esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
As additive manufacturing technologies continue to gain ground in industrial applications, the need for the accurate metrological evaluation of parts produced with advanced materials becomes increasingly critical. In this context, non-contact metrology plays a key role. This research investigates the performance of conoscopic holography as an optical metrology technique for the inspection of ceramic parts manufactured by stereolithography. However, its reliability needs to be validated, especially as factors such as material properties, surface finish, and color can significantly affect measurement accuracy. Spherical artifacts in alumina were chosen as mathematically well-defined reference elements, and a representative series was produced with the best values for the printing, debinding, and sintering parameters. These spheres were first measured via contact with a coordinate measuring machine (CMM) to establish dimensional (diameter) and geometrical (form error) reference values. These parameters were then compared with measurements obtained via conoscopic holography and optimized by means of Gaussian filters. The results indicated significant dimensional (up to 60 \(\upmu\)m) and geometrical (up to 280 \(\upmu\)m) deviations from the CMM reference data. The investigation shows that conoscopic holography does not ensure an accurate measurement method for this additive process and ceramic material, making it impossible to achieve power and frequency settings that would allow signal-to-noise ratios above 50%.  
Keywords:additive manufacturing; stereolithography; alumina; conoscopic holography; non-contact metrology; reference spheres Article
# Implementing Many-Lights Rendering with IES-Based Lights
## 3 Photometric Description of Light Sources
The numbers of the considered angles are stated in line 11 (in our example, thirty-seven vertical angles and five horizontal angles). Lines 15 to 19 show the list of luminous intensity values (expressed in candelas) captured at each angle pair (a line for each horizontal angle, each consisting of 37 values). ```
1[ESMA:LM-63-1995]
2[REPORT NUMBER]ITL36346
3[DATE]11-28-1989
4[MANWICA]LEDALITE ARCHITECTURAL PRODUCTS, INC.
5[CATALOG NUMBER]111621-PH-12 RP-NN
6[LUMIAIR]EKTRUDED SQUARE ALUMINUM HOUSING, WHITE PAINTED REFLECTORS, SEMI - SPECULAR PARABOLIC LOUVER, OPEN TOP. 7[LAMP]F40T12/CW
8[BALLIST]PHLIPS HM-140-TPS
9[MOUMINUISSWEDED-DIRECT/INDIRECT
10 TILT-NONE
11
331501375110.406340.5104
1211147.5
1305011052025303540455055606570758085909510010151015120121021251301315141515151601651710715180
14022.546576.590
15173417387104165115741480138412541108936678.8337.169.1126.1130.057.683.84.3070723.899.68177.4264.9354436.1511.4582.8646.5707.2764811.6847881.5907.6927.6939.1936.6
16173417317041654157148913881250170892.3673.476.3109.431.168.99.985.38.42.2031.485.622141.3210.4292.973.6744.9572.459.939.693.759.406.3839.9871.5966.5199.933.7936.6
17134173171121671761031461381322610790715.6500.3242.678.71
1724.1912.296.914.61018.8180.63135.91925621.5325402.4474.5
185467.629.673.474.21809.387.808.957.910.3928.7936.6
1873417217111685117411385122120710927.761.5357.923.3156.33517.667.684.99012.6763.73133.6182.4247.3312.9379.7452.3
19522.1591.6665.4766.3085.5842896.1910.7923.4936.6
191734172817151687157517841153791252111978.3826.26083.5358.6181.2
20767.7923.048.455.38010.755.89.127.5179.7242.66308.7371.6438.5
2151.4579648.1734.1832.4814.7898.4916.1926936. ```  
Listing 1: The content of an IES file. Figure 2 shows an example of visualization of the content of the IES file in Listing 1. The orientation of the luminaire's axes with respect to those of the photometric web is provided by the value of the _Photometric Type_ (the sixth value in line 11 of Listing 1). In this paper, we consider the most common case, where Photometric Type corresponds to Type C luminaires (=1 in the IES file format), i.e., architectural and roadway luminaires. For this kind of luminaire, the polar axis of the photometric web coincides with the vertical axis of the luminaire. Finally, notice that, even if the actual luminaire may have multiple lamps (like the luminaire described in Listing 1), the results are expressed considering the light as emitted by a single position in space, thus making the data perfectly compatible with the point light source used in CG. A more comprehensive description of the IES file format can be found in [29].
## 3 Results
### Synthesis of Results  
Data from three studies including 273 patients reported pain discomfort through VAS score of clear aligners versus fixed appliances [6; 31; 32; 33; 34]. Four studies provided data on a daily basis [6; 31; 32; 34], while one had data for pain discomfort at day 1, 3 and 7 after [33].  
