esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Agent Dynamic Fog Service Placement Approach
## 3 Service Placement Orchestrator Implementation
### Resource Monitoring  
Resource monitoring is implemented using a monitoring agent. It uses a cyclic behavior to wait for messages. The content of these messages is filtered with the method startsWith() in order to take a relevant action. It can obtain messages from a battery voltage agent (BattVoltAgent), light agent (LightingAgent), or a sensor agent (AbstractSensorAgent). A battery voltage agent keeps track of Raspberry Pi 4 battery charge level as a fog node. Pi 4 does not have a native analogue-to-digital (ADC) conversion option. An external one such as the MCP3424 18-Bit ADC-4 channel converter would be needed. However, a high or low pin 3 voltage is checked for simulation purposes using the Pi4j library.  
A sensor agent is used to monitor such external resources as light intensiveness or temperature etc. A 5 s interval is used as a sensor update interval, which involves communication between end-devices and particular agents and as a sensor agent poll interval, which involves communication between agents and the orchestrator. There are a few commands that a sensor agent can receive like Get, Set, Move, or Changeip. If a sensor agent receives a Get message, it identifies a sender and responds with a value that is obtained using the getValue() method as an ACL INFORM message. These messages are sent as a part of a regular sensor polling task at a predefined interval like 5 s. It can be also triggered once a threshold value is reached.  
Communication with the end-devices to obtain their values is completed using the COAP protocol. Lighting and temperature agents keep on polling their end-devices for their values as a part of an onTick() event, which is periodically triggered after a timeout interval. The COAP request method GET is used to obtain a value from the end-point device. The PUT method is used to set a value instead.  
As in Figure 2 shown above, an end-device keeps on periodically communicating with its fog node. Once a monitoring agent detects that its battery voltage is below a required level, it sends a service redistribution request to the fog node Decision Maker agent. A remote service redeployment is calculated using IMOPSO and AHP, and its solution is communicated to the Execution agent. Services in the current fog node are stopped. A redeployment solution is synchronized by the current fog node and the fog node where the services have to be moved to. A Synchronization agent sends a request to its Execution agent and the end-device service is assigned to the fog node 1.
## 4 SRPC Control Algorithm
### SM Voltages Balancing Control  
Figure 7a shows an averaging voltage balancing control for the SM capacitors. This control ensures that the voltage of each SM in the leg is close to the average voltage that is provided as a reference. It is implemented by summing the measured SM voltages for each MMC leg and dividing the result by the number of SMs per leg. The actual average voltage value, in this case, is calculated and compared to a reference average voltage, \(V_{SM}^{*}\). Then, a PI controller is used to correct the difference between the actual and the reference values [6; 32]. The output of this controller is considered as a reference for a circulating current controller, which it is implemented by summing the upper and the lower arm currents for each converter as in (21). This controller allows a low circulating current when it is necessary to balance the SM voltages.  
The individual voltage balancing control presented in Figure 7b forces the capacitor voltage of each SM to follow its reference, which is performed by a proportional controller for each SM to act dynamically in the balancing process in every switching period [6]. The output of the individual voltage balancing controller is multiplied by +1 if the arm's current direction is to charge the capacitors, or by -1 if its direction is to discharge the capacitors (the positive directions of MMC arm currents are presented in Figure 1).  
Figure 6: Voltage control and calculation of the voltage reference signals: (**a**) When using the deadbeat predictive control; (**b**) When using the conventional PI control.  
Figure 7: Voltages balancing control: (**a**) Averaging voltage balancing control; (**b**) Individual voltage balancing control.  
Figure 8 shows the voltage command generation for the two-level SM applied to a phase-shifted PWM. The final averaging voltage control signals of \(A_{\sigma}\), the final individual voltage control signals of \(V_{io}\)(,\(u\)), were added to the obtained final reference waveforms of \(u_{\sigma}\)'(,\(u\)). By taking into consideration twenty-four SMs in each MMC leg, so the total SMs number will be divided equally between the upper and the lower arms in the same phase leg, then the arm will consist of twelve SMs (\(N=13\), Thirteen-levels MMC). Consequently, the phase-shifted PWM triangular carriers in one MMC arm are shifted 360/12 = 30\({}^{\circ}\) between each other.
## 1 Introduction
### State-of-the-Art Study
#### 1.1.2 ANNs Realized at the Transistor Level  
There are not many studies in the literature on the implementation of ANNs as specialized integrated circuits. The existing realizations can be divided into digital and analog. For analog solutions, only three comparable designs can be indicated, as reported in [4; 11; 12]. The solutions reported in [11; 12] are mentioned here only as an illustration, as they were designed in much older technologies (CMOS 2 \(\upmu\)m) that are not comparable to currently used technology. In [4] is reported a fully analog neural network designed by us earlier, in a similar technology (CMOS 180 nm) as the current prototype (CMOS 130 nm). This ANN consisted of four neurons. Each of them contained three calculation channels corresponding to three inputs of the ANN and thus three weights per each neuron. The ANN worked in the current mode. All main components were designed by us from scratch [13; 14; 15]. Design of such NNs is a complex task, as after the fabrication no changes are possible. The design process requires comprehensive simulations to check as many scenarios as possible [16] to make the fabricated chip a universal solution.  
