esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Advances in Femtosecond Laser-Induced Surface Structuring for Oil-Water Separation
## 5 Oil-Water Separation via FS Laser Structuring
In the case of a FS laser-structured PDMS surface, superhydrophobic, underwater superoleophilic, and underwater superaerophilic wetting properties were observed. Further irradiation with oxygen plasma activated the PDMS surface by inducing Si-OH groups. As a result of oxygen plasma treatment, the PDMS wettability was switched from hydrophobic to hydrophilic. Therefore, superhydrophilic, underwater superoleophobic, and underwater superaerophobic wettability were realized [73]. In light of the above results, it can be concluded that wettability control via FS laser structuring is a promising tool for oil-water separation.
## 3 Results
### Extraction Optimization  
The extraction of free fatty acids and sterols was based on a study performed by [PERSON] et al. [26], with some modifications. A freshly prepared synthetic medium (2.5 mL) was used instead of freeze-drying because it was time-consuming and the sticky caramel-like residue proved difficult to handle (for example, weighing out different sample masses). In addition, as previously mentioned, an additional dilution step was incorporated into the extraction protocol in order to bring the concentration of the lipids into the dynamic range of the MS.  
Fatty acids and sterols predominantly exist in bound form (steryl esters, triacylglycerols, phospholipids, etc.). Therefore, it was necessary to liberate the lipids of interest before extraction through saponification [24; 52]. Before taking this step, the yeast pellet was first washed with saline and then with a diethyl ether:hexane (1:1) mixture. Even though this was an additional step to include in an already multi-step procedure, it was considered necessary as residual compounds on the pellet can interfere with analyses when sensitive instruments (such as the GC-MS) are used.  
For the yeast, hexane was initially added to the saponified material as the method was initially aimed at sterols (compounds with high lipophilicity). However, hexane alone did not separate the organic and aqueous phase in the sample. Three solvent extractions were evaluated: (1) hexane followed by chloroform, the extracts then combined; (2) chloroform followed by hexane, the extracts then combined; and (3) extraction with a chloroform/hexane mixture. Small differences between treatments were observed, and although the chloroform/hexane mix was best for sterols, it did not allow the optimal extraction of fatty acids. In the end, a different mixture, consisting of chloroform/methanol was used. In this case, the main modification of the method compared to literature [24; 52]  
Figure 3: Extracted ion chromatogram (EIC, SIM mode) of the standard mixture. The insert is a zoom-in of the critical pair area (top) with the oleic acid peak in SIM mode (\(m/z\) 339.3) and linolenic acid peak in SIM mode (\(m/z\) 335.2). The critical pair zone is marked with the red rectangle.  
was the change from a 1/2 to a 1/1 for the saponified sample. The addition of brine was also considered necessary, as a clear separation between the organic and aqueous phase was not observed without it.  
The volume of sample harvested was adjusted based on the yeast growth stage. Three resulting pellet sizes were tested: 5, 50, and 200 mg. The lowest mass gave a low response in MS, while the highest resulted in a response outside the dynamic range of the detector, unless an additional dilution step was included. Most importantly, it was difficult to recover 200 mg from a 10 mL fermentation sample. The medium mass (50 mg) gave reasonable response in the MS and was easier to recover from the small volume fermentation.