Clear aligners promote lower pain experience than patients using fixed appliances, and the difference is statistically significant for 1, 3, 6 and 7 days of follow-up (Table 3). The overall pain experience also favored clear aligners. Funnel plot analysis revealed no publication bias (Figure 3).  
A detailed number of analgesic consumption at day 1, 3 and 7 after, were reported in three studies [6; 31; 33]. Overall, patients treated with clear aligners have significantly less analgesic consumption compared with fixed appliances group control, one day and seven days after treatment beginning (Table 4). Overall, patients with fixed appliances consume more analgesics at the beginning of treatment.  
Figure 3: Funnel plot. The outer dashed lines indicate the triangular region within which 95% of studies are expected to lie in the absence of both biases and heterogeneity (fixed effect summary log odds ratio \(\pm\) 1.96 \(\times\) standard error of summary log odds ratio). The dashed vertical line corresponds to no intervention effect. The distribution gives a clear visual impression of symmetry, which is confirmed by a \(p>0.05\).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Subgroup Meta-Analysis** & **n** & **MD** & **95\% CI** & \(p\)**-Value** & **I\({}^{2}\) (\%)** & **Tau** & \(p\)**-Value** \\ \hline
**Day 1** & **6** & **1.07** & **(0.00; 2.13)** & **\textless{}0.001** & **84.9** & **1.16** & **\textless{}0.001** \\ \hline Day 2 & 5 & 0.60 & (\(-\)0.20; 1.41) & \textgreater{}0.05 & 80.7 & 0.77 & \textless{}0.001 \\ \hline
**Day 3** & **6** & **1.25** & **(0.32; 2.17)** & **\textless{}0.001** & **83.0** & **0.99** & **\textless{}0.001** \\ \hline Day 4 & 5 & 0.79 & (\(-\)0.07; 1.65) & \textgreater{}0.05 & 83.8 & 0.84 & \textless{}0.001 \\ \hline Day 5 & 5 & 0.62 & (\(-\)0.13; 1.37) & \textgreater{}0.05 & 83.7 & 0.71 & \textless{}0.001 \\ \hline
**Day 6** & **5** & **0.76** & **(0.31; 1.21)** & **\textless{}0.001** & **67.6** & **0.38** & **\textless{}0.001** \\ \hline
**Day 7** & **6** & **0.79** & **(0.37; 1.22)** & **\textless{}0.001** & **64.2** & **0.36** & **\textless{}0.001** \\ \hline
**Overall** & **6** & **0.33** & **(0.45; 1.57)** & **\textless{}0.001** & **80.5** & **0.58** & **\textless{}0.001** \\ \hline \hline \end{tabular}  
* Test for subgroup differences (random effects model): Q = 1.62, \(p\) = 0.9514. MD–Mean Difference.  
\end{table}
Table 3: Subgroup meta-analysis of VAS score from 1 to 7 days of follow-up.  
### Synthesis of Results  
According to the SORT recommendation, the evidence revealed that clear aligners produce less pain experience than fixed appliances, based on consistent findings of at least two good-quality meta-analyses that obtained significant results (SORT A) [30].
## 2 System Design and Analysis  
### Circuit Model Analysis  
Figure 1 shows the circuit model of a two-coil SS-compensated WPT system in which \(L_{p}\) (\(L_{s}\)) and \(M_{\text{ps}}\) are primary (secondary) self-inductance and mutual inductance, respectively. \(R_{p}\) stands for the equivalent resistance composed of the coil resistance and equivalent series resistance (ESR) of the compensation capacitor \(C_{p}\). The operating angular frequency \(\omega\) is determined by the voltage-fed power source \(u_{p}\), and the natural resonant angular frequency of the WPT system is given by (1) and (2).  
\[\omega_{p}^{2}L_{p}C_{p}=1 \tag{1}\]  
\[\omega_{s}^{2}L_{s}C_{s}=1 \tag{2}\]  
\[\left(R_{p}+\mathrm{j}X_{p}\right)\dot{I}_{p}+\mathrm{j}\omega M_{ps}\dot{I}_ {s}=\dot{U}_{p} \tag{3}\]  
\[\mathrm{j}\omega M_{ps}\dot{I}_{p}+\left(R_{s}+R_{L}+\mathrm{j}X_{s}\right) \dot{I}_{s}=0 \tag{4}\]  
After implementing KVL on both sides, the system can be expressed with (3) and (4), where \(\dot{U}_{p}\), \(\dot{I}_{p}\) and \(\dot{I}_{s}\) are phasors of fundamental input ac voltage, with current passing through primary and secondary coils. \(X_{p}\) and \(X_{s}\) refer to the reactance of resonators on both sides, respectively.  
