esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Proposed Approach
#### 3.1.4 Working Model of the Proposed Approach
```
1:Input:\(T_{all}\)
2:Output: void
3:\(T_{l}=(t_{1_{l},t_{2_{l}}},...t_{n_{l^{\prime}}})\) // set of task length inputs from \(T_{all}\)
4:\(T_{d}=(t_{1_{d},t_{2_{d}}},...t_{n_{d}})\) // set of task deadline inputs from \(T_{all}\)
5:\(T_{p}=(t_{1_{p}},t_{2_{p}},...t_{n_{p}})\) // set of task priority inputs from \(T_{all}\)
6:\(T_{f}=(\phi)\) // final sorted task list, initially empty
7:\(VmAssignmentMapping=FuzzyController(T_{l},T_{p})\) // VM to Task mapping
8:for every VM \(v_{i}\) in \(VmAssignmentMapping\)do
9: sort all tasks \(t_{j}\) on \(v_{i}\) by Earliest Deadline First
10: sort all tasks \(t_{j}\) on \(v_{i}\) by High Priority First
11: add sorted and mapped tasks to \(T_{f}\)
12:endfor
13: submit \(T_{f}\) to Public cloud
```  
**Algorithm 1** Proposed FPD Scheme  
Figure 2: Flowchart of the proposed approach.
# The Value of Fractal Analysis in Ultrasound Imaging: Exploring Intricate Patterns
###### Abstract  
Fractal analysis is a mathematical approach employed to study and describe complex patterns and structures across various disciplines, including mathematics, physics, computer science, biology and finance. Introduced by mathematician [PERSON] in the 1970s, fractals are intricate, self-similar patterns that repeat at different scales, exhibiting consistent structures upon magnification or reduction. This analysis involves generating fractals through iterative processes or recursive equations, resulting in highly detailed and complex formations. Fractal analysis enhances medical images by removing noise while preserving details and improving diagnostic quality in magnetic resonance and computed tomography scans. However, there is a lack of comprehensive studies on its application in ultrasound imaging, prompting this narrative review to investigate its use and methodology in this context. Selected papers on the use of fractal analysis in ultrasound imaging were analyzed. Out of 186 records screened, 60 duplicates were removed and 28 were discarded. The text content of 98 potentially eligible papers was checked, with 65 not meeting inclusion criteria. Finally, 33 studies were included in the review. Fractal analysis enhances ultrasound imaging by providing detailed tissue texture characterization, aiming in the diagnosis of conditions like breast and lung cancer, osteoporosis and hypertensive disorders in pregnancy. It quantifies biological structure complexity and improves diagnostic accuracy and reliability. This technique supports clinicians in making informed decisions by offering critical insights into various medical conditions.  
fractal analysis; ultrasound imaging; ultrasonography; texture analysis; fractal dimension; lacunarity; blood vessels; IVUS; pancreas; thyroid nodules +
Footnote †: journal: Medical Imaging
## 5 Results and Discussion
### Impact of Distance  
The identification results under different distances are reported in Table 4. The resolution of the individual regions in the image gradually became smaller as the distance between the camera and individuals increased. The precision and recall rate for the identification of proper hard hat use gradually decreased with increasing distance from the camera, but the precision was above 95% for all measured distances, while the recall rate was above 90% except for measurements at 7 m. The overall performance of the identification of proper hard hat use is acceptable (precision rate: 97.59%; recall rate: 89.21%). For the identification of proper full-face mask use, the performance of our approach declined only slightly as distance increased; the precision and recall rates remained higher than 97%.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Impacts** & **No.** & **Value** & **Images** & \multicolumn{2}{c}{**Hard Hat**} & \multicolumn{2}{c}{**Full-Face Mask**} \\ \hline  & & & & **Positive** & **Negative** & **Positive** & **Negative** \\  & & & & **Samples** & **Samples** & **Samples** & **Samples** \\ \hline  & 1 & 3 m & 500 & 684 & 816 & 500 & 1000 \\ Distance & 2 & 5 m & 500 & 770 & 730 & 500 & 1000 \\  & 3 & 7 m & 500 & 818 & 682 & 500 & 1000 \\  & 1 & Standing & 500 & 1582 & 1813 & 972 & 2417 \\ Individual posture & 2 & Bending & 500 & 277 & 168 & 180 & 284 \\  & 3 & Squatting & 500 & 413 & 247 & 348 & 299 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Information of collected testing dataset.  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **Predicted** & **Ground Truth** \\ \hline TP & Proper use & Proper use \\ FP & Proper use & Improper use \\ FN & Improper use & Proper use \\ \hline \hline \end{tabular}
\end{table}
Table 3: Definitions of true positive (TP), false positive (FP), and false negative (FN).
