esa-sceva/satcom-chunk-collection huggingface dataset:
### Organization  
This paper is structured as follows: Section 2 presents a comprehensive literature review, highlighting recent advancements in NOMA and C-NOMA, iSIC and HWD effects, HNOMA, and RIS and IOS technologies. Section 3 outlines the system model, describing the pairing schemes, IOS integration, and considerations for iSIC and HWDs. Section 4 delves into the performance analysis, deriving analytical expressions for the outage probability, throughput, diversity order, and SSE and quantifying the IOS's impact. Section 5 discusses the simulation results, validates the derived analytical expressions, and shows the enhancement in the performance of the proposed system. Finally, Section 6 concludes the manuscript by summarizing key findings and suggesting directions for future research.
## 4 Results and Discussion
### DIC-PDDO for Noise Reduction
The DIC strain filter of size 15 does not effectively reduce the generated noise (Figure 15a left) resulting in almost 20 times higher maximum absolute strain value than the accurate DIC values in Figure 15c. Only by increasing the DIC strain filter size up to 57 or 75 reduces the generated noise, but the peak values of strains are lost (see red circle in Figure 15). On the  
Figure 14: Effect of the input noise on the calculated strains at different levels of deformation: (**a**) \(\varepsilon_{x}\) strains, (**b**) \(\varepsilon_{y}\) strains. Figure 15: Strain field results: (**a**) noisy DIC; (**b**) PDDO to noisy DIC; (**c**) not noisy DIC. contrary, the results obtained by applying the PDDO to the noisy DIC displacement data do not lose the peak values (Figure 15b). Thus, it can be concluded that the PDDO method applied to noisy DIC displacement data can filter the input noisy data and smoothen the results while not losing the strain resolution due to filtering as it is the case for the DIC strain filter. Based on this feature, the PDDO can be considered for implantation in DIC calculation software, as an alternative method to the ones currently existing in different DIC software for the DIC strain evaluation (e.g., based on displacement shape functions, strain shape functions, strain filters, etc. ).
## 2 Materials and Methods
### Valorization of Tomato Peels
#### 2.2.2 Synergistic Application of UAE-MAE for the Recovery of Lycopene  
The recovery of lycopene from the dried peels was performed using a synergistic application of the UAE-MAE method. The extraction was carried out using the XO-SM50 Ultrasonic Microwave Reaction System (Nanjing Xianou Instruments Manufacture Co., Ltd., Nanjing City, China). In each experiment, the dry powder of the peels was mixed with ethyl acetate, which was the extraction solvent. The extraction process was conducted at 45 \({}^{\circ}\)C for 8 min, with the ultrasonicator operating in a pulse mode of 1 s on and 1 s off. To investigate the effects of extraction parameters on the lycopene content (mg/100 g of tomato peels) and extraction yield (EY, %), response surface methodology with a Central Composite Design (CCD) was employed. The independent variables--solid/liquid ratio (g/mL), microwave power (W), and ultrasound power (W)--were optimized within the ranges of 0.03-0.1 g/mL, 0-500 W and 0-600 W, respectively (Table 1). After the extraction was complete, the lycopene extracts were obtained following the procedure outlined in Section 2.2.1. Each experiment was conducted in triplicate.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & & **Variable Levels** & & **Observed Values** \\ \hline \multirow{3}{*}{**Run**} & \(\mathbf{X_{1}}\) & \(\mathbf{X_{2}}\) & \(\mathbf{X_{3}}\) & \(\mathbf{\mathrm{Lycopene}}\) & \multirow{3}{*}{**Lycopene Content**} \\  & **(Solid/Liquid** & **(Microwave** & **(Ultrasound** & **(mg/100 g of Tomato** & **EY (\%)** \\  & **Ratio, g/mL)** & **Power, W)** & **Power, W)** & **Peels)** \\ \hline
