed, high-capacity communications, while others such as the mission-critical IoT and autonomous vehicles require ultra low latency, ultra-reliable services. In this respect, a new term called edge slicing has arisen. By combining two promising approaches, SDN and edge computing, network operators aim to efficiently and simultaneously meet the diverse and multiple requirements of several vertical applications, offering flexible data transmissions and computing capabilities with minimal latency.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Function** & **ML Technique** & **Objective** \\ \hline \multirow{3}{*}{Network planing, management and monitoring} & • & K-means clustering; & • & Clustering users and service requirements; \\  & • & Deep neural network; & • & Routing and forwarding decisions; \\  & • & Reinforcement Learning; & • & Resource optimization; \\  & • & SVM. & • & Parameter configuration; \\  & & & • & Forecasting resource usage. \\ \hline \multirow{3}{*}{Fault detection and security} & • & Principal component analysis; & • & Classification of users and applications; \\  & • & Logistic regression; & • & Anomaly detection; \\  & • & Deep neural network. & • & Predicting unusual behaviour. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Network functions and relevant ML techniques [19].  
In this paper, our aim is to provide an extensive and comprehensive literature review of the recent research on joint edge computing and network slicing applications and integration of ML into them. Although there are previous works reviewing network slicing and edge computing mechanisms in 5G and beyond networks, our paper focuses on their combination and provides recent works on edge enabled network slicing mechanisms together with currently used ML methods. In this respect, to the best of our knowledge, this is the first review paper on edge enabled network slicing mechanisms and ML applications.  
The remainder of this paper is outlined as follows. Section 2 presents a literature survey on the applications of network slicing to edge of the networks. Section 3 presents a literature survey about network slicing and edge computing with the contribution of ML techniques. Then, Section 4 describes our solution and model based on network slicing, edge computing, and RL techniques. Lastly, Section 5 concludes the paper.
## 4 ESI Challenges and Limitations
In (**a**), a small number of electrode channels and source points leads to a more sparse and lower resolution outcome, while in (**b**), Medium-Density EEG and more dipoles refine the resulting solution and in (**c**), High-Density EEG and ultra-high resolution of the source model produces a solution with high spatial resolution and accuracy. Equally important is the co-registration of EEG sensors, i.e., the location of the electrodes, related to the head model and how coherent the electrode alignment is with the actual position of the electrodes during the EEG recordings. In this regard, minor misplacements during computational modeling could lead in inaccurate solutions [95]. New techniques and pipelines include 3D scanners in order to perform digitization of electrodes correctly and eliminate registration errors from the analysis [96,97]. Nevertheless, such systems are often very expensive, whereas the electrode alignment is done interactively [89]. It must also be taken into account that correct calculations of ESI relies on conductivity values that are provided to the head model [95]. On this premise, anatomical MRI could contribute to the correct solution of the forward (and as a result the inverse) problem, since the information concerning the thickness and conductivities of each head tissue is needed for the source localization computation [40]. For this reason, it is optimal to use subject-specific MRI in order to reconstruct the individual head model, additionally incorporating the anisotropic properties of the skull and white matter [36,98]. However, in several cases, individual MRI is unavailable or the head model cannot be created, due to brain abnormalities and impairments, such as brain malformation or tumors. In such conditions, pre-existing template solutions, such as standardized head models, could be used, albeit results could be controversial or designate non-existent areas [99]. Concerning the current state-of-the-arts methods presented in this paper, several limitations are still present. As far as the pattern recognition approaches are concerned, the main obstacle for effective localization is the lack of labeled real data to be used in order to train the Deep Learning architectures. Annotating real EEG data with the true source locations, for instance via simultaneous EEG-fMRI recordings, could be an encumbering task, but crucial in the progress of Deep Learning approaches in ESI. On the contrary, Bayesian Models provide enhanced localization accuracy, though these methods are extremely time-consuming in solving the inverse problem, which is an unbearable obstacle for real-time applications. Recent advances focus towards this direction, increasing the efficiency of the algorithms employed. Future multi-modal approaches could combine the advantages of both data-driven and model-driven methods, exploiting the localization precision of statistical methods applied in Bayesian models with the computational speed of the Deep Learning processes.
# Degradation Assessment of In-Service Transformer Oil Based on Electrical and Chemical Properties
## 4 Discussion
### Relationship Between Different Characteristics
#### 4.1.2 Impact of Moisture Contents  
Figure 6 represents the relationships between the moisture content and DDF. Since the precipitation of moisture is considered to deteriorate the dielectric characteristics, moisture content can be used as an index to evaluate electrical characteristics. In Figure 6, however, no clear correlation was found between moisture content and DDF for T1 and T2. However, the correlation results for T3 showed that with the increase in the moisture content, the dielectric dissipation factor also increased.  