If the system-operating-angular frequency is set to \(\omega_{p}\), then \(X_{p}\) will be eliminated. The relation between \(\dot{U}_{p}\) and \(\dot{I}_{p}\) is given in (5), where the second term on the right hand of the equation is the reflected impedance brought by the secondary side. It should be noted that (5) also expressed the value of input impedance \(Z_{in}\), whose real and imaginary parts are provided in (6) and (7). The value of \(R_{s}\) is omitted because it is usually negligible compared to \(R_{L}\).  
\[\frac{\dot{U}_{p}}{\dot{I}_{p}}=R_{p}+\frac{\omega_{p}^{2}M_{ps}^{2}}{R_{L}+R_ {s}+\mathrm{j}X_{s}} \tag{5}\]  
\[\mathrm{Re}[Z_{in}]=R_{p}+\frac{\omega_{p}^{2}M_{ps}^{2}R_{L}}{R_{L}^{2}+X_{ s}^{2}} \tag{6}\]  
\[\mathrm{Im}[Z_{in}]=-\frac{\omega_{p}^{2}M_{ps}^{2}X_{s}}{R_{L}^{2}+X_{s}^{2}} \tag{7}\]  
\[\begin{cases}Re[Z_{in}]=f\big{(}R_{L},M_{ps},L_{s},C_{s}\big{)}\\ Im[Z_{in}]=g\big{(}R_{L},M_{ps},L_{s},C_{s}\big{)}\end{cases} \tag{8}\]  
Normally, the primary-side \(\omega_{p}\), \(R_{p}\) and \(\dot{U}_{p}\) are known parameters, and then the input impedance \(Z_{in}\) can be expressed as a function with \(M_{\text{ps}}\), \(L_{s}\), \(C_{s}\), and \(R_{L}\) being variables, as shown in (8). In that case, as long as the root-mean-square (RMS) value of \(\dot{I}_{p}\) and its phase relative to the input voltage under different \(\omega_{p}\) are obtained, multiple equations  
Figure 1: Circuit model of SS-WPT system.  
of Formula (8) can be deduced. Since the equation cannot be explicitly expressed, it is transformed into the solution of an optimization problem.
# Graphene Deposition on Glass Fibers by Triboelectrification
## 2 Experimental Part
### Experimental Techniques  
The morphological analyses of received GFs and rGO coated glass fibers were performed by field emission scanning electron microscopy (FESEM) using a Zeiss Supra 40 microscope (Berlin, Germany). Prior to this analysis, the samples were coated by a Pt/Pd alloy coating (80:20) to make them conductive.  
Epoxy microdroplets (having an average length of 477.1 \(\pm\) 62.5 \(\upmu\)m) were deposited onto single fiber filaments of as received GFs and rGO coated fibers glued on a paper frame. The size of microdroplets was measured by a Nikon SMZ25 optical microscope equipped with a Nikon DS-Fi2 digital camera. The deposited epoxy microdroplets were left at room temperature for 24 h, then cured at 60 \({}^{\circ}\)C for 15 h. Micro debonding tests were conducted at a crosshead speed of 1 mm/min by an Instron(r) 4502 universal testing machine (Norwood, MA, USA), equipped with a 2.5 N load cell. During these tests, the paper tab attached to one end of the glass fiber was slowly pulled up, while the droplet was constrained by a shearing plate fixed on a stationary support. The interfacial shear strength (IFSS) was calculated according to Equation (1):  
\[\text{IFSS}=\ \frac{\text{F}_{\text{max}}}{\uppi\text{dL}} \tag{1}\]  
where \(\text{F}_{\text{max}}\) is the maximum load detected from the load-displacement curve, d is the fiber diameter, and L is the length of the microdroplet.  
The volumetric resistivity of rGO coated glass fibers was evaluated by using a four-point probe setup according to Equation (2):  
\[\rho_{\text{V}}=\ \frac{\text{V}\times\text{w}\times\text{t}}{\text{I}\times \text{L}} \tag{2}\]  
where V is the measured voltage, w and t are the width and thickness of the samples, I is the measured current, and L is a fixed distance.
