esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Classification of Optical Waveguide Sensors
### MZI-Structured Optical Waveguide Sensor
#### 3.2.3 Lithium Niobate Compound MZI Optical Waveguide Sensor  
Similar to SOI, LNOI has a high refractive index contrast, which strongly confines light in the micro/nano level photonic structure. From the manufacturing and application perspectives of photonic devices, it can be shown that LNOI has a transparent window in the range of visible and mid-infrared wavelengths, which makes LNOI waveguides suitable for biochemical photonics and nonlinear optical applications. Lithium niobate is transparent in the visible and mid-infrared wavelength ranges, which makes LNOI waveguides suitable for biochemical photonics [53].  
Waveguide-based integrated evanescent field sensors enable the interaction between evanescent waves and the surrounding matter to detect the concentration of chemical or biochemical analytes.  
[PERSON] et al. [105] proposed an optofluidic device realized in monolithic lithium niobate by combining an optical waveguide with a microfluidic channel. The channel was  
Figure 18: Sensing device with microfluidic circuit and MZI.  
realized via thermal diffusion of thin strips of titanium on LN. In addition, the microfluidic channels were mechanically engraved on the same substrate using a self-polishing saw. The proposed optofluidic device was used for pH optical sensing of the analyte solutions. The detection limit was 1.58 \(\pm\) 0.01 uM. The results showed an almost 18-fold improvement compared with polymer waveguide systems. As the Ti:LN waveguide can guide light beams of different wavelengths, the sensor can be used with pH indicators other than bromothymol blue, which increases the pH range and broadens the application range of the device. Furthermore, this device provides real-time monitoring required for a feedback system. By integrating the device with a flow controller, a solution with the desired pH can be easily obtained.  
[PERSON] et al. [106] proposed a single-mode porous silicon-integrated MZI waveguide sensor. Using the MZI-structure optical waveguide biochemical sensor to quantify the detection of isopropanol, it was shown that the exposed area would produce a large measurable phase shift of 9.7\(\upmu\). The measured sensitivity of the device was as high as 13,000 rad/RIU cm, and it can detect refractive index changes as small as 10\({}^{-5}\). The integrated interferometer exhibited a more stable response than the fiber-based sensing approach.
## 2 Materials and Methods
### Mesh Generation  
Surface meshes (Figure 2D left) are generated via the Python library _PyMeshLab_ that in turn interfaces with the popular open-source application MeshLab [25], using as input a cloud of 3D points coming from the contours computed by the shape interpolation algorithm. To enhance the robustness of the algorithm (e.g., avoiding over-sensitivity of convergence due to a wrong ratio of the number of pixels per layer and number of intermediate interpolations), the cloud points are randomly sampled following the Poisson Disk sampling method [26], ensuring homogeneous spatial distribution. This algorithm takes as inputs an estimation of the number of points sampled, which is defined as a fraction of the number of points in the original cloud.  
Then, the algorithm computes the normals for the sampled point cloud and finally generates a closed surface based on these normals using the Screened Poisson surface reconstruction method [27]. To improve the quality of the volumetric mesh to be generated afterwards, a decimating filter is added after the surface reconstruction. This filter is based on the quadric based edge-collapsed strategy [28]. Additionally, the generated surface can be smoothed (user-defined option) to simplify complex geometries that could be problematic for the subsequent volumetric meshing. We employ the HC Laplacian Smoothing [29] algorithm for this purpose.  
Three-dimensional meshes (Figure 2D middle) are then generated from the surface files (.stl) using the open-source library _Gmsh_[30] which provides great flexibility.  
Figure 1: The _im2 mesh_ workflow. Image files of multiple formats containing the segmentation masks are accepted as input for the mesh generation. For every pair of layers, starting from the bottom, shapes are interpolated using any number of intermediate positions. Afterwards, the contours of these shapes are computed and stored for later stages. When the interpolation is complete, a surface mesh with the desired number of faces is created (.stl) and further processed to obtain a volumetric 3D mesh in which scalar data is automatically interpolated if available (optional). Useful information (e.g., element labels, connectivity, coordinates, etc.) and already formatted mesh files are exported for further analysis.
