esa-sceva/satcom-chunk-collection huggingface dataset:
# GASP: Genetic Algorithms for Service Placement in Fog Computing Systems
## 6 Conclusions and Future Work  
This paper presents a solution based on fog computing to address the issues of the typical scenario of a smart city, where sensors or smart devices disseminated over a geographic area produce a large amount of data. We pointed out that a traditional cloud infrastructure, with all data flows converging on a single cloud data center (or, at most, on few data centers) is at risk of network congestion. Moreover, as some applications in such scenarios are latency-sensitive (e.g., applications related to automated traffic management) or produce a bulk of data that could create congestion at the network level (e.g., widespread sensors for environmental analysis), the most suitable approach is to exploit a layer of intermediate fog nodes located as close as possible to the data sources to perform preprocessing (e.g., filtering and aggregation) or latency-critical tasks.  
The innovative paradigm of fog computing opens several new issues. In this paper, we focus on the problem of mapping the data streams produced by the sensors over the fog nodes. We provided a formal model for the problem of minimizing the overall latency experienced in the system, considering both data transfer and processing times. Furthermore, we proposed an heuristic algorithm, based on genetic programming to solve the problem without the need to rely on an external solver.  
The proposed solution is evaluated in the context of a realistic smart city scenario. The experiments show the excellent performance of the proposed genetic algorithm to solve the mapping problem. Moreover, different evolutionary strategies and genetic operators are compared to identify the best performing one in the considered scenario. Finally, we demonstrate the stability of the proposed heuristic through a sensitivity analysis on its main parameters, such as the number of generations, the probability of mutation and crossover, and the population size.  
As a future work, we plan to extend the current research, taking into account more complex scenarios that involve dynamic changes in the workload, for example, to introduce mobility in the data sources or to consider adaptive sampling techniques at the sensor level producing dynamic outgoing data rate. To support these news scenarios, we plan to provide contributions not only in terms of the definition of the topology, but also at the level of adaptive algorithms proposal.  
Theoretical model: R.L.; optimization problem software development: C.C.; genetic algorithm software development: R.L.; experiments: C.C. and R.L. writing: C.C. and R.L.  
This research received no external funding.  
The authors acknowledge the support in the preliminary stages of the research of their students [PERSON] and [PERSON].  
The authors declare no conflict of interest.
# Survey of Localization for Internet of Things Nodes: Approaches, Challenges and Open Issues
## 3 Measurement Techniques
### Range-Based Technique
#### 3.1.1 Angle of Arrival  
The angle of arrival (AoA) measurement technique is called the path of arrival or orientation measurement. The AoA is determined either by using the amplitude response of receiver antennas or the phase response of receiver antennas [16]. The angle is calculated when the maximum signal emerges from the anchor node to the unknown sensor node. An unknown sensor node's location is considered a line that makes a certain angle with the anchor node, as shown in Figure 3. Therefore, it requires at least two anchor nodes for determining the position of the unknown node. In this technique, a measurement error occurs due to multiple paths or shadows, leading to higher error in localization [16].  
Figure 3: Angle of Arrival.  
To attain desirable precision, larger antenna arrays must be used. Consequently, it demands added hardware with increased power consumption. Therefore, this technique is of limited interest, and improvements are expected for achieving optimal and feasible solutions in real-time application scenarios [17; 18].
## 2 Problem Formulation
### Vision Guidance System  
The low-cost vision guidance system consists of a consumer-grade camera and a digital signal processor (DSP). Specifically, the analog charge-coupled device (CCD) camera adopted here has a resolution of \(480\times 640\) pixels, and a set of LED lights surrounding the CCD lens. The installation way of the onboard camera on AGVs can be either looking down vertically or obliquely. Although the vertical installation way can probably avoid the interference from the surrounding illumination to the vision guidance system to some extent, it will reduce the field of view of the onboard camera for vision guidance of AGVs, thereby restricting its predictive control performance. In order to collect abundant guidance information in a larger field of view, the onboard camera is mounted in the front of an AGV, looking down obliquely at coloured stripes on the ground, as shown in Figure 1.The field of view is determined by the installation height \(h\) and the pitch angle \(\gamma\) of the onboard camera. The geometric parameters are calibrated before the vision guidance system is used.  