As regards state-of-the art digital solutions, on the one hand, solutions based on field programmable gate arrays (FPGA) can be indicated [17; 18; 19; 20]. However, such solutions are not comparable with our works, which results from the fact that in this case the emphasis is put on different aspects. For example, in our solutions, the miniaturization of the circuit, as well as reduction of energy consumption, is of paramount feature, while, in the FPGA realization, the emphasis is put rather on the convenience and time spent on the design, while the two mentioned parameters are usually of second importance.  
On the other hand, to some extent, selected circuits designed for the pattern recognition applications may be comparable with self-organizing ANNs [21; 22; 23]. In such circuits, some components have a similar structure. For example, the block used to calculate the distance between two vectors of signals and the block used to determine which internal pattern is the most similar to an input pattern  
Figure 1: A general structure of the wireless intelligent sensor.  
are the same as in the self-organizing ANNs. However, in these solutions, the adaptation block, which is one of the subjects of the presented work, is not used. There is also no clock generator described in these works.
## 2 Materials and Methods
### Evaluation of Biofilm Formation  
B Biofilm formation ability was assessed in the group of 30 _E. coli_ strains derived from cows with subclinical mastitis and 30 strains isolated from cows with clinical mastitis, using the crystal violet method [16]. The studied strains were plated on Trypticase soy agar (TSA) (bioMerieux) and incubated at 37 \({}^{\circ}\)C for 24 h. Next, bacterial suspensions in Trypticase soy broth (TSB) (0,5 Mc) (bioMerieux) were prepared and 200 \(\upmu\)L of each suspension was placed in 96-well plates, in triplicates, for each strain. _Staphylococcus aureus_ ATCC 6538P (very strong biofilm-forming strain) and _E. coli_ ATCC 35218 (weak biofilm-forming strain) reference strains were used as controls. A negative control (200 \(\upmu\)L of TSB medium) was made in at least four repetitions. Plates were incubated at 37 \({}^{\circ}\)C for 24 h in a humid chamber. After 24-incubation, the suspensions were removed, the wells were rinsed with sterile distilled water and left to dry at 37 \({}^{\circ}\)C for 20 min. Next, 200 \(\upmu\)L of pure methanol (Avantor Performance Materials Poland S.A.) was added to each well. Subsequently, the plates were shaken for 20 min (400 rpm), methanol was removed and the plates were dried at 37 \({}^{\circ}\)C for 20 min. Afterward, 200 \(\upmu\)L of 0.1% crystal violet (CV) was pipetted to each well and the plates were shaken (400 rpm, 20 min). Then, the dye was removed and the wells were washed with distilled water to obtain colorless washings. The plates were left for 20 min at 37 \({}^{\circ}\)C to allow the evaporation of the remaining water. Eventually, 200 \(\upmu\)L of pure methanol was added to each well, plates were shaken (400 rpm, 5 min), and the absorbance (A) at 570 nm was measured (The Synergy HT Multidetection) (Figure 2).  
Figure 1: Scheme of the evaluation procedure of the effectiveness of dipping agents.  
Subsequently, the threshold value of absorbance (T), defined as the sum of the arithmetic mean of negative control and a triple value of its standard deviation (T = xnc + 38), was established. A value below the calculated sum was recognized as weak biofilm. Mild biofilm was determined when the value of sum was between T and 2T, moderate biofilm--between 2T and 4T, and strong for a value higher than 4T [22].
## 2 Materials and Methods
### Modelling Framework
#### 2.3.2 Sentinel Data and Indices  
The Sentinel-2 product Multispectral Instrument Level 2A (S2-MSIL2A) was made available in March 2018. The Level-2A images are bottom-of-atmosphere (BOA) reflectance in cartographic geometry and are atmospherically corrected. The satellite has a temporal resolution of 5 days, and the spatial resolution varies between 10m and 60m, depending on the wavelengths. The S2 has three vegetation red edge bands (spatial resolution--20 m) dedicated specifically to identifying and analyzing vegetation on the ground. S2 imagery was acquired for the years 2018, 2019, and 2020, with a cloud filter of less than 10% through the Copernicus Open Access Hub (Copernicus Open Access Hub, 2020). The spatial resolution of S2 bands is 10 m, 20 m, and 60 m (depending on the wavelength), and to maintain consistency, all bands were resampled to 10m spatial resolution using the bilinear interpolation technique in SNAP v.7.0. (\"SNAP--ESA Sentinel Application Platform\"). The images were then subset for the location of Clara Bog using SNAP Subset tool, specifically looking at the subcentral and submarginal ecotopes. The six widely used vegetation and water indices, as mentioned in Table 1, were extracted from Sentinel 2 imagery using SNAP Raster toolbox and were averaged for each subset (using MATLAB v.2019a). These were then used as a proxy for f\({}_{\text{PAR}}\) to model GPP using Equation (1). The formulas and descriptions of the indices are shown in Table 1.