# Applications of the Open-Source Hardware Arduino Platform in the Mining Industry: A Review
## 2 Arduino in Field Monitoring Systems
### Atmospheric Monitoring
Several studies have modeled situations for ventilation or escape from underground mines using atmospheric data. These results can help miners manage serious problems. To analyze the ventilation networks in small-scale underground mines, [PERSON] et al. [46] used an Arduino Mega board with various types of sensors. The network, scaled down to a 1:500 scale, was configured using pipes; centrifugal fans were used as actuators to drive the air flow. A hot wire sensor, mass flow sensor, and orifice plate sensor were used to measure the air flow. As a result, it was possible to effectively observe the air flow and control the ventilation system; however, this study has not been applied to various other gases. In addition, it is necessary to apply the study to real mine sites. [PERSON] et al. [47] simulated CO inhalation as a carboxyhemoglobin (COHb) level (%) according to the escape route when a fire occurred in an underground mine. Arduino Nano 3.0 and MQ7 sensors were used to detect CO and to transmit the measured values to a mobile phone through a Bluetooth module. The COHb level was predicted from an application installed on the mobile phone, thereby suggesting a suitable escape route. [PERSON] et al. [48] further developed this study, in conjunction with other software. An Arduino board was used for monitoring and transmitting data. Thus, the role of Arduino was similar to that of the previous study, but a specific situation was simulated in three dimensions using VentFIRE (a part of the ventilation software Ventsim (Howden, Australia)). In addition, a scaled-down model was constructed for testing the monitoring and alarm system using Arduino. As a result, the mobile application connected with the Arduino suggested a proper location for a self-rescuer device according to the data and could provide a warning when a dangerous situation occurred. To evaluate potentially explosive areas in underground coal mines, [PERSON] et al. [49] considered three conditions potentially affecting the explosion (i.e., flammable material, sufficient oxygen, and energy supply). An MQ2 sensor was connected to an Arduino UNO to measure CH4, and a WSN was configured via ZigBee. A probabilistic strategy was presented by analyzing the measured data and other conditions to simulate a potential area. Figure 6: System architecture comprising Arduino used as a microcontroller for air quality monitoring in. mines, based on Azure machine learning (AML), for air quality prediction [44].
## 3 The Proposed Method
### Verification of Finder Patterns  
Each Finder Pattern consists of a central black square with the side of 3 units (\(R_{1}\)), surrounded by a white frame with the width of 1 unit (\(R_{2}\)), surrounded by a black frame with the width of 1 unit (\(R_{3}\)). In Figure 7 there are colored regions \(R_{1}\), \(R_{2}\) and \(R_{3}\) in red, blue, and green, respectively. For each Finder Pattern candidate Flood Fill algorithm is applied, starting from the centroid \(C\) (which lies in the region \(R_{1}\)) and continuing through white frame (region \(R_{2}\)) to black frame (region \(R_{3}\)). As continuous black and white regions are filled, following region descriptors are incrementally computed:  
* area (\(A=M_{00}\))
* centroid (\(C_{x}=M_{10}/M_{00}\), \(C_{y}=M_{01}/M_{00}\)), where \(M_{00}\), \(M_{10}\), \(M_{00}\) are raw image moments
* bounding box (Top, Left, Right, Bottom)  
The Finder Pattern candidate, which does not meet all of the following conditions, is rejected.  
* Area(\(R_{1}\)) \(<\) Area(\(R_{2}\)) \(<\) Area(\(R_{3}\)) and \(1.1<\) Area(\(R_{2}\))/Area(\(R_{1}\)) \(<\) 3.4 and \(1.8<\) Area(\(R_{3}\))/Area(\(R_{1}\)) \(<\) 3.9
* 0.7 \(<\) AspectRatio(\(R_{2}\)) \(<\) 1.5 and 0.7\(<\) AspectRatio(\(R_{3}\)) \(<\) 1.5
* \(|\)Centroid(\(R_{1}\)), Centroid(\(R_{2}\)) \(|\) \(<\) 3.7 and \(|\)Centroid(\(R_{1}\)), Centroid(\(R_{3}\)) \(|\) \(<\) 4.2  
Note: the criteria were set to be invariant to the rotation of the Finder Pattern, and the acceptance ranges were determined experimentally. In an ideal undistorted Finder Pattern, the criteria are met as follows:  
* Area(\(R_{2}\))/Area(\(R_{1}\)) = 16/9 = 1.8 and Area(\(R_{3}\))/Area(\(R_{1}\)) = 24/9 = 2.7
* AspectRatio(\(R_{1}\)) = 1 and AspectRatio(\(R_{2}\)) = 1 and AspectRatio(\(R_{3}\)) = 1
* Centroid(\(R_{1}\)) = Centroid(\(R_{2}\)) = Centroid(\(R_{3}\))  
In real environments there can be damaged Finder Patterns. The inner black region \(R_{1}\) can be joined with the outer black region \(R_{3}\) (Figure 8a) or the outer black region can be interrupted or incomplete (Figure 8b). In the first case the bounding box of the region \(R_{2}\) is completely contained by bounding box of the region \(R_{1}\) and in the second case is bounding box of the region \(R_{3}\) contained  
Figure 7: Regions of the Finder Pattern candidate.  