Coloured stripes are set up on the floor as guide paths, which is the tracking target captured by the onboard camera in real time. The video signal is first generated in the phase alteration line (PAL) standard by the analog camera and then converted by a video decoder into an 8-bit YCbCrcolour digital image for the DSP. The texas instruments (TI) @DSP DM642 is used as the image processor of vision guidance. The lateral position deviation and the orientation deviation of AGV are measured by the vision guidance system with respect to the given guide path. The workflow of the vision navigation system includes two stages: offline and online, as shown in Figure 2. The camera parameters are calibrated offline, then used online to rectify the image distortion and to convert the measurement unit from image pixel into physical dimensions, e.g., mm. Path pixel extraction can simply be regarded as an image operation of threshold segmentation, which divides the image into two regions: objects in the scene and the background, in terms of the difference of image threshold. The commonly used segmentation methods include the fixed single threshold, the fixed multiple threshold [20], the iterative optimal threshold, and the maximum inter-cluster variance (Otsu) [23]. Since under- or over-segmentation errors are unavoidable in the threshold segmentation for the current methods, path models have to be estimated according to a flock of target pixels with noise points. That is the main concern of this paper. Once the guide path is recognized from the background, two path deviations, i.e., the lateral position and orientation errors, can be calculated in the field of view of the onboard camera.
## 4 Experimental Results
### Case Four  
Finally, we aimed for the moving table to orient itself from the initial coordinates \(x=0,y=0,z=15\), and \(\theta=0^{o}\) to the final coordinates \(x=0,\ y=0,\ z=15\) cm, and \(\theta=10^{o}\) in 1.5 s. In other words, the movable table was intended to rotate only 10 degrees to the right around the x-axis without any translational movement. In order for this reference to be realized by the system, according to the kinematic equations of the system, the first and third motors must turn leftward at an angular position value of \(-7.21\) radians, and the second and fourth motors must turn rightward at an angular position value of 19.15 radians. According to the experimental results, the angular position changes of the motors are presented in Figure 12a,b. It is inevitable that different torque values affect the motors in the orientation movement, which is one of the most important features of the desired movement. When Figure 12a,b and Table 5 are examined, the performance of the sliding mode controller is quite better than that of the classical PID controller. Although higher torque values affected the motor shafts, the sliding mode controller was not affected by this situation and successfully performed these difficult reference trajectories faster and with lower error values.  
Figure 12: (**a**) Position change of the motors for case four. (**b**) Position change of the motors for case four.
# Precise Mission Process Control Based on a Novel Dual-Code Group Network Plan Diagram
## 6 Results and Discussion
### Experiment Results and Discussion
#### 6.3.2 Experiment 2: With Disturbance and No Control  
In Experiment 2, we randomly apply delayed disturbances to some missions during execution. This increases the mission duration of the disturbed missions, so that these disturbed missions are not able to end at the plan end time. However, no control is applied during the entire mission process, i.e., the amount of system control is always \(0\). The purpose is to observe the effect of the disturbance on the system states and output.  
Figure 18: Diagram of the mission process in the case of no disturbance. (**a–c**) show the mission progress at \(t=0\), \(t=40\), and \(t=110\), respectively.  
As shown in Figure 19a, \(\omega_{C_{1}}(5)=1\) at \(t=5\), which proves that there is a delayed disturbance at this time for mission G1. According to the plan, the end time of G1 shown in Table 3 is 2; when \(t=5\), G1 has been delayed by 3 min. According to the system state equation shown in Equation (30), after the amount of disturbance is applied to the system, all subsequent missions of G1 are delayed sequentially. At this moment, the plan start time of E7 is delayed to the 113 th minute and the output is \(y(5)=113\), which means that the system produces an error of 3 min. This system error persists in the absence of system control. However, not all delay disturbances can affect the output. Since the constant critical mission E7, which determines the output volume, belongs to the critical path, according to the system output equation shown in (42), a disturbance to the critical path mission definitely has an effect on the output volume, such as mission G1 in Figure 19a. For the non-critical missions, they have a certain amount of mission duration redundancy due to the existence of a single time difference. As long as the disturbance is within the single time difference, it does not affect the output. As shown in Figure 19b, there is a disturbance in task C2 at \(t=29\), and according to Table 3, the planned duration of C2 is 17 min, and at this moment, the mission duration of C2 is extended by an additional 4 min under the effect of the disturbance. However, from the network plan diagram in Figure 19b, it can be seen that C2 has a 9-min mission duration redundancy, so the output is not yet affected by the C2 delay disturbance.  