1 & 0.03 & 0 & 600 & 9.59 \({}^{\mathrm{f}}\)\(\pm\) 1.01 & 23.58 \({}^{\mathrm{i}}\)\(\pm\) 1.20 \\ \hline
2 & 0.03 & 200 & 600 & 25.5
## 2 Empirical Model for Calculation of Mechanical Properties  
### Modulus of Elasticity for UD Composites  
The iso-strain approach states that the strain in the composite is equal to the strain in the matrix and fibers. Based on a simple model for unidirectional fiber-reinforced composite material, following the assumptions of the uniform diameter and the properties of fibers, with parallel orientation and continuous fiber, the following expression for the modulus of elasticity was derived [2]:  
\[E_{cl}=E_{f}V_{f}+E_{m}V_{m} \tag{1}\]  
Similarly, the stress in the transverse direction of the composite is equal to the stress in the transverse direction of the matrix and fiber. Using this model, the transverse modulus of elasticity of unidirectional fiber-reinforced composite is expressed as follows [2]:  
\[E_{ct}=\frac{1}{\frac{V_{f}}{E_{f}}+\frac{V_{m}}{E_{m}}} \tag{2}\]  
Another expression to predict the transverse modulus of elasticity of a unidirectional fiber-reinforced composite was developed by [PERSON] and [PERSON] and is expressed as follows [22]:  
\[\frac{E_{ct}}{E_{m}}=\frac{1+\zeta\eta V_{f}}{1-\zeta\eta V_{f}} \tag{3}\]  
\[\eta=\frac{\frac{E_{f}}{E_{m}}-1}{\frac{E_{f}}{E_{m}}-\zeta} \tag{4}\]  
where, \(\zeta\) is a constant for individual fiber cross-section. For fibers having a square or circular cross-section, [PERSON] and [PERSON] have taken \(\zeta=2\).  
The expression for the shear modulus of elasticity of the unidirectional fiber-reinforced composite was also derived using the same constant stress and the iso-stress model that was used for the transverse modulus. The expression for the shear modulus is as follows:  
\[G_{lt}=\frac{G_{f}G_{m}}{G_{m}V_{f}+G_{f}V_{m}} \tag{5}\]  
[PERSON] and [PERSON] [22] also developed an expression for the shear modulus that is more accurate and is as follows:  
\[\frac{G_{lt}}{G_{m}}=\frac{1+\zeta\eta V_{f}}{1-\zeta\eta V_{f}} \tag{6}\]  
For the shear modulus, [PERSON] and [PERSON] suggested that \(\zeta=2\).  
\[\eta=\frac{\frac{G_{f}}{G_{m}}-1}{\frac{G_{f}}{G_{m}}+\zeta} \tag{7}\]  
The Poisson ratio is of two types, including the major and minor Poisson ratio. The major Poisson ratio expresses the relationship of the longitudinal stress to the transverse strain, whereas, the minor Poisson ratio expresses the relationship of the transverse stress to the longitudinal strain. In the case of unidirectional fiber-reinforced composites, the major Poisson ratio is only of significance where the minor Poisson ratio is very small and is neglected. The expression for the major Poisson ratio is also derived using the iso-stress model. The expression for the major Poisson ratio is as follows [2]\[v_{lt}=v_{f}V_{f}+v_{m}V_{m} \tag{8}\]
# Application of Deep Convolutional Neural Networks and Smartphone Sensors for Indoor Localization
## 4 Experiment and Results  
This section contains the description of the setup and experiments conducted to evaluate the accuracy of the proposed approach. The results from CNN classifier and localization process are discussed separately.