in bounding box of the region \(R_{2}\). These cases are handled individually. If the first case is detected, then the region \(R_{1}\) is proportionally divided into \(R_{1}\) and \(R_{3}\) and if the second case is detected then the region \(R_{2}\) is instantiated using the region \(R_{1}\) and \(R_{3}\).  
The Centroid (C) and Module Width (\(MW\)) of the Finder Pattern candidate are updated using the region descriptors as follows:  
\[C=(\frac{M_{10}(R_{1})+M_{10}(R_{2})+M_{10}(R_{3})}{M_{00}(R_{1})+M_{00}(R_{2}) +M_{00}(R_{3})},\frac{M_{01}(R_{1})+M_{01}(R_{2})+M_{01}(R_{3})}{M_{00}(R_{1})+M _{00}(R_{2})+M_{00}(R_{3})}) \tag{6}\]  
\[MW=\sqrt{M_{00}(R_{1})+M_{00}(R_{2})+M_{00}(R_{3})}/7\]
# Deep Learning-Based Next-Generation Waveform for Multiuser VLC Systems
## 2 Materials and Methods
### VLC Channel  
Visible light passes through the optical medium for the transmission of data through LEDs to photodetectors. VLC systems experience multiple challenges during the propagation phase, such as shadowing when the illumination location becomes blocked from a certain region. This can also lead to information loss. The LoS and diffused components of propagating visible light form the VLC channel. Figure 6 is an illustration of the VLC channel in MIMO configuration. The main sources of the diffused components in the VLC channel are the walls and other objects in the indoor environment [18].  
Figure 4: OFDM-IM transmitter system block diagram.  
Figure 5: Receiver block diagram of OFDM-IM.  
Figure 6: VLC channel MIMO configuration.  
Establishing a point-to-point link is pivotal to effective data communication in VLC systems. This is a challenging task for mobile communication when the receiver is moving. Therefore, it is important to have enough illumination resources that can cover certain indoor areas for uninterrupted data transmission. The VLC channel gain depends on LED illumination intensity, Lambertian order, the size of the photodetector, and several reflective surfaces. The VLC channel gain from the \(p^{\text{th}}\) LED to the \(q^{\text{th}}\) photodetector is calculated in (15) as follows:  
\[h_{p,q}=\left\{\begin{array}{cc}\frac{\rho_{q}A_{q}}{d_{p,q}^{2}}R(\phi_{p}) cos\big{(}\varphi_{p,q}\big{)}&\varphi_{p,q}\leq\psi_{q}\\ 0&\varphi_{p,q}>\psi_{q}\end{array}\right., \tag{15}\]  
where \(\varphi_{p,q}\) is the incidence angle from the \(p^{\text{th}}\) LED to the \(q^{\text{th}}\) photodetector, \(\phi_{p}\) is the emission angle, \(R(\phi_{p})\) is the Lambertian radiant intensity, \(A_{q}\) is the receiver collection area, \(\rho_{q}\) is the responsivity coefficient of the photodetector, \(\psi_{q}\) refers to the receiver field of view, and \(d_{p,q}\) refers to the distance between the \(p^{\text{th}}\) LED and the \(q^{\text{th}}\) photodetector. \(A_{q}\) can be calculated as follows:  
\[A_{q}=\frac{\beta^{2}A_{PD,q}}{sin\big{(}\psi_{q}\big{)}}, \tag{16}\]  
where \(\beta\) refers to the refractive index of the photodetector, and \(A_{PD,q}\) refers to the area of the \(q^{\text{th}}\) photodetector. \(R\) (\(\phi_{p}\)) can be calculated as follows:  
\[R\Big{(}\phi_{p}\Big{)}=\frac{\Big{(}(m+1)cos^{m}\Big{(}\phi_{p}\Big{)}\Big{)} }{2\pi}, \tag{17}\]  
where \(m\) is the Lambert mode number defining the directivity of the source beam.