# Comprehensive Analysis of Advanced Techniques and Vital Tools for Detecting Malware Intrusion
## 9 Case Study 2: Cisco System Breach by Yanluowang Ransomware Gang
### Future Directions
#### 9.7.2 Contextual Analysis: Enhancing Detection Accuracy through Context  
Contextual analysis considers the broader context surrounding potential security threats, such as user behavior, network activity, and environmental factors.  
* **Leveraging Big Data Analytics:** Involving big data analytics and threat intelligence feeds to contextualize detected events, enabling a more comprehensive understanding of the threat landscape.
* **Improving Cybersecurity Posture:** Enhancing the overall cybersecurity posture by providing more accurate and relevant alerts for effective incident response.
## 5 Conclusions  
In this paper, a recently introduced VMD was proposed to filter the mixed and real noisy signals contaminated by FBGs. The main problem in VMD is selecting the decomposition number \(K\) of the basic modes of BLIMFs and the balance parameter \(\alpha\). To solve the problem, mixed FBG signals were used to find the best decomposition number \(K\) and the data-fidelity constraint \(\alpha\) for VMD denoising, and SNR\({}_{\text{out}}\), the MAE, and the correlation coefficient were presented as denoising performance indicators. In Case 1, the best balance parameter \(\alpha\) was obtained by analyzing the anti-noise performance at different \(\alpha\) values. The mixed FBG input signals with different SNR noises were broken down into the given numbers \(K\) of BLIMFs through VMD. After applying the changed thresholding wavelet to the BLIMFs, the filtered signals were reconstructed from modes by summing the de-noised BLIMFs. Additionally, the previously mentioned three indexes were used to evaluate the denoising performance. The best decomposition number \(K\) could be defined when the VMD-changed thresholding wavelet algorithm had the best denoising effect. It was found that the denoising results of the proposed algorithm for the mixed signals were different. Furthermore, considering the mixed FBG signals, the denoising results show that the proposed algorithm is superior to the EMD-changed thresholding wavelet, the EMD-DFA, and the EMD-DWT filtering techniques. To illustrate the effectiveness of this proposed method, we carried out Case 2. The input signal data were collected from the crack monitoring experiment described in Section 2. Based on Case 1, the best decomposition number \(K\) was set at 6, and the balance parameter was set to 200. It was found in the quantitative results of RMSE indicators that the capability of VMD-changed wavelet methods in FBG signal denoising was higher than that of other denoising algorithms. As previous discussed, the parameter of the proposed definition methods considers the FBG signal physical characteristics and the VMD-changed thresholding wavelet algorithm denoising performed better than other three traditional methods, which can be used in FBG signals processing in the SHM field.  
Figure 8: The RMSE of four signal denoising algorithms at different crack lengths.  
**Author Contributions:** [PERSON] and [PERSON] conceived the key idea and designed the experiments, [PERSON] provided the academic support and checked the manuscript, and [PERSON] and [PERSON] performed the experiment. All authors made contributions to the writing and revising of the manuscript.  
**Funding:** This work was supported by the Natural foundation program (grant number 51705015) from the National Natural Science Foundation of China, and the Technical foundation program (grant JSZL2017601C002) from the Ministry of Industry and Information Technology of China.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Experimental Results and Discussion
### Prototype Implementation and Experiment Configuration  
The proposed watermarking model is trained on the Chinese Traffic Sign Detection Benchmark (CCTSDB) dataset [39], a comprehensive collection designed for evaluating traffic sign detection and recognition algorithms. The CCTSDB consists of 10,000 images for training and the remaining 5000 for testing to ensure the generalization of the trained model. The model is implemented using PyTorch and executed on an NVIDIA Tesla V100 GPU. All images are resized to \(512\times 512\times 3\). The performance of the watermarking model is evaluated using the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Metric (SSIM) to assess watermarking invisibility. In this paper, we utilize both subjective and objective evaluations to thoroughly assess the performance of the watermarking model.  
We also implemented a proof-of-concept prototype for video data authentication using Python. Flask [40] is used to develop RESTful APIs for security services. We use a standard Python library cryptography [41] to develop all cryptographic primitives, such as digital signature and hash function (SHA-256). Smart contracts are implemented by using Solidity [42]. All smart contracts are deployed on a private Ethereum network where all participants use Go-Ethereum [43] as client applications to interact with smart contracts. A private Ethereum network consists of six miners, and each containerized miner is assigned a single CPU core. The prototype is set up on a ThinkPad P16 equipped with a 13 th-generation Intel(R) Core(TM) i7-1370P (20 cores) and 64 GB memory.