Figure 5: The relationship between breakdown voltage and dielectric dissipation factor at 90 \({}^{\circ}\)C.  
Figure 6: Correlations between moisture content and dielectric dissipation factor at 90 \({}^{\circ}\)C for each transformer.  
The impact of moisture content on the breakdown voltage of the three transformers was also analyzed, Figure 7. As with the DDF, no clear correlation was found between the moisture content and breakdown voltage for all transformers. These results are believed to be due to the fact that the moisture content values were well below the threshold values and, hence, the moisture content had little influence on the electrical characteristics.
## 3 Simulation Model for Surveillance Systems
### Target Object and Target Detection  
In this section, we first describe the modeling of the target object and then the way to determine if the target object is located in the surveillance area in the horizontal domain. First, we define the target object as the three points \(p_{t_{0}}\), \(p_{t_{1}}\), and \(p_{t_{2}}\) for the horizontal aspects. The point \(p_{t_{0}}\) is the center of the target, and \(p_{t_{1}}\) and \(p_{t_{2}}\) are the points that either the shortest and the longest point, respectively, depending on the distance from the optic lens. Since, in most cases, the plane of the target, represented by the large rectangle in Figure 2b, is not parallel to the optic lens and the image plane, we consider the angle of the rotation of the target plane \(\theta_{t}\) to determine if the target belongs to the surveillance area. In other words, the target is located in the surveillance area only if all the points represented in (9) are located within the surveillance area.  
\[p_{t_{i}}(x,y)=\begin{cases}(x_{t}-x_{o},y_{t}-y_{o})&i=0\\ (-\frac{L_{lm}}{2}sin\theta_{t},\frac{L_{lm}}{2}cos\theta_{t})+p_{t_{0}}(x,y) &i=1\\ (\frac{L_{lm}}{2}sin\theta_{t},-\frac{L_{lm}}{2}cos\theta_{t})+p_{t_{0}}(x,y) &i=2\end{cases} \tag{9}\]  
To determine whether a target object lies within a designated surveillance area, it is imperative to preliminarily establish whether the object is located within the efficient surveillance distance \(D_{eff}\), as described in Section 3.2. In the event that the object is indeed situated within \(D_{eff}\), the procedure is executed as described as follows.  
The key factor for target detection is the AOV (angle of view), a well-known formula like the FOV, and is defined as \(H_{AOV}=2\arctan(L_{sw}/2L_{f})\). If positions \(p_{t_{1}}\), \(p_{t_{2}}\), and \(p_{t_{3}}\) are in the surveillance area, the angles of \(p_{t_{1}}\), \(p_{t_{2}}\), and \(p_{t_{3}}\) must be within the angular extent of \(H_{AOV}\), which can determined by (10).  
\[\frac{p_{H}\bullet p_{T}}{\|P_{H}\|\times\|P_{T}\|}\leq cos(\frac{H_{AOV}}{2}) \tag{10}\]  
where \(P_{H}\) is any coordinate in \((H_{x_{0}},H_{y_{0}})\) except (0, 0) and \(P_{T}\) is one of the coordinates of \(p_{t_{i}}\). That is, the target is in the surveillance area if the angle between the centerline of the surveillance area and the line from the origin and the target point is smaller than the AOV. Similar to the procedure to calculate the horizontal surveillance area, the discriminating procedure for target detection is repeated in terms of the vertical surveillance area.  
Figure 4: Maximum surveillance distance \(D_{max}\), blinded surveillance distance \(D_{bz}\), and effective surveillance distance \(D_{eff}\) in the vertical domain.
## 3 Results and Discussion  
### Sample Sustainability  
The sustainability analysis of all samples was evaluated in terms of the Embodied Energy (EE) and Carbon Footprint (CF) [34; 35; 36; 37]. The bottom ash pre-treatment and the thermal treatment on a heating plate at a low temperature (70/80 \({}^{\circ}\)C), to which some samples were subjected, was considered in the evaluations. In Figure 2, the EE (MJ/kg) versus CF (kg/kg) of six samples is reported. All samples appear to be more sustainable compared to materials commonly used in the filter production such as polypropylene (PP), polyethylene (PE), polyamide (PA) and polystyrene (PS). The samples' sustainability can be compared with building materials such as brick, Portland cement and concrete due to the idea to propose SUNSPACE use in construction. The graph shows that sample 1 has lower EE, while samples 5 and 6 are more sustainable in terms of CO2 emissions compared to other samples. The higher EE and CF resulting for samples 2, 3 and 4 can be attributed to the use of BA instead of SF in their synthesis. In particular, no pre-treatment is necessary for silica tune before its reuse. Instead, BA was subjected to a thermal treatment, to reduce its moisture, to control the amount of water that was added (see Table 1) and to guarantee the sample's workability during the synthesis. However, in the future, it may also be possible to find more sustainable BA pre-treatment to increase the sustainability of its use.