## 4 Discussion  
This study analysed the FA profiles, TG compositions, and the crystalline microstructures of BSFs obtained using different extraction solvents. To achieve this goal, three different organic solvents were used to obtain the best extraction solvents. Interestingly, the findings showed the solvents affect the composition of the BSF, in which the BSFs extracted using nonpolar solvents (hexane and petroleum ether) are of better quality than those extracted using the polar solvent. Besides that, a previous study also reported that the seed oils extracted using the nonpolar solvents have a high-quality parameter [17; 19; 20]. The results showed that the characteristics of the extracted BSFs in this study are also in agreement with the reported properties of the BSF reported by [PERSON] et al. [1], [PERSON] et al. [12], and [PERSON] et al. [9; 13], but the TFC is very low. The unsaturation value (59.84 g iodine/g), UFA (more than 50%) content, and the low-melting TG (33.45%) were high in the ethanol-extracted BSF.  
The oil's lower solubility could explain the differences in the extracted BSF's quality parameters, such as the TFC, in the polar solvents compared to the nonpolar solvents. The extracted BSF in the polar solvent was mainly disturbed by another component, such as the antioxidant-extract compounds and free fatty acids, which were co-extracted during the extraction process; thus, a low TFC was obtained in the ethanol oil-extracts. The final results, such as the oil yield, FA profile, and the physicochemical qualities, were significantly influenced by the solvents used for extraction, which have been previously reviewed [61; 62]. In fact, hexane and petroleum ether, commonly used as the extraction solvents for lipid-extraction in seed oils [22; 25], are supposed to have similar properties. However, these solvents showed significant differences in the properties of the extracted BSF.  
Although petroleum-ether-extracted BSF shows high-quality parameters; it has a low unsaturation value, a high SFA, and a high-melting TG composition, the extracts' crystalline microstructure is dense and oval and has a low yield. Concerning the TFC, FA, and TG compositions and the crystalline microstructure, hexane seems to be the most efficient among the extraction solvents studied, with a TFC of 7.7%. Still, it has a low POP (3.83%) and POS (11.78%) content and a high SOO (23.83%) level, which could result in the softening effect. The toxicity of the hexane solvents could also become a concern, such that the solvents' traces should be analysed and not exceed the permitted maximum amount in edible oil, as mentioned above. Nevertheless, petroleum ether is an alternative solvent choice known to have less toxicity than hexane, and it could also be used as an extraction solvent as it has comparable properties with hexane-extracted BSF but with a significantly lower yield.