## 1 Introduction
Due to the sparsity and noise in the intensity and depth maps acquired by the ToF sensor, we choose to use CS algorithms to process these maps for their good denoising performance and high resolution at low sampling rates. Though CS provides an alternative choice for improving the performance of CGI by its various modified image reconstruction algorithms, its application is more or less limited by the assumption of strong sparsity and the reconstruction process [52; 53; 54]. The emergence of deep learning (DL) [55; 56; 57; 58] provides an opportunity to eliminate the constraint of sparsity to recover the images by data trained and untrained strategies at an ultralow sampling rate (SR) [58; 59; 60; 61; 62]. Meanwhile, a variety of novel computational 3D imaging techniques, including those based on the ToF and SPI, all gave a promising potential applications in different imaging fields [12; 63; 64; 65; 66; 67; 68]. In this paper, in a single-pixel imaging (SPI) experiment based on structure detection, a ToF camera is used as a bucket detector, and different algorithms are used to reconstruct the likeness of the target object based on the intensity information from the ToF sensor, and compare it with the original image output from the ToF camera (ToF image). It is found that the scheme based on TVAL3 algorithm and untrained DL can obtain higher quality images at a very low sampling rate. In addition, we have experimented with ToF cameras by scattering media, and the SPI-based CS and DL methods can recover images clearly, while the normal ToF methods cannot. We believe that the ToF camera may be suitable for multi-noise and complex environments, such as scattering media or underwater in similar schemes.
## 4 Experimental Results
### Cerber  
#### 4.3.1 Default Environment Outcome  
Upon extracting the Cerber ransomware in the default environment, Windows Defender instantly sprang into action even if the user tried to execute Cerber before Windows Defender took action. The attached appendices (Figures S5 and S6) provide all the screenshots associated with these actions. The Windows log also indicates the Windows Defender behaviors in detecting the Cerber ransomware. Figure S7 shows the logs of Windows Defender when it detected the Cerber ransomware. The subsequent section outlines the main observations from testing and executing the Cerber ransomware variant on the Windows 11 Machine.  
Key Observations:  
Origin: The threat was detected on the test machine, meaning the antivirus did not rely on cloud-based checks for this detection.  
Type: The detection type was \"Concrete\", which suggests that the antivirus software was certain about the malicious nature of the file.  
Source: The detection source being \"System\" means the system processes or the operating system itself flagged it.  
User: The threat was executed or encountered under the system authority, which means it might have had elevated permissions.  
Process Name: The process that initiated or was infected by the ransomware is not known, as per the log.  
Action Taken: The Windows Defender antivirus took the action to \"Quarantine\" the file, effectively isolating it to prevent any harm. No further actions were required from the user's end as the threat was neutralized. The operation was successful, as indicated by the error description.  
While the threat was automatically quarantined, a potential improvement could be allowing the notification to await user acknowledgment, ensuring they are informed about the detected threat. Currently, the notification is temporary and might be missed by the user.  
The Windows Defender protection history offers insights, but we must ensure users can easily access this information. It is essential to prioritize user experience while maintaining stringent security.
# Linear Precoding Designs for MIMO VLC Using Multi-Color LEDs under Multiple Lighting Constraints
## 1 Introduction
However, they only took a single multi-color LED as the transmitter and also fixed the CCT values. As has been standardized in [22], the chromaticity difference in the region called the MacAdam ellipse on the CIE1931 chromaticity diagram could be ignored. Then, to meet the needs of LED products, chromaticity tolerances were even modified to be limited in quadrangle regions [23]. Therefore, the performance of multi-color VLC systems is able to be improved by considering the quadrangle chromaticity regions instead of fixed CCT values. In this paper, different from the existing methods, we employ multi-color LEDs in MIMO VLC to improve the data transmission rate and consider the multi-color crosstalk and chromaticity tolerance regions to improve the system adaptivity for practical illumination scenarios. We propose a linear precoding design to minimize the MSE of the received signal under power, amplitude and chromaticity constraints. With the precoding design transformed into a convex optimization problem, an iterative algorithm is designed to derive the optimal precoder. The main contributions of this paper are summarized as follows:  
* We construct the MIMO VLC system model based on multi-color LEDs and formulate the precoding design problem to minimize the total MSE under multiple lighting constraints. * Unlike the conventional multi-color precoding method designed for fixed CCT values, our proposed method is optimized under the chromaticity constraint in a quadrangle range. * Considering the multi-color interference, our proposed MSE minimization algorithm provides an effective method to get the optimal precoder by updating optimization variables iteratively. * The transmission data rate of traditional white light MIMO VLC systems is improved by the utilization of multi-color LEDs, and our proposed method would be applicable in high speed broadband optical wireless communication. The remainder of this paper is laid out as follows: Section 2 mainly introduces the MIMO VLC system model. Section 3 indicates the main optimization problem and lighting constraints. In Section 4, we propose an iterative algorithm to solve the optimization and analyze the complexity. Simulation results are demonstrated in Section 5, and conclusions are drawn out in Section 6.