## 5 Results and Analysis  
This section evaluates the performance of the tomato plant multi-object recognition and positioning system based on the VGG16-UNet model. The focus is on two main aspects: segmentation performance and localization accuracy. For segmentation performance, the ability of the model to identify main stems, lateral branches, axillary buds, and background in the images will be evaluated using the intersection over union (_IoU_), mean intersection over union (_MIoU_), pixel accuracy (_PA_), and mean pixel accuracy (_mPA_) metrics. In terms of localization accuracy, two user-defined scoring criteria are used to determine whether the predicted axillary bud removal points meet agronomic requirements. This evaluation is critical to ensure that robots can accurately perform pruning tasks, which directly impacts the efficiency and quality of agricultural production.
## 5 Results
### Confusion Matrices  
Confusion matrices were used to visualize and analyze the performance of the tested neural network models in identification and recognition. A confusion matrix contains information about the actual and expected object classifications of a categorization system [40]. The diagonal line in the middle of the confusion matrix shows the significance of the prediction results. The vertical line represents false positives, while the horizontal line  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **mAP** & **mAP** & \multirow{2}{*}{**Precision\%**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**F1-Score**} & \multirow{2}{*}{**GFLOPS**} & \multirow{2}{*}{**Megabytes**} & **Inference Time** \\  & **0.5\%** & & & & & & **M**illisecond** \\ \hline YOLO-Lite & 51.7 & 27.9 & 53.3 & 48.1 & 47.0 & 14.8 & 11.3 & 10.01 \\ YOLOv5n & 83.85 & 46.35 & 74.53 & 83.91 & 77.0 & 4.2 & 3.79 & 9.0 \\ YOLOv5s & 94.61 & 63.0 & 86.70 & 93.78 & 90.0 & 16.0 & 13.8 & 9.9 \\ YOLOv5m & 97.18 & 71.69 & 90.13 & 96.86 & 92.0 & 448.2 & 42.4 & 14.0 \\ YOLOv5l & 97.04 & 72.76 & 91.60 & 95.50 & 93.0 & 108.2 & 93.1 & 22.6 \\ YOLOv5x & 98.3 & 79.8 & 94.5 & 98.84 & 96.0 & 204.4 & 173.5 & 40.5 \\ YOLOv3 & 97.6 & 74.6 & 93.8 & 95.7 & 95.0 & 155.9 & 125.9 & 27.5 \\ YOLOR & 96.80 & 75.75 & 77.58 & 97.8 & - & 80.69 & 282 & 13.38 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of YOLO-Lite, YOLOv5n, YOLOv5s, YOLOv5m, YOLOv5l, YOLOv5x, YOLOv3, and YOLOR Models.  
Figure 9: Performance comparison of [EMAIL_ADDRESS], [EMAIL_ADDRESS]:0.95, precision and recall. The IoU threshold [EMAIL_ADDRESS] means the average precision when the IoU > 0.5, and the [EMAIL_ADDRESS]:0.95 denotes the average precision when the IoU has taken 10 values between 0.5 and 0.95. These charts represent YOLOv5x, YOLOv5l, YOLOv5m, YOLOv5s, YOLOv5n, YOLOv3, YOLO-Lite, and YOLOR separately.  
represents false negatives. The data in (Figure 10A-G) show the confusion matrices of the Yolo models.