## 2 Related Works
In [22; 23], the concept of the queuing model was used to model the resource allocation problem for the coexistence of different services with different QoS in 4G or 5G networks. In [22; 23], the coexistence of machine-to-machine (M2M) and human-to-human (H2H) communications in 4G networks was studied. In [22], a queuing model was used to analyze the impacts of coexistence between M2M and H2H communication on a 3 GPP LTE system. In [23], a resource sharing method was proposed for M2M and H2H traffic under a time-controlled scheduling scheme in LTE networks. In [24], the resource allocation problem for 5G network use cases across its main domains is analyzed using a general queuing model. This proposed scheme consists of three sub-queuing models covering the three main domains of the 5G network, which are C-RAN, mobile edge computing (MEC), and cloud data centers (CDCs). More specifically, the model aims to estimate the required QoS for network use cases in each component of the 5G architecture. In [25], a radio resource allocation for 5G use cases at 5G C-RAN is proposed using polling system methods. The aim of this scheme is mainly to protect the required delay for each 5G slice (use case) [26; 27]. In [28], the authors proposed an allocation method that allocates the cost of the network to each deployed 5G network slice. This resource allocation method saves the core network costs. However, the QoS for each 5G slice was not addressed. In [29], an energy-efficient-based scheduling algorithm for eMBB and URLLC is proposed. The proposed algorithm is implemented as a mixed-integer non-linear problem. The numerical results proved that it outperformed the existing classical schemes. In [30], the impact of the SDN controller on packet delay is studied using an analytical mode. The study focused on two services: eMBB and mMTC. The results show that the proposed model can be used to plan the resource allocation method in 5G edge clouds in order to meet the QoS requirements for all 5G slices [31; 32; 33]. To the best of my knowledge, the balance between the provision of higher resource utilization in 5G C-RAN and the protection of QoS required by each of the coexisting slices has not been investigated. Therefore, we are motivated to investigate this issue by proposing a priority-based resource allocation that can accommodate the coexistence of 5G slices at the C-RAN node. Protecting slice isolation and ensuring maximum C-RAN utilization are the main goals of the proposed scheme.
# Low-Power Rectennas in Microwave Wireless Power Transmission
## 2 Low-Power Rectifiers
### Diode Performance Optimization
#### 2.1.2 High-Nonlinearity Device Selection  
The current responsivity \(\Re_{l0}\) of a Schottky diode is defined in the square-law region as the ratio of the output DC current \(I_{0}\) to the input RF power \(P_{I0}\) when the load is shorted [44]. A simpler and general form can be derived from the I-V curve of the diode. \(\Re_{l0}\) is an intrinsic property of the diode, limited by its physical mechanisms. The maximum current responsivity attainable by any Schottky diode at ambient temperature is 19.4 A/W. This indicates that the efficiency of current commercial Schottky diodes has reached the theoretical limit, and achieving higher rectification efficiency at low power levels requires exploring devices with stronger nonlinearity.  
In 2015, the use of a backward tunnel diode to break the efficiency limitation of Schottky diodes was reported [45]. Compared to conventional Schottky diodes, the backward tunnel diode exhibits a lower parasitic junction capacitance \(C_{j}\) and higher current responsivity \(\Re_{l0}\), measured at 4.5 fF and 21.65 A/W, respectively. The photograph of the rectifier can be seen in Figure 4. At 2.4 GHz with an input power of \(-40\) dBm, it achieved a rectification efficiency of 14.6%, which is 13.3 times higher than that of the HSMS-285B  
Figure 3: Layout and prototype of: (**a**) single-band, (**b**) wide-band cooperative rectifier [36].  
and 7 times higher than that of the SMS-7630. The latest ReS\({}_{2}\)/BP negative capacitance tunnel field-effect transistor has a response time of 8 \(\upmu\)s and a current responsivity of 41 A/W [46]. The metal-insulator-insulator-metal (MI\({}^{2}\)M) diode, which sandwic thin insulator layers between two metal electrodes, utilizes the resonant tunneling effect to provide high current responsivity [47; 48]. A nanoscale spin rectifier, based on magnetic tunnel junctions, represents a novel type of rectifying device capable of collecting RF energy at extremely low input power levels of \(-62\) dBm. Energy harvesting modules based on arrays of such structures can power commercial sensors at low RF power levels as low as \(-27\) dBm [49]. Table 1 summarizes the key parameters and rectification performance of all the above high-nonlinearity devices.
# From Modeling to Failure Prognosis of Permanent Magnet Synchronous Machine
## 4 Aging of PMM's Components  
The components forming the electrical machine are the core of the stator and rotor, which are formed mainly from steel, the stator's coil, which is made from copper, the magnet on the rotor, which in our machine is a rare earth magnet called neodymium iron boron, and the shaft, constituted of aluminum. They are all subject to aging.  
The aging of the delicate components in the electrical machine is the major factor that intervenes in the calculation of the RUL of the system after the prognostic decision is formulated. The machine's components that are most subject to aging are the coil and the magnet.