## 3 Risk Identification during Navigation
### Ship Position Monitoring Method  
Ships shall navigate on the centerline as much as possible when navigating within the channel of TSS to avoid navigation risks, such as grounding, hitting rocks, and sailing out of the traffic lane due to anti-collision or human negligence. As a result, the track control boundary line is established in the traffic lane as the ship's safe navigation zone in this study. A yaw warning is delivered when the ship departs the region, reminding the ship navigators to alter the course in a timely manner.  
Set \(DIS\) as the distance from the track control boundary line to the real traffic lane boundary line. Because the traffic lane in the Chengshantou TSS waters is very wide and the ship in the study is large, \(DIS=2L\) is used in the study. Figure 5 displays the ship location monitoring method.  
When \(DIS_{S}<DIS\) or \(DIS_{P}<DIS\), a yaw warning will be issued to remind the navigator to adjust the course. Equation (18) shows the \(DIS_{S}\) and \(DIS_{P}\) computation methodology.  
\[\left\{\begin{aligned} & DIS_{S}=\frac{|px-y+Y_{\text{T}_{\text{BH}}}-pX_{ \text{T}_{\text{BH}}}|}{\sqrt{1+p^{2}}}\\ & DIS_{P}=\frac{|px-y+Y_{\text{T}_{\text{BH}}}+1-pX_{\text{T}_{ \text{BH}}}+1|}{\sqrt{1+p^{2}}}\end{aligned}\right. \tag{18}\]  
where \(p\) is the slope of the traffic lane boundary.
## 3 Methodologies and Research Framework
### False Positive Reduction  
One of the main potential issues is false positive detection. This means that, even though the object is not a vehicle, the system detects it either in or out of a blind spot through the sliding window technique. To minimize this issue, two concepts, namely heat map and thresholding, are used. The fundamental concept of the heat map is to visualize an image by representing each pixel by the color corresponding to its magnitude [39; 40]. Assume that an image, \(x=\{x_{p}\}\), and a classification function, \(f(x)\), are given where \(p\) denotes a pixel. Here, the function, \(f(x)\), is used for indicating the presence of an object in the image and can be learned very well by a deep neural network [40].  
Based on this, the heat map is denoted as:  
\[h=\{h_{p}\}=\mathcal{H}(x,f,p) \tag{2}\]  
where \(\mathcal{H}\) is a function derived from a class discriminant \(f\). Note that a value is assigned to each pixel, \(p\), by \(\mathcal{H}\). In addition, \(h\) is visualized as an image since its dimensionality is same as \(x\)[40].  
Thresholding refers to the technique of image segmentation, and it is denoted as:  
\[T=T[x,y,p(x,y),f(x,y)] \tag{3}\]  
where \(T\) is the threshold value, \(x\) and \(y\) are the coordinates of the threshold value, \(p(x,y)\) is points in gray-level image pixels, and \(f(x,y)\) is gray-level image pixels [41; 42]. There are two main thresholding techniques: global and local. The major difference is that global thresholding applies one threshold value to the whole image, while local thresholding applies different threshold values to different areas of the image [41]. Among these two techniques, the simpler one is global thresholding, which was adopted in this research, and denoted as:  
\[g(x,y)=\begin{cases}0&\text{if }f(x,y)\leq T\\ 1&\text{if }f(x,y)>T\end{cases} \tag{4}\]  
where \(T\) is the threshold value, and \(g(x,y)\) = 0 or 1 means an object and background, respectively [41; 42]. Note that \(f(x,y)\) is replaced with the heat map, \(h_{p}\), and we initially set 10 to the value of thresholding, \(T\). The main objective of both concepts is to maximize a probability that the detected (target) object is a vehicle.  
Figure 8: Blind spot setting zone.  
As mentioned in the previous section, the size of a rectangle box is also used for false positive reduction. Assume that vehicles are seen in a blind spot, but they are far away from a blind spot. In this case, vehicles are seen smaller than presenting in a blind spot. The system recognizes them as vehicles but does not need to detect them. For this reason, if a rectangle box given by the heat map is smaller than a particular value, the system rejects it and the false positives can be reduced.  
According to all the methodologies described above, the entire research framework is depicted in Figure 9.