## 5 Simulation and Results
### Comparison with the Latest Published Algorithms in Literature  
GOA, GWO algorithms and their chaos variants are the only published meta-heuristic optimization algorithms for estimating equal and variable order fractional PMSM models as in [12]. The authors in [12] modified GOA and GWO for improving both of the accuracy and the rapidity of convergence of the basic algorithms. In this subsection, an excessive comparison is accomplished among the results of the novel variants and that of the literature algorithms GOA, GWO and their chaotic variants.  
Based on the results in [12], the best variants in CGWO and CGOA in case of equal order fractional model are CGWO1 and CGOA5. These variants achieved \(2.7590\ \times\ 10^{-7}\), \(\pm 8.7827\times 10^{-6}\) and \(1.7935\times 10^{-14},\pm 8.5292\times 10^{-6}\), respectively. While, C.EPSO, the C.EPSO3 and C.EPSO10 offer \(best,\pm STD\) values of \(MSE\) equal to \(4.432\times 10^{-26}\pm 4.985\times 10^{-28}\), respectively. These results prove that the newly developed variants provide more authenticity and consistency of the results than CGOW and CGOA best variants which subsequently affects on the veracity of identifying the corresponded parameters for the chaotic behavior in motor.  
In case of variable order fractional model, C.EPSO3 and C.EPSO7 exhibits \(best\) values of \(MSE\) equal to \(1.385\times 10^{-27}\) with \(STD\) values are \(2.630\times 10^{-27}\) and \(5.423\times 10^{-28}\), respectively. However, the best variants of CGWO and CGOA are CGWO7 and CGOA2 have achieved \(3.9396\times 10^{-6},\pm 1.6357\times 10^{-4}\) and \(2.4653\times 10^{-10},\pm 6.7161\times 10^{-2}\), respectively. By this way, the current work introduces more efficient technique than that in literature as C.EPSO variants achieve more veracity and uniformity of results.  
Additionally, the convergence curves of the best variants of the proposed algorithm (C.EPSO) and that of the previously published ones (CGWO and CGOA) are plotted in Figure 4 for the two fractional models. Figure 4a is indicating the case of equal order fractional model where C.EPSO3 and C.EPSO10 show the lower mean values of \(MSE\) with faster convergence speed compared with CGWO1 and CGOA10. Likewise, for variable order fractional model as in Figure 4b, C.EPSO3 and C.EPSO7 exhibit the lower mean values of MSE with faster speed of convergence. Accordingly, the (C.EPSO3, C.EPSO10) and (C.EPSO3, C.EPSO7) achieve the most optimal and consistent solutions with the fastest convergence speed for both of equal and variable order fractional models compared to the other algorithms.  
The main outcome is that by introducing the C.EPSO, the optimal parameters at which the motor behaves chaotically are identified accurately and that will affect in turn on improving of the accuracy of the control process of the motor chaos and ensure the protection of motor from ravage as well.  
Figure 4: Average convergence curves by the best chaotic variants of the proposed algorithms (C.EPSO, EPSO) and comparable ones (CGWOA, CGOA) for (**a**) C-FO-M, and (**b**) InC-FO-M.
## 2 System Model  
### Tracking Scenario and Geometry  
We set up a multi-RIS-aided localization scenario in a Cartesian coordinate system, where a multi-antenna BS localizes \(M\) moving single-antenna users by processing the received signals transmitted by users and reflected by \(K\) RISs consisting of multiple elements.  