## 3 Results
### Oxidative Stability of Cranberry, Black Currant and Strawberry Seed Oils  
The induction time of the oxidation reaction at 100 \({}^{\circ}\)C and 120 \({}^{\circ}\)C was determined for cranberry, black currant and strawberry seed oils in order to assess oxidative stability using pressure differential scanning calorimetry. The results are given in Table 5.  
Exemplary pDSC curves of oxidation induction time for cranberry seed oil, black currant seed oil and strawberry seed oils at 120 \({}^{\circ}\)C are presented in Figure 2 and at 100 \({}^{\circ}\)C in Figure 3. Cranberry seed oil was characterized by the longest induction time of the oxidation reaction both at 120 \({}^{\circ}\)C and 100 \({}^{\circ}\)C. This was probably due to the high content of oleic acid (18:1 n-9) and the lower content of polyunsaturated fatty acids in comparison to the tested strawberry and black currant seed oils. Oleic acid makes oils such as olive oil or rapeseed oil more stable and resistant to oxidation in comparison to oils rich in polyunsaturated fatty acids. Cranberry seed oil contained the highest amount of oleic acid (23.99%) and the lowest amount of polyunsaturated fatty acids (68.1%) among all the tested oils, and therefore, the induction time of the oxidation reaction in this case was extended and demonstrated good oxidative stability. In addition, cranberry seed oil was characterized by an intense yellow color, which may indicate the presence of natural  
\begin{table}
\begin{tabular}{c c c} \hline
**Oil** & **OIT at 100 \({}^{\circ}\)C [min]** & **OIT at 120 \({}^{\circ}\)C [min]** \\ \hline Cranberry seed oil & 212.04 \(\pm\) 3.55 \({}^{\mathrm{e}}\) & 46.09 \(\pm\) 0.63 \({}^{\mathrm{c}}\) \\ Blackcurrant seed oil & 73.00 \(\pm\) 2.29 \({}^{\mathrm{d}}\) & 17.33 \(\pm\) 0.41 \({}^{\mathrm{b}}\) \\ Strawberry seed oil & 50.04 \(\pm\) 2.33 \({}^{\mathrm{c}}\) & 9.39 \(\pm\) 0.46 \({}^{\mathrm{a}}\) \\ \hline \end{tabular} Data denoted by different letters (\({}^{\mathrm{a}}\), \({}^{\mathrm{b}}\), \({}^{\mathrm{c}}\), \({}^{\mathrm{d}}\), \({}^{\mathrm{e}}\)) are statistically different (\(\alpha\) = 0.05).  
\end{table}
Table 5: Oxidation induction time (OIT) of cranberry, black currant and strawberry seed oils at 100 \({}^{\circ}\)C and 120 \({}^{\circ}\)C.  
antioxidants [28]. Strawberry seed oil, with oxidation induction time reaching 50.04 min. at 100 \({}^{\circ}\)C and 9.39 min. at 120 \({}^{\circ}\)C, was the least stable.
###### Abstract  
This article presents the experimental validation of a model-based predictive control (MPC) strategy for the safe interconnection of voltage source inverters (VSI) with output LC filters for the grid connection of DC energy resources. The MPC is formulated as a quadratic programming (QP) problem and solved using the operator splitting quadratic programs (OSQP). The proposed approach incorporates integral action to achieve precise voltage magnitude reference tracking while accounting for modulated voltage limits and nominal current constraints within the control design. The effectiveness of the proposed strategy is validated through simulations conducted in MATLAB, demonstrating superior dynamic performance compared to the traditional hierarchical PI control. The implementation of the proposed MPC is experimentally verified on a VSI setup using the dSPACE MicroLabBox. The results confirm that the computational requirements are satisfied, establishing this approach as a practical alternative for modern power electronic systems. The proposed MPC for VSIs offers an effective approach to enforcing operational constraints, improving dynamic performance, and facilitating the robust integration of renewable energy sources in microgrids.  