## 4 Discussion
The parameters that affect the response of yeasts during the biosynthesis of aromatic compounds are diverse [7]. The production of active aromatic compounds is directly related to the yeast strain used in fermentation [1], as the genome of each strain is unique and will ultimately impact the final aroma profile of the beverage [40]. This is very well reported in _S. cerevisiae_[12; 41; 42; 43], while in _S. eubayanus_, only a handful of studies have evaluated the aromatic profile in beer [6; 23; 36]. Other relevant parameters reported are word composition, oxygen [44], yeast assimilable nitrogen (YAN) [11; 45; 46], and temperature of fermentation [7; 47; 48]. In the present study, it was possible to observe that temperature exerted an effect on the total production of VCs depending on the genetic background of the _S. eubayanus_ strains. These changes have been previously reported in yeast of the genus _Saccharomyces_, where fermentation temperatures above 20 \({}^{\circ}\)C modified the production of VCs in beer and wine [47]. A special case was the POF 4-VG, where no changes were observed in strain CL905.1, while in CL602.1, its content significantly increased when the fermentation temperature was increased (12\({}^{\circ}\) to 20 \({}^{\circ}\)C). Most reports focused on the study of 4-VG have set out to find ways to reduce their production within the fermentation process by modulating precursors in the word [8]; however, our results also demonstrate the impact of strain \(\times\) temperature on the production of 4-VG. From a physiological perspective, none of the parameters analyzed in this study may solely explain the changes in the VCs produced in _S. eubayanus_ strains, suggesting a complex temperature \(\times\) strain response under fermentative conditions. An example is the YAN content. During alcoholic fermentation, the cells import and metabolize the YAN in the word together with other nutrients to produce biomass as well as VCs [1; 30; 49; 50]. Therefore, depending on the amount and type of assimilated YAN, different VCs will be generated, thus determining the aromatic profiles of the fermented word [7; 50]. The YAN content can be separated into two groups, ammonium and amino acids. Ammonium is the primary nitrogen source in beer and wine yeasts [30]. Nitrogen consumption has been widely studied in wine yeast, establishing hierarchies in nitrogen source utilization [29]. For example, ammonium, glutamine, glutamate, and asparagine are considered good or preferred nitrogen sources. In _S. eubayanus_, we found that ammonium, aspartic acid, glutamic acid, serine, and threonine nitrogen sources were rapidly consumed and may represent the preferred nitrogen sources for word fermentation. Interestingly, no differences were observed between strains in terms of such preferences, although temperature significantly impacted the nitrogen consumption rate at early timepoints.
## 2 Materials and Methods
### Data Aggregation and Filtering  
After combining and cleaning the data, a dataset that contained 2533 records (rows) about solar irradiance, temperature, cloudiness (with various factors), and SPP generation in the period from 6 May 2020 to 23 February 2022 with a resolution of three hours was obtained.  
The calculation of the volume of lost data when creating the final dataset is given in Table 3. Due to the reduction of data to the latest date (6 May 2020), in order to obtain the intersection of all samples, it was already necessary at this stage to abandon 491 days of generation and weather data records on climatic factors (without irradiance), as well as from 191 days of insolation data records. Also, due to the need to bring the data to a lower resolution (one hour), records of consumption on feeders at time stamps xx:30 (hours:minutes) had to be excluded from the final dataset. Then, we excluded records in which the power generated by the SPP was 0 (since such records are not informative for forecasting) and intersected the obtained data with the weather data, which were taken from 07:00 with a resolution of three hours. Therefore, in the non-zero SPP generation interval, hits from three to five weather description hours (07:00/10:00/13:00/16:00/19:00) and the remaining six to eight hours (08:00/09:00/11:00/12:00/14:00/15:00/17:00/18:00) were discarded.  
Features were classified according to Figure 1. A correlation analysis allowed us to exclude some of the less informative meteorological features: _Po_, \(P\), _Pa_, \(U\), _DD_, _Ff_, _ff10_, _VV_, _Td_, _RRR_, _tR_, \(E\), _Tg_, _E'_, and _sss_.  
Figure 1: Types of data in the considered dataset.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Initial Number of Records**} & \multirow{2}{*}{**Due to Mismatched Records**} & \multicolumn{2}{c}{**Due to the Identification of Data to Time Ranges**} & \multicolumn{2}{c}{**Due to the Data to a Weather**} \\  & & & **Date to Mismatched time Ranges (Records/\% of Total)** & **Reduction of Data to One Hour Discreteness (Records/\% of Total)** & **Discarding of Hours with Zero Generation of SPP (Records/\% of Total)** \\ \hline Generation & 27,600 & 11,784/42.7\% & – & 9490/34.3\% & 3793/13.7\% \\ Insolation & 20,401 & 4584/22.5\% & – & 9490/46.5\% & 3793/18.6\% \\ Consumption & 31,633 & & 15,817/50\% & 9490/30.0\% & 3793/12.0\% \\ Weather & 9087 & 3928/43.2\% & – & 2626/28.9\% & – \\ \hline \hline \end{tabular}
\end{table}
Table 3: Amount of high-quality data lost when creating the dataset.