Since no control is applied to the system, the system error gradually accumulates as the disturbance continues to be generated. As shown in Figure 19c, the execution of E7 only starts at \(t=1128\), which is 18 min delayed from the plan start time. This is due to the fact that a total of five delayed disturbances have occurred in the previous mission process, and the system error has already accumulated for 18 min. Therefore, without control, the slightest disturbance may have an impact on the plan, resulting in the phenomenon of \"mission starts, plan fails\".
# Wireless Energy Harvesting for Internet-of-Things Devices Using Directional Antennas
## 3 System Model
### Channel Model  
RF energy is assumed to undergo path loss attenuation during transmission. Path loss attenuation is affected by \(\alpha\), where \(\alpha\) is a path loss exponent varying between 2 and 6.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Beamforming** & **Battery** & **Research Goal** \\ \hline
[24] & None & Yes & Optimizing WET time period for data Transmission \\ \hline
[25] & None & Yes & Optimizing WET time period and avoiding \\  & & & energy overflow \\ \hline
[28] & Yes & Yes & Minimizing energy consumption with single \\  & & & WET source \\ \hline
[29] & Yes & No & Optimizing WET time period for data transmission \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 1: The architecture of RF energy harvesting.  
The value of \(\alpha\) is influenced by the environment. We assume that \(d_{i}\) is the Euclidean distance between the charged device \(i\) and the AP, and \(d^{-\alpha}\) is the path loss attenuation. The channel gain of device \(i\) is indicated by \(h_{i}\). \(\mu\) is the energy conversion efficiency of converting RF energy to DC via a recharged device. The value of \(\mu\) can be used to estimate the energy acquired by the device. In this work, we assume that the energy conversion efficiency is a constant to simplify the calculation. \(P_{t}\) is the transmission power in Watts, and \(P_{r}^{i}\), the power received by device \(i\), can be expressed by Equation (1) [22; 27; 28]. The battery level of device \(i\) in Joules is updated by Equation (2), where \(B_{i}(t)\) is the current battery level, and \(B_{i}(t+1)\) is the battery level in the next time interval.  
\[P_{r}^{i}=P_{t}d_{i}^{-\alpha}h_{i}G_{t}G_{r}\mu \tag{1}\]  
\[B_{i}(t+1)=B_{i}(t)+P_{r}^{i}\times\textit{one time unit} \tag{2}\]
## 2 Materials and Methods
### Antibacterial Susceptibility--Agar Disc Diffusion Test  
Bacterial suspensions were prepared according to the EUCAST (European Committee on Antimicrobial Susceptibility Testing) standards [31] with an optical density of 0.5 according to the McFarland standard measured with a McFarland optical densitometer (Biosan, Riga, Latvia). Bacterial suspensions were inoculated with a sterile cotton swab on Mueller-Hinton (MH) (Oxoid, Basingstoke, UK) agar or Mueller-Hinton agar with 5% sheep blood (MHBA) (Oxoid, UK). Four sterile filter paper disks (\(\mathcal{O}\) = 5 mm) were impregnated with 15 \(\upmu\)L of semi-solid extracts of _T. vulgar_ flowers or leaves and placed on agar plates. The MH and MHBA agents were incubated in a thermostat (Memmert, Schwabach, Germany) for 24 h at 37 \({}^{\circ}\)C. The antibacterial properties of the extracts were examined by measuring the diameter of the sterile zones.  
Since this disc diffusion test had some limitations for the determination of antibacterial activity of the plant material [32], we used the broth microdilution method to determine quantitative effects of extracts, which is described below.  
Broth Microdilution Method for Determination of Minimum Inhibitory Concentration and Minimal Bactericidal Concentration  
The broth microdilution method was used to determine the MIC (minimal inhibitory concentration) and MBC (minimal bactericidal concentration) of lyophilized _T. vulgar_ flower and leaf extracts according to the EUCAST standard [31]. Whole yields of dry extract powder (obtained by the method described in Section 2.2) were dissolved in 2 mL of dimethyl sulfoxide DMSO (Sigma Aldrich, St. Louis, MO, USA). This compound solution was mixed with 50 \(\upmu\)L of Mueller-Hinton broth (MHB) (Oxoid, UK). Two-fold serial dilutions were made to obtain extract solutions ranging between 7.9 and 125.9 mg/mL  
Figure 3: Antibacterial susceptibility for the bovine mastitis clinical isolates.  