## 1 Introduction
Additionally, it offers a holistic method to enhance the economics of biomass processing technologies by utilising solid residues/biochars in an efficient dye removal process [30]. Furthermore, whilst adsorption is the most commonly investigated method of MB removal from wastewaters [39; 40], it can also be used to evaluate a sorbent potential. It provides a measure of a specific surface area of sorbent particles in aqueous solutions, along with assessments of average pore size and distribution and dry surface area measurements [41]. This method has been used to evaluate numerous sorbents made from materials such as PANI@ZnO nanocomposites [42], metal organic framework [43], graphene-based materials [44], and fly ash residues [45]. Many research papers focus on individual biomasses [46; 47; 48; 49], but to date limited studies have comprehensively investigated the adsorption potentials of distinctly different biosorbents in MB dye removal from wastewater or explored the impact of surface chemistry on performance. This paper aims to address this gap in knowledge by presenting a comparative study on how the adsorption process interdependencies influence the MB dye adsorption properties of five distinctly different biomass feedstocks: seaweed (_Laminaria digitata_), horse chestnut husk, rapeseed residue, hazelnut husk, and whitewood. The valorisation of these five distinctly different biomass as green biosorbents to adsorb MB dye was explored based on the effects of particle size, pH, temperature, and concentration. The study also explored the adsorption kinetics, equilibrium models, and thermodynamics to determine the MB adsorption characteristics of these biosorbents.
## 5 Performance Evaluation
### Performance Comparison  
To verify the optimization of RBF neural network by the improved PSO algorithm with compression factor, different neural networks are constructed for comparative analysis. Figure 8 is a graph comparing the relationship between the fitness value and the iterations of improved PSO and PSO. It is obvious that on the premise of the same iterations, the RBF neural network based on improved PSO algorithm has better fitness results, and it also needs fewer iterations under the same fitness value. Hence, it is proved that the improved PSO algorithm has better optimization precision and faster convergence speed than the PSO algorithm.  
To evaluate the prediction performance of the IPSO-RBFNN model and compare it to different algorithms, three evaluation metrics are computed, including deformation, residual error, and mean residual error.  
\begin{table}
\begin{tabular}{c c} \hline \hline \(K\) & **Clustering Result** \\ \hline \(K=3\) & [T6], [T1–T5, T7–T10], [T11–T16] \\ \(K=4\) & [T6], [T1–T5, T8–T10], [T7, T12–T14], [T11, T15, T16] \\ \(K=5\) & [T11–T15], [T  
The \(Z\) direction deformations of different algorithms, including RBF neural network (RBFNN), BP neural network (BPNN), PSO-BPNN, PSO-RBFNN, and IPSO-RBFNN based on the experimental data at 12,000 rpm are shown in Figure 9.  
It can be seen that the predicting deformations of IPSO-RBFNN and PSO-RBFNN are fitted better for the expected value.  
To compare the prediction performance of above algorithms further, the residual error and mean residual error are computed, and the results are displayed in Figure 10.  
It is apparent that the residual errors of IPSO-RBFNN and PSO-RBFNN are smaller than other algorithms. Furthermore, Figure 10 indicates that the mean residual error of IPSO-RBFNN is smallest among all the algorithms.  
The above analysis shows that, the predictive performance of IPSO-RBFNN is better than PSO-RBFNN. As a result, compared with PSO-RBFNN, the mean residual error of IPSO-RBFNN is reduced by 10.48%.  
Figure 8: Comparison between PSO and IPSO. (**a**) The same iterations; (**b**) The same fitness value.  
Figure 9: \(Z\) direction deformations of different algorithms.
## 5 Results and Discussion
### Consolidated Trends  
After the launch of L8/OLI and S2/MSI, an increasing trend in the medium-resolution SITS exploitation for LULC classification was detected [56]. In theory, every month S2/MSI can provide three observations and L8/OLI two, providing five monthly medium resolution observations, benefiting the monitoring of abrupt changes [21]. This higher temporal resolution of global observations enabled the concept of dense SITS for surface monitoring [25], which increased the use of cloud computing platforms (i.e., Amazon Web Services--AWS and Google Earth Engine--GEE) to process big EO data [49; 57]. Due to flexibility, efficiency, and availability of with cloud-based processing, these platforms optimized the on-demand data access, bulk downloading, ingesting, and processing capabilities [58]. They also approximated the pixel- and object-based approaches for LULC mapping at different geographical scales, by encompassing segmentation and classification techniques [50; 59]. Critical reviews discussed the integration between these approaches [1; 26; 60].