## 2 Materials and Methods
### Mechanical Tests  
The Vickers hardness test was performed on three specimens of each subgroup using the hardness testing machine ZHV20/Z2.5 (Zwick-Roell GmbH & Co. KG, Ulm, Germany) and repeated 5 times. Tests were run according to the ISO/TS 19278 norm. A 136\({}^{\circ}\) pyramidal indenter was pressed into the material with a force (F) of 4.903 N for 10 s. Images of the resulting impression were acquired using the optical microscope with a magnification of 20:1, that the hardness testing machine ZHV20/Z2.5 is equipped with. The diagonals d\({}_{1}\) and d\({}_{2}\) of the impression were measured manually using the software testXpert (Zwick-Roell GmbH & Co. KG) (Figure 1). Then, the average diagonal d and Vickers hardness HV0.5 were calculated using the following equations.  
\[\text{d}=(\text{d}_{1}+\text{d}_{2})/2 \tag{1}\]  
HV0.5 = 0.1891 \(\times\) F/d\({}^{2}\)  
\begin{table}
\begin{tabular}{c c c c} \hline
**Group \$ [\$]** & **Printing Method** & **Printer, Manufacturer** & **Resin, Manufacturer** \\ \hline
0 & DLP & Micro Plus XL, Envisiontec Inc. & E-Guide, Envisiontec Inc. \\
1 & DLP & NextDent 5100, Vertex-Dental B.V. & NextDent SG, Vertex-Dental B.V. \\
2 & DLP & ASIGA MAX, Pluradent GmbH \& Co. KG & Optiprint Guide, dentona AG \\
3 & SLA & Form 3, Formlabs Inc. & Dental SG, Formlabs Inc. \\
4 & LCD-SLA & Slash Plus, UniZ Technology LLC. & zSG Amber, UniZ Technology LLC. \\ \hline \end{tabular}  
\({}^{\$}\) for each of the five groups, 15 samples divided in 3 subgroups (\(n=5\) per subgroup): untreated; cycle 1 (121 \({}^{\circ}\)C, 1 bar, 20.5 min); cycle 2 (134 \({}^{\circ}\)C, 2 bar, 5.5 min). DLP: digital light processing; LCD-SLA: liquid crystal display stereolithography; SLA: stereolithography.  
\end{table}
Table 1: Printers and resins used in this study, with details of the number of specimens per group and subgroup.  
To evaluate autoclaving-induced changes in terms of flexural properties of the tested materials, a 3-point bending test was performed following the test standard DIN EN ISO 178 on all specimens (\(n=75\)). The tests were performed using the material testing machine ZMART.PRO (Zwick-Roell GmbH & Co. KG) with a testing stamp radius of 5 mm that bends the specimens with a speed of 2 mm/s. The specimens were positioned such that the acquired ratio of the test piece length (l) to the distance between the support points (d) was 1/d = 16 (Figure 2). The software testXpert (Zwick-Roell GmbH & Co. KG) was used to detect the flexural moduli.
## 4 Method for UAV Electromagnetic Interference Suppression  
Typical UAV electric signal path comprises a power circuit and a signal loop (Figure 4).  
Electromagnetic interference is mainly caused by three elements: the source of interference, the way of interference, and the device being interfered with. Through the analysis of the working environment of the electric power inspection drone, it is known that strong electromagnetic fields are distributed around the wires of UHV transmission lines, and the strong electromagnetic waves emitted by them are the main interference source. The electronic devices that are susceptible to interference in UAV systems mainly include flight control systems, various measurement modules (IMU, GPS locator, barometer, magnetic compass, etc.) and signal receivers. Therefore, the core of the design of anti-electromagnetic interference schemes for UAVs mainly lies in how to protect these easily interfered devices [40].
## 5 Future Scope and Applicability of the Proposed Model in Seaports
### Integration of Autonomous Vehicles  
With the rapid evolution of autonomous vehicle technology, our model can seamlessly integrate with autonomous drayage trucks operating within seaports. Leveraging the model's capabilities in lane identification and parking spot detection, autonomous vehicles can navigate port premises with heightened precision and safety. This integration not only enhances the efficiency of container drayage operations, but also reduces the risk of human error and optimizes the traffic flow within seaports.
# Dynamic Behavior of Twin-Spool Rotor-Bearing System with Pedestal Looseness and Rub Impact
## 2 Modeling
### Coordinate System  
According to the definition of Euler angle [24], establish the generalized coordinate system shown in Figure 1  
* \(OXYZ\): Fixed coordinate system with origin \(O\).
* \(OX^{\prime}y^{\prime}z^{\prime}\): It is obtained by rotating \(OXYZ\) counterclockwise about the \(Ox^{\prime}y^{\prime}z^{\prime}\) by an angle \(\Gamma\).