(leaf extract) and 3.4 and 53.9 mg/mL (flower extract) placed in 96-well plates (SarsTEDT, Sarstedt, Germany). Each well was seeded with 50 \(\upmu\)L of bacterial suspension (10\({}^{6}\) CFU/mL) which was made from suspension with 0.5 McFarland optical density. The 96-well plates were incubated in a thermostat (Mermmert, Germany) for 24 h at 37 \({}^{\circ}\)C. Broth without added extract was used as control. The lowest concentration of extracts that did not show visual bacterial growth was considered as the MIC value. To obtain the MBC value, extra cultivation on nonselective tryptic soy agar (Oxoid, UK) or 5% sheep blood agar (Oxoid, UK) was conducted. The lowest concentration of extracts that completely inhibited bacterial growth was considered as the MBC value.
# Modern Techniques for Flavonoid Extraction--To Optimize or Not to Optimize?
## 4 Selection of Variables for the Optimization of Modern Techniques for Flavonoids Extraction
### Supercritical Fluids Extraction
[132], the authors detected eight flavonoids in the prepared extracts, namely quercetin 3-O-polinobioside, rutin, hyperoside (quercetin-3-O-D-galactoside), quercetin-3-O-D-glucose, kaempfer-3-O-glucose, quercetin-3-O-\(\beta\)-L-arabi-nosyl-(1\(\rightarrow\)2)-\(\alpha\)-L-rhamnoside and quercetin-3-O-\(\beta\)-D-xylosyl-(1\(\rightarrow\)2)-\(\alpha\)-L-rhamnoside. On the other hand, [PERSON] et al. [136] detected six representative flavonoids (apigenin, bacalin, bacalin, luteolin, nartogenin and wogonin) when performing SFE-CO\({}_{2}\) extraction of _Scutellaria barbata_ with different percentages of ethanol as co-solvent (0-14.5%), dynamic extraction times (5-240 min), temperatures (33-62 \({}^{\circ}\)C) and pressures (10.5-31.5 MPa). Furthermore, [PERSON] et al. [137] efficiently developed SFE-CO\({}_{2}\) method to co-extract the sesquiterpene lactones and the twelve lipophilic flavonoids simultaneously from the leaves of _Tannectum parthehenium_ L. The described extraction was performed using 7% ethanol as co-solvent at a temperature of 64 \({}^{\circ}\)C and a pressure of 22 MPa. [PERSON] et al. [138] also showed the positive effect of using co-solvent application in the SFE-CO\({}_{2}\) on the extraction yield of flavonoids, namely quercetin, kaempfer and resveratrol from _Leptocarpha rivalaris_. In the presented study, temperature had no significant effect on the extraction yield but a positive effect of pressure on the increase of extraction yield was noticed, which was explained by the rise in CO\({}_{2}\)'s solubility due to the pressure-induced growth in its density. With growing interest in using fruit and vegetable residues at the rich source of the bioactive molecules, there is also an increasing trend in using SFE-CO\({}_{2}\) extraction as an effective green extraction technology for exploring the bioactive potential of food and vegetable residues. For example, [PERSON] et al. [139] used SFE-CO\({}_{2}\) for extracting phenolics, flavonoids, fatty acids, and carotenoids from _Capsicum annuum_ industrial waste, [PERSON] et al. [140] for phenolics and flavonoids extraction from orange processing waste, [PERSON] et al. [141] for bioactives recovery from beetroot leaves, [PERSON] and [PERSON] [142] for bioactive compounds extraction from five fruit wastes including mango peels, yellow passion fruit seeds,raspberry seeds, mandarin peels, and acai berry exhausted pulp, and [PERSON] and [PERSON] [143] for the extraction of phytochemical from _Terminalia chebula_ pulp.