## 5 Results and Discussion
### Ring Shear Results
#### 5.3.2 Effect of Contamination on Cohesion and Friction Angle  
Figure 9 shows the effect of the atrazine contents on the friction angle (\(\$\)) and cohesion (\(\mathrm{C}\)) of the AC, BC and KC specimens. As shown in Figure 9a, increasing the atrazine contents of the specimens caused a reduction in friction angle in all cases. For instance, the friction angle of the uncontaminated AC specimens was 27.32\({}^{\circ}\), while this value decreased to 25.32\({}^{\circ}\) and 21.70\({}^{\circ}\) when 4% and 6% atrazine were added to the specimens. An identical trend was observed for BC and KC specimens. For instance, the friction angle was in the range of 23.40\({}^{\circ}\)\(\leq\$\)\(\leq\) 25.10\({}^{\circ}\) and 22.40\({}^{\circ}\)\(\leq\$\)\(\leq\) 24.30\({}^{\circ}\) for BC and KC specimens with an uncontaminated friction angle value of 28.80\({}^{\circ}\) and 26.80\({}^{\circ}\) respectively. Conversely, increasing the atrazine content showed an increasing trend on cohesion of the tested specimens. As can be seen from Figure 9b, a cohesion value of 6.4, 8.6 and 4.7 kPa for uncontaminated AC, BC and KC specimens were recorded while this value increased to 7.8 \(\leq\)\(\mathrm{C}\)\(\leq\) 9.5 kPa, 10.2 \(\leq\)\(\mathrm{C}\)\(\leq\) 12.6 kPa, and 6.3 \(\leq\)\(\mathrm{C}\)\(\leq\) 7.2 kPa when 4% to 6% atrazine was added to the AC, BC and KC specimens respectively. The reported results in this section has a good agreement with reported results by ([PERSON] and [PERSON], 2018). Increasing the viscosity of the clays caused a lower surface tension of the clay particles ([PERSON] and [PERSON], 2018; 2018) lower values of the friction angle, and greater values of cohesion at higher percentages of ATZ.
## 2 Materials and Methods
### Measurement and Calculation of N\({}_{2}\)O Fluxes  
Fluxes of N\({}_{2}\)O were measured using a closed chamber method. A PVC collar (0.65 \(\times\) 0.65 m) was inserted to 0.10 m depth in all plots (\(n\) = 24 for both sites) on 27 September (five days after planting of wheat). The collars remained intact throughout the measurement periods. Fluxes were generally measured at two-week intervals until the harvest of winter wheat.  
The collars had a 0.04 m wide outer rim that was aboveground and parallel to the soil surface to support the top chamber used for flux measurement. Fluxes were measured by placing an opaque (white color) chamber (0.70 \(\times\) 0.70 \(\times\) 0.41 m\({}^{3}\)) on the collars. Chamber headspace air was continuously mixed by a small battery-driven fan. Concentrations of N\({}_{2}\)O and CO\({}_{2}\) was determined by a portable Fourier transform infrared (FTIR) based analyzer (DX4040; Gasmet Technology Oy, Helsinki, Finland) that was connected to the chamber through 3 mm inlet and outlet tubing. The chamber was enclosed for 8 min during each measurement and concentrations were recorded at 40 s intervals.  
Fluxes were calculated by linear regression using the MATLAB(r) (MathWorks, Inc., Natick, MA, USA) routine of [PERSON] et al. [20]. The first few records after chamber enclosure were discarded as dead-band based on visual inspection of the CO\({}_{2}\) concentrations recorded simultaneously with N\({}_{2}\)O concentrations. Fluxes of N\({}_{2}\)O were assumed to be zero when probability of linear regression was insignificant (\(p>0.05\)).
# Neurofeedback Effects on EEG Connectivity among Children with Reading Disorders: I. Coherence
## 2 Materials and Methods
### Statistical Analysis
#### 3.2.3 Functional Connectivity Changes: Intrahemispheric Coherence  
In Figure 6, the results of intrahemispheric coherence in both hemispheres for the NFB group can be observed. In the left hemisphere, P3-O1 coherence in the alpha frequency band increased significantly (t = 3.2083, \(p\) = 0.0310, d = 0.527). In the theta frequency band, the coherence values decreased significantly in F3-P3 (t = 3.6818, \(p\) = 0.0130, d = 0.876), F3-01 (t = 3.3866, \(p\) = 0.0236, d = 0.692), F3-T3 (t = 3.8031, \(p\) = 0.0096, d = 0.550), F3-T5 (t = 3.8038, \(p\) = 0.0096, d = 0.870), and C3-P3 (t = 4.9384, \(p\) = 0.0006, d = 0.774); in the delta and beta bands, no significant changes occurred in the intrahemispheric coherence of the left hemisphere. In the right hemisphere, the delta coherence C4-T4 decreased (t = 3.2628, \(p\) = 0.0366, d = 0.770), and the theta coherence decreased in FP2-F8 (t = 3.1543, \(p\) = 0.0424, d = 0.802) and F4-C4 (t = 3.7558, \(p\) = 0.0126, d = 0.693). In the beta band, the coherence values decreased in FP2-F4 (t = 3.0484, \(p\) = 0.0476, d = 0.979), FP2-F8 (t = 3.1789, \(p\) = 0.0378, d = 0.818) and P4-T4 (t = 3.1180, \(p\) = 0.0416, d = 0.844). No significant changes were observed in the alpha band in the right hemisphere.  