* \(OX^{\prime\prime}y^{\prime\prime}z^{\prime\prime}\): It is obtained by rotating \(Ox^{\prime}y^{\prime}z^{\prime}\) around the \(y^{\prime}\) axis by an angle \(B\) counterclockwise.
* \(Oxyz\): It is obtained by rotating \(Ox^{\prime\prime}y^{\prime\prime}z^{\prime\prime}\) around the \(x^{\prime\prime}\) axis by an angle \(\Phi\) counterclockwise, in which case the positive direction in the \(x\) axis is the rotor rotation direction.  
The change of position from the fixed coordinate system \(OXYZ\) to the coordinate system \(Oxyz\) is expressed in the generalized form:  
Define the displacement of a rigid body in a fixed coordinate system as\((V,W,S)\). At this point, the general motion of the rigid body can be expressed in terms of three Euler angles \((\Gamma,B,\Phi)\) and three translational coordinates \((V,W,S)\) in the fixed coordinate system.
## 3 Results and Discussion
### Sequence Analysis  
The obtained sequences were vector trimmed using DNA Baser and aligned with all resulting sequences to obtain the final transcript. The deduced sequence included the start methionine at 348 bp resulting in 347 bp of the 5' untranslated region (the UTR is shown in the solid box in Figure S1), the stop codon at 2,483 bp and a 3' UTR region followed by the poly-A tail of 29 adenine residues. No signal sequence was identified using the web-based software, SignalP 4.1 [15]. Combining the 5' contig and the resulting 3' contig, the complete cDNA was found to be 2,822 bp in length, as shown in Figure S1. The open reading frame was deposited into GenBank and is listed as Accession Number EU934738.3. The 5' UTR consisted of several conserved transcription factor binding sites, as shown in Figure S2 and listed in Table S1. All of the transcription factors that bind to the sites identified [23; 24; 25; 26; 27; 28] have been well studied in _Drosophila_ and correspond to factors produced in the saliva glands consistent with the spPEP having regulated expression in the salivary glands of the sum pest.  
The deduced amino acid sequence aligned with the insect PEP amino acid sequences used to create the degenerate primers, bacterial sequences for which the structure has been determined and that of _Sus sussex_ is shown in Figure 2. The first twenty hits from BLASTX [12] are shown in the Supplemental Data as Table S3. The first 19 hits were PEPs of closely-related insects with 85% coverage. The highest identity was found to be 56% to _Bombus terrestris_ and _Daphnia pulex_. _Nasonia vitripennis_ and _Apis mellifera_ were both only 55% identical, and _Camponotus floridanus_ and _Harpegnathos saltator_ were 53% identical. Further supporting the fact that the sequenced DNA is a prolyl endopeptidase, the spPEP sequence contained the conserved domain for serine proteases of type S9A with the conserved catalytic domain consisting of Ser-Asp-His aligning (boxed residues in Figure 2). A phylogenetic tree was constructed using the alignment and was performed using Phylogeny.fr [16] obtained from ClustalW2 with the top 25 hits from BLASTX in addition to the sequences of the insects used in the degenerate primer design (if they did not come up in the BLASTX result), as well as four species for which the three-dimensional structure has been determined, the three bacterial sequences (_Sphingomonas capsulate_, Elizabethningaspecifica_ and _Myxococcus xanthus_) and the porcine PEP (Figure S3). The spPEP sequence separated into a node shared with _Daphnia pulex_ with both the spPEP and the _Daphnia_ PEP being separated by several nodes from the other insect PEPs and even further removed from the mammalian PEPs. The bacterial PEPs were at most 39% identical, as determined from BLASTX (Table S4).
# A Cyber-Physical System for Wildfire Detection and Firefighting
## 3 Materials and Methods
### Ground Control Station Node
#### 3.5.1 Node-RED Forest Dashboard  
Node-RED Dashboard is an extension to Node-RED that provides web-based dashboards for visualizing data and controlling devices. The dashboard created is used to monitor the status of the wildfire detection system, and provide real-time information about temperature, humidity, and wind speed (Figure 5).  
Additionally, it is used to connect the dashboard to different MQTT topics for data transmission and fire notification. Furthermore, other flows are used to create an _MQTT to ROS_ communication bridge between the wildfire detection system and the UAV (Figure 6), but also between the UAV and the notification system (Figure 7).