# Self\(-\)Mode\(-\)Locked \(2-\)um GaSb\(-\)Based Optically Pumped Semiconductor Disk Laser
## 2 Experiment Design and Setup
For the F\(-\)P etalon, any position after M\({}_{2}\) can be set because the size change of the transmission mode is very small. Here, we set \(L_{3}\) to 70 mm. The mode size on the chip surface was an ellipse close to a circle, with the major axis \(\omega_{y}\) = 252 \(\upmu\)m and the minor axis \(\omega_{x}\) = 243 \(\upmu\)m. The design of this Z\(-\)shaped cavity allows us to change the cavity length only by lengthening the \(L_{4}\) arm without moving other components. At this condition, the pump spot diameter was larger than the mode. Therefore, we introduced a variable aperture at the \(L_{4}\) arm to suppress the resonance of higher\(-\)order transverse modes [55; 56] and assist the self\(-\)mode\(-\)locking process to initiate. The aperture will not be adjusted to smaller than the size of the fundamental mode, so the mode\(-\)locking essentially operates in the soft aperture mode [24; 57]. Based on the simulation results in the previous section, we set two groups of experiments as controls. One was a relatively stable cavity with a cavity length of less than 400 mm (\(L_{4}\) = 110 mm and the total length was \(\sim\)365 mm, as shown in Figure 3), and the other one was a relatively unstable cavity with a cavity length of more than 800 mm (\(L_{4}\) = 645 mm and the total length was \(\sim\)900 mm). The laser output through the plane output\(-\)coupling mirror M\({}_{3}\) will incident in a wedged plane beamsplitter with AR coating at 2 \(\upmu\)m, as shown in Figure 2a. About 99% of the laser transmission incident into the optical power meter kits. Two beams of weak reflected laser with different angles were used: one beam was coupled into the optical spectrum analyzers, and the other beam incident into the high\(-\)speed photodetector connected to the digital oscilloscope. Figure 2: (a) Schematic diagram of the Z\(-\)shaped self\(-\)mode\(-\)locked optically pumped GaSb SDLs. (b) Photograph of the self\(-\)mode\(-\)locked optically pumped GaSb SDLs experiment setup with a Z\(-\)shaped cavity. (c) The calculated fundamental transverse mode of the resonator configuration.
# A Novel Perceptual Hash Algorithm for Multispectral Image Authentication
## 3 Our Perceptual Hash Algorithm Design
### Band Fusion-Based Edge Feature Extraction
#### 3.2.2 Feature Extraction  
Selecting the fusion rules is of great importance; it directly affects the fusion quality and the sensitivity of the perceptual hash algorithm. For each grid set \(G_{wh}\), the features are extracted and fused based on discrete wavelet transform (DWT) with adaptive weighted rules.  
The whole process can be divided into the following steps:  
1. To obtain the fixed length of hash values, each grid \(G_{wh}^{k}\) is firstly conducted with the normalization of bilinear interpolation to resize with the size of \(m\times m\).
2. Two-level DWT is applied to each resized grid, which is decomposed into different kinds of coefficients at different scales. To extract more robust detailed information for generating hash values, we choose the two-level high-pass coefficients \(LH_{2}\), \(HH_{2}\), and \(HL_{2}\) as the basic perceptual feature.
3. For each grid, the three sub-bands \(LH_{2}\), \(HH_{2}\), and \(HL_{2}\) are fused into one matrix though the fusion rule of maximum, and the fusion result is expressed as a matrix denoted by \(M_{wh}^{k}\).
4. For each band cluster \(IC_{n}\), the adaptive weighted fusion is made on the matrix \(M_{wh}^{k}\) of the grid in different bands, and the fusion matrix is denoted \(FM_{wh}\). The fusion process should satisfy the followed conditions, i.e., the preservation of as much detailed information as possible. To do this, the fusion coefficient of each matrix is as follows:  
\[\alpha_{k}=\frac{E_{wh}^{k}}{E_{wh}^{Total}}=\frac{\sum\limits_{i=1}^{n}E_{wh} ^{i}}{\sum\limits_{i=1}^{n}E_{wh}^{i}} \tag{6}\]  
where \(\alpha_{k}\) is the weighted coefficient of the matrix \(M_{wh}^{k}\) of the grid in the \(k\)th band, and \(E_{wh}^{k}\) is the entropy of the corresponding grid. Obviously, \(\alpha_{k}\) depends on the entropy of the grid and is different from other areas. Then, the pixel of the matrix \(FM_{wh}\) can be computed as follows:  
\[FM_{wh}(i,j)=\sum\limits_{k=1}^{n}\alpha_{k}M_{wh}^{k}(i,j) \tag{7}\]  
A fusion example of sub-band \(HL_{2}\) is given in Figure 5, where Figure 5a-c show the intermediate frequency components of the grids in In\(R_{3}\), In\(R_{4}\) and In\(R_{7}\) respectively, and Figure 5d shows the fusion result. It is observed that the fusion result retains the obvious edge features of the grid in several bands.  
Figure 5: A fusion example of the sub-band: (**a**) the intermediate frequency components of the grid in In\(R_{3}\); (**b**) the intermediate frequency components of the grid in In\(R_{4}\); (**c**) the intermediate frequency components of the grid in In\(R_{7}\); (**d**) the fusion result.