According to Figure 2, we denote \(\mathbf{p_{B}}\in\mathbb{R}^{3}\) as the centre of BS, \(\mathbf{p}_{b}\in\mathbb{R}^{3}\) as the coordinate of the \(b\)-th antenna on the BS with \(b\in\mathcal{B}\triangleq\{0,1,\ldots,N_{B}-1\}\), \(\mathbf{p}_{k}\in\mathbb{R}^{3}\) as the centre of \(k\)-th RIS, \(\mathbf{p}_{k,p}\in\mathbb{R}^{3}\) as the coordinate of the \(p\)-th element on the \(k\)-th RIS with \(k\in\mathcal{K}\triangleq\{1,\ldots,K\}\) and \(p\in\mathcal{P}\triangleq\{0,1,\ldots,N_{R}-1\}\), \(\mathbf{p}_{m}\in\mathbb{R}^{3}\) as the coordinate of the \(m\)-th user with \(m\in\mathcal{M}\triangleq\{1,\ldots,M\}\). The distances between elements can be represented as \(d_{Bk}=\left\|\mathbf{p_{B}}-\mathbf{p}_{k}\right\|\), \(d_{km}=\left\|\mathbf{p}_{k}-\mathbf{p}_{m}\right\|\), \(d_{k;p,p}=\left\|\mathbf{p}_{b}-\mathbf{p}_{k,p}\right\|\) and \(d_{k;m,p}=\left\|\mathbf{p}_{m}-\mathbf{p}_{k,p}\right\|\). The elevation and azimuth angles between two elements are \(\theta\) and \(\phi\), respectively. The local reflection coefficient of an element of RIS is \(r_{k,m;b,p}=G_{c}e^{i\phi_{k,p}}\sqrt{F(\Phi_{k;b,p})F(\Phi_{k,m;p})}\), where \(G_{c}\) is the gain of the unit element and \(e^{i\phi_{k,p}}\) is the unit-norm load coefficient [25, 28]. The variable set \(\Phi\) includes \(\{\theta,\phi\}\), and \(F(\Phi)\) represents the normalized power radiation pattern per element, which remains constant across the frequencies of interest and is characterized in an exponential-Lambertian radiation pattern with parameter \(q\) as  
\[F(\Phi)=\left\{\begin{array}{ll}\sin^{q}(\theta),&\theta\in\left[0,\pi/2 \right],\phi\in\left[0,2\pi\right]\\ 0,&\text{otherwise.}\end{array}\right. \tag{1}\]
# Modification and Optimization of Cycloidal Gear Tooth Profile Based on Machining Error Compensation
## 4 Modifying the Parameter Optimization
### Restrictions  
(1) Curtate ratio  
The curtate ratio is crucial in cycloidal pinwheel transmission. Under the constant values of the pin wheel radius and the number of teeth, the curtate ratio will affect the carrying capacity of the cycloidal gear. According to the recommended value of the curtate ratio [39], when the number of cycloidal teeth \(z_{c}\) is 25\(\sim\)59, the curtate ratio should be 0.65\(\sim\)0.9.  
(2) Backlash  
As the input shaft rotates in the opposite direction, the output shaft lags behind the input shaft in transmission, generating backlash. The backlash is an essential indicator affecting the accuracy of RV transmission, so it should be constrained. The backlash caused by equidistant and radial movement modification can be calculated according to Formula (28) [40]. In this paper, the designed backlash value \(\Delta\varphi\) is less than 0.2.  
\[\Delta\varphi=\frac{2\Delta r_{rp}}{az_{c}}-\frac{2\Delta r_{p}}{az_{c}}\sqrt{1-{ K_{1}}^{2}} \tag{27}\]  
(3) Radial clearance  
In order to compensate for errors and facilitate assembly and lubrication, a specific radial clearance must be retained. A radial clearance that is too large will reduce the transmission accuracy of the reducer. In contrast, a small radial clearance cannot compensate for the error and meet the lubrication requirements, so the radial clearance \(\Delta r\) must be constrained. For the combined modification of equidistant and radial movement, the radial clearance can be numerically calculated as \(\Delta r=\Delta r_{rp}-\Delta r_{p}\)[41]. This paper takes the radial clearance as 0.02 mm.