constrained control; LC filter; MPC control; power electronic Article
## Results and Discussion
### Genomic Islands in Arthrobacter sp. Strain SRS-W-1-2016  
Another interesting genomic trait of strain SRS-W-1-2016 is the presence of several genomic islands (GEIs). Bacterial genomes consist of a set of core genes encoding for essential metabolic functions which are supplemented with genes acquired via horizontal gene transfer (HGT) mechanisms that may render additional metabolic functions to the recruiting bacteria such as adaptive traits and genomic plasticity, facilitating evolutionary survival [38]. GEIs have been more commonly known to render virulence or antibiotic resistance to the host bacteria, but more recently, whole genome  
Figure 5: In-silico phenotypic comparison of _Arthrobacter_ sp. SRS W-1-2016 with six closely related _Arthrobacter_ spp. using the Traitar pipeline.  
sequencing studies have also revealed other GEI-encoded functional traits classified into the following four broad categories - pathogenicity islands (PAIs), that code for virulence genes; metabolic islands (MIs), genes for the biosynthesis of secondary metabolites; resistance islands (RIs), genes that code for resistance- typically against antibiotics; and symbiotic islands (SIs), facilitating symbiotic associations of the host with other micro- and macroorganisms.  
Interestingly, several genomic islands were identified in strain SRS-W-1-2016 when compared against the complete genome of _Arthobacter aurescens_ TC1 as the reference strain (Figure 6). When the identified GEI homologues in strain SRS-W-1-2016 were further analyzed by BLAST, several of them closely affiliated with genes previously implicated in the resistance of metals, as well as the biodegradation of contaminant hydrocarbons, suggesting the strong possibility that GEIs are likely recruited via HGT to facilitate survival in an environment that is co-contaminated by heavy metals and aromatic compounds.
# A Joint Training Model for Face Sketch Synthesis
## 3 Joint Training Model for Face Sketch Synthesis  
In most exemplar-based face sketch synthesis methods, only the test photo and training photos are considered for selecting the candidate photo patches and calculating the reconstruction weight. This strategy cannot achieve an optimal result when the training photo and sketch are misaligned. In this paper, we put forward a novel exemplar-based face sketch synthesis approach, which takes the training sketch into account by joining it with its corresponding training photo for reconstruction weight representation.  
Figure 1 shows the illustration of the proposed face sketch synthesis method. First, the high-pass filtered components of the sketch images in the training data are extracted by using the Laplacian of Gaussian (LoG) filter. Then, the extracted high-pass filtered components are attached to their corresponding photo and sketch images to form the joint training data. After that, the candidate joint patch pairs are sampled with the offline random sampling strategy. In the test phase, the high-pass filtered component of the input photo is extracted with the same filter and attached to the input photo to obtain the joint test photo. For each joint test patch, a reconstruction weight can be calculated by approximating the joint test patch and joint training photos. Finally, the target patch can be constructed by linearly combining the sampled joint training sketches with the reconstruction weight and the final sketch image can be synthesized by averaging the overlapping sketch patches. The strategy to structure the joint training model and the way to calculate the reconstruction weight coefficients of the proposed method will be explained next in detail.
## 3 Proposed Solution
### Mobile Application Implementation  
The smart mobile authentication app holds a key place in our overall authentication solution, as the approval or denial of the transaction is based on the GPS location retrieved from the user's mobile device. Thus, this automated geolocation-based authentication security adds another layer of security that does not involve user interaction. Further, through mobile applications, users are able to see the notifications regarding their transactions, and they also have the ability to activate/deactivate the authentication. The backend of the mobile application was linked with the Google Firebase database, which is a NoSQL database platform that assists in developing high-quality applications with very good security levels. Data stored on Firebase are encrypted with the AES-256 symmetric key algorithm, hence providing the maximum security for the rest data. Each encryption key is also encrypted with a regularly rotated set of master keys. The mobile application was developed and implemented using Android Studio IDE utilizing Firebase as a real-time database. Figure 4 showcases the overview of our mobile application. To receive the location updates, Google Maps API was used, which uses two sources to determine the accurate location: a mobile GPS chipset and information received from the cell towers. According to the official documentation of Google Maps API, this use of GPS, cell tower triangulation, and other data sources is very accurate; however, the accuracy of the location information provided by the API can vary depending on a number of factors, including the quality of the data sources used to determine the location and the type of device being used [44]. That being said, the accuracy is generally quite high up to the nearest 20 m, and it is considered to be one of the most reliable location services available.