In contrast, in the Sham group, only the F7-T3 intrahemispheric coherence in the theta band was reduced significantly (t = 7.3478, \(p\) = 0.0298, d = 0.230).  
Figure 6: Significant differences between before and after treatment in intrahemispheric coherence between homologous pairs in the NFB group. (**Top**): Bar graphs showing the significant changes in coherence z values by frequency band (indicated at the **top**) when comparing intrahemispheric connectivity before vs. after NFB treatment; the left or right hemisphere is indicated below the graph. (**Bottom**): topographic representation of significant changes in z-coherence after NFB treatment, showing the increase or decrease in red or blue, respectively.
## 2 Results and Discussion  
### LC-UV Method Validation  
In this work, the main aim was to develop a chromatographic method able to separate the various cannabinoids. Since THC/CBD and THCA/CBDA are isomers with similar UV spectra (see panels in Figure 1), their identification is only possible depending on their retention time. The separation of the compounds under investigation was carried out on core-shell column in reverse phase mode, with good results in terms of analytes retention, peak shape, and resolution power [6]. The optimized gradient elution allowed for a good separation of cannabinoids within 16.0 min of chromatographic analysis. In detail, CBD elutes (8.9 min) after its acidic precursor CBDA (8.2 min) because of its higher lipophilicity. The same could be said about the acid precursor THCA (15.7 min) compared to THC (13.1 min). CBN elutes (11.7 min) after CBD, due to higher lipophilicity of the additional pyran ring, but before THC because of the higher polarity of the aromatic ring compared to the cyclohexane occurring in the THC molecule. THC and THCA elute following CBD and CBN due to the presence of the dihydropyran ring and the simultaneous absence of a free hydroxyl group leading to higher lipophilia. Figure 1 reports the chromatographic profile of a cannabinoid standard mix at 1 mg/mL used to evaluate the reliability of the chromatographic method.  
Method validation results are reported in Table 1. In the analyzed concentration range, the linearity was good for all the analytical standards of the cannabinoids studied, being R\({}^{2}\) > 0.9998. The instrumental LOD and LOQ were determined by the calibration curve, based on the formulas expressed in Section 4.4, and ranged between 0.05 and 0.08 mg/L and 0.15 and 0.25 mg/L, respectively. Compared to literature [22], lower LOD and LOQ values were found for all the cannabinoids under study, confirming the sensitivity of the developed method. Repeatability and intermediate precision results (Table 1) demonstrate  
Figure 1: HPLC-UV chromatographic profile of a cannabinoid standard mixture at 1 mg/mL. The observed retention times were 8.2 min for CBDA, 8.9 min for CBD, 11.7 min for CBN, 13.1 min for THC, and 15.7 for THCA. The UV detector was set at 220 nm for THCA, CBDA, and CBN detection and at 210 nm for THC and CBD detection.  
a very high precision of the method over the linearity range. In fact, the %RSD varied from 0.04 to 1.51% for repeatability and from 0.31 to 3.49% for intermediate precision at 1 mg/mL. Additionally, retention times precision resulted very good, with %RSD less than 1.51%. As shown in Table 1, the percentage of recovery values was higher than 87.2%, thus proving the accuracy of the method was similar to those previously reported in literature [22].
# Survey on Power-Aware Optimization Solutions for MANETs
## 2 Related Work  
Several studies, as shown in Table 1, have analyzed the existing energy-efficient routing schemes for MANETs. Some surveys have focused only on PHY layer methods for energy-efficient wireless communication. For instance, [PERSON] et al. [52] reviewed only PHY layer techniques for energy-efficient wireless communication, such as orthogonal frequency division multiplexing (OFDM) and multiple-input multiple-output (MIMO), cognitive radio (CR), network coding, cooperative communication, etc. [PERSON] et al. [53] surveyed and analyzed energy-efficient routing protocols for Wireless Sensor Networks (WSNs). [PERSON] and [PERSON] [54] discussed the design challenges of routing protocols for Wireless Multimedia Sensor Networks (WMSNs) and classified current techniques with their limitations. [PERSON] et al. [55] considered diverse routing schemes, investigating the benefits of multi-antenna assisted relay nodes, the number of MAC retransmissions, and the number of hops on the performance energy consumption. [PERSON] [26] summarized state-of-the-art solutions on QoS routing and resource reservation mechanisms to provide multimedia communication over MANETs. The author considered the limitations of existing QoS models concerning satisfying QoS in serving multimedia over MANET. [PERSON] et al. [20] discussed the challenging factors in MANETs by highlighting issues in power-based routing metrics. They classified existing power-efficient routing algorithms in MANETs into six categories and compared the routing techniques of each category using their merits and limitations. [PERSON] et al. [23] exposed the critical view on why the Host-Centric Networking (HCN)-based MANET is not energy-efficient by indicating the incompatibility of the HCN paradigm with the MANET itself. They recommended a new solution for improving energy efficiency in MANETs by shifting from the HCN paradigm to the Information-Centric Networking (ICN) paradigm. [PERSON] [56] presented various types of scheduling techniques for MANETs. The author also analyzed load-based queue scheduling techniques and presented various cross-layer schedulers for power control. Most of the presented techniques are related to power-efficient routing. [PERSON] et al. [57] presented and evaluated energy-based clustering algorithms for cluster-based MANETs. Such as, clustering algorithms electing a node as a Cluster-head (CH), based on its node energy level. A CH performs cluster management and this activity causes too much energy consumption, which affects network performance. Consequently, the energy constraint of a node is a vital parameter for electing a CH because it directly impacts the overall lifetime of the network. Finally, [PERSON] and [PERSON] [58] presented cross-layer solutions for WSNs.
# Improved Patch Packing and Refining Segmentation for the V-PCC Standard
## 4 Experimental Results  
This paper aims to reduce time consumption while improving RD performance and compares the proposed algorithm with TMC2-v18.0 to demonstrate its advantages. We also compare with methods from [25; 26; 35]. Generally, the bit rates generated by various methods do not match perfectly. The Bjontegaard-Delta rate (BD-rate) [36] is used for a fair comparison, allowing the comparison of two coding solutions while considering multiple RD points. We report D1 (point-to-point) PSNR and D2 (point-to-plane) PSNR as geometric distortions [37]. For attributes, the distortions of Luma PSNR, Cb PSNR, and Cr PSNR components are given. The PSNR is calculated using the mean squared error (MSE) [38]. In order to better evaluate performance, we have defined additional evaluation metrics for testing in the refinement segmentation and patch packaging stages.
# The Influence of Binaural Room Impulse Responses on Externalization in Virtual Reality Scenarios
## 3 Experimental Results
### Results for Experiment I  
Figure 4 shows the externalization results for the desktop-based experiment using box plots. Each panel shows the results for a different source direction (left: 0\({}^{\circ}\); right: 45\({}^{\circ}\)). It can be seen that the externalization ratings of lateral sources are generally higher than those of frontal sources. For each source direction, the results affected significantly by different BRIR types (Friedman test: \(p\ll 0.05\)). The ratings for the condition \"BRIR-C\" are significantly lower than for the other two conditions (a series of Wilcoxon tests: \(p<0.04\)). Overall, subjects rated the externalization higher for the \"BRIR-A\" condition than for the other two conditions, consistent with the theory of the RDE [7; 8; 9]. However, only a weak enhancement of externalization is observed for the condition \"BRIR-A\" compared to the condition \"BRIR-B\" (Wilcoxon tests: \(p\approx 0.06\) for both directions). Nevertheless, this result suggests that matching the room acoustics between the binaural signals and the real listening environment is important for externalization.
## 2 Related Works
### Object Detection Model  
The latest object detection models are commonly sorted into two types: one-stage detector and two-stage detector. For applications, where faster inference is required, typically a one-stage detector which directly predicts bounding boxes (bbox) and classes is used. On the contrary, the two-stage model first extracts region proposals, before predicting the exact bboxes and the corresponding classes to those locations. Since we aim for real-time performance, a one-stage detector will be chosen.  
As for the one-stage model, anchor-based and anchor-free detectors are frequently studied. The anchor box is a predefined bbox of a certain height and width. Among anchor-free detection methods, keypoint detection [5, 35, 36] utilizes a center, or a corner point, without using any predefined anchors. An alternative way is to detect the objects with pixel-wise prediction [37].  
In anchor-based detectors, the bounding boxes are defined to capture the scale and aspect ratio of target object classes. During detection, the predefined anchor boxes are titled across the image. The most popular anchor-based network are the various versions of YOLO [17, 18, 38].  
YOLOv3 [17] makes the prediction at three different scales based on feature pyramid networks structure [39]. Additionally, it adopts up-sampling layers concatenated with the previous layers to preserve the detailed features. Moreover, this algorithm uses more anchor boxes, logistic regression for label prediction, multi-label classification, and deeper backbone model Darknet-53.  
To develop a more efficient network, the YOLOv4 has been released by applying \"Bag of freebies\" and \"Bag of specials\". Bag of freebies are the training strategies that only change the cost in the training procedure and Bag of specials includes plugin modules or post-processing methods that only increase the inference cost for higher improvement in the detection accuracy. Despite the advantages of YOLOv4, we employ YOLOv3 in this study because it was used to lay the groundwork that is extended here from prior work. Moreover, many functionalities of the \"Bag of freebies\" and \"Bag of specials\" of YOLOv4 can be implemented in this framework as well.  
In this paper, we exploit the concatenation layer which can capture the spatial information from the shallower layer, and semantic features from the deeper layer which improves the detection performance of small objects [13, 40]. Therefore, we suggest additional concatenation modules with three variants based on YOLOv3-SPP for extracting more meaningful information for small objects in fisheye images [41]. Figure 4 presents the structure of the baseline and each concatenated model.  
Figure 4: Different feature concatenation strategies. (**a**) YOLOv3 with Spatial Pyramid Pooling module. (**b**) Short-skip Concatenation. (**c**) Long-skip Concatenation. (**d**) Short-Long-skip Concatenation.
# Characterization of Dynamic Blockage Probability in Industrial Millimeter Wave 5G Deployments
## 5 Blockage in Industrial Deployments
### BS to UE Communications
#### 5.3.1 Random Machines' Height  
Similarly to the UE to UE communications, we can consider the occlusion of the LoS path created by lines over \(x\)- and \(y\)-axes independently. However we now need to take into account whether a machine is high enough to potentially block the LoS path. To this aim, consider Figure 4 illustrating the side view of the LoS path as seen from the \(x\)-axis perspective. We are interested in determining the height of the LoS path at coordinate \(x\) of this axis, \(f_{L,x}(x)\). By utilizing geometrical arguments we see that  
\[\tan\alpha=\frac{l_{x}}{h_{A}-h_{U}}=\frac{x}{h_{A}-y(x)}, \tag{3}\]  
leading to the following LoS height equation  
\[f_{L,x}(x)=h_{A}-\frac{(h_{A}-h_{U})x}{l_{x}}. \tag{4}\]  
Similarly, for the \(y\)-axis, we obtain  
\[f_{L,y}(y)=h_{A}-\frac{(h_{A}-h_{U})y}{l_{y}}. \tag{5}\]  
At certain \(x\)- and \(y\)-coordinates, the probabilities that the LoS path can be occluded by a machine coincide with the probabilities that the machine height is higher than the LoS path and are given by  
\[p_{B}(x)=1-F_{H_{B}}\Big{(}h_{A}-\frac{(h_{A}-h_{U})x}{l_{x}} \Big{)},\] \[p_{B}(y)=1-F_{H_{B}}\Big{(}h_{A}-\frac{(h_{A}-h_{U})y}{l_{y}} \Big{)}, \tag{6}\]  
where \(F_{H_{B}}(x)\) is CDF of machine height.  
The mean intensity of lines over \(x\)- and \(y\)-axes inducing machines capable of occluding the LoS paths are given by  
\[\Lambda_{x}=\int_{0}^{l_{x}}\lambda_{x}\Big{(}1-F_{H_{B}}\Big{(}h_ {A}-\frac{(h_{A}-h_{U})x}{l_{x}}\Big{)}\Big{)}dx,\] \[\Lambda_{y}=\int_{0}^{l_{y}}\lambda_{y}\Big{(}1-F_{H_{B}}\Big{(}h _{A}-\frac{(h_{A}-h_{U})y}{l_{y}}\Big{)}\Big{)}dx. \tag{7}\]  
Recall that the thinning procedure for Poisson processes also holds when the thinning probability is varying. By applying it and also utilizing the independence property of MPLP, we arrive at the final result for the blockage probability in the following form  
\[p_{B}(l_{x},l_{y})=e^{-\kappa v(\Lambda_{x}l_{x}+\Lambda_{y}l_{y})}. \tag{8}\]