esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Experiments
### Experimental Setup
In fact, the execution time of DOA methods using FP64 was shown to be about 20 times slower than ones using FP32 [34]. To measure the energy consumption, we connected the Otti Arc (power measurement tool) to the nRF52840, and to measure the execution time, we utilized the Saleae logic analyzer. However, in both measurements we needed to activate a General-Purpose Input/Output (GPIO) port, thus a GPIO was set high and low before and after the execution of the algorithm. So, we could check when the method started and finished to properly carry out the two measurements. Thus, energy usage is slightly overestimated. Additionally, we measured the stack memory consumption and the relative execution time of the principal operations in the implemented solution. There is no dynamic memory usage. We define the relative execution time as the running time of an operation divided by the execution time of the implemented method in percentage. Moreover, we calculated the Root Mean Squared Error (RMSE) of accuracy considering a 500 azimuth-elevation pair for each SNR. In mathematical terms, the RMSE of accuracy is defined in Equation (52),  
\[RMSE=\sqrt{\left(\frac{1}{L}\right)\sum_{i=1}^{L}((\theta_{i}-\hat{\theta}_{i} )^{2}+(\phi_{i}-\hat{\phi}_{i})^{2})}, \tag{52}\] \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**SoC** & **Processor** & **Flash** & **RAM** & **Does It Have** \\  & & **Memory** & & **FPU?** \\ \hline nRF52833 & \begin{tabular}{c} ARM Cortex M4 \\ 64 MHz \\ \end{tabular} & 512 KB & 128 KB & Yes \\ \hline nRF52811 & \begin{tabular}{c} ARM Cortex M4 \\ 64 MHz \\ \end{tabular} & 192 KB & 24 KB & No \\ \hline nRF52820 & \begin{tabular}{c} ARM Cortex M4 \\ 64 MHz \\ \end{tabular} & 256 KB & 32 KB & No \\ \hline nRF5340 & \
\begin{tabular}{c} ARM Cortex-M33 \\ 128/64 MHz \\ \end{tabular} & 1 MB & 512 KB & Yes \\ \hline EFR32 BG24 &
\begin{tabular}{c} ARM Cortex M33 \\ 78 MHz \\ \end{tabular} & Up to 1536 KB & Up to 256 KB & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 2: SoCs with Bluetooth Direction Finding capability. where \(L=500\) is the number of estimated azimuth-elevation pair, and \(\theta\) and \(\hat{\theta}\) is the actual azimuth and its estimation in degrees, respectively. Similarly for the elevation (\(\phi\)). The SNR values were 5 dB, 10 dB, 15 dB, 20 dB, 25 dB, and 30 dB. In total, we analyzed 6000 different pairs.
## 4 NURBS Interpolation
### Feed-Rate Planning  
Feed-rate planning is the main influencing factor in interpolating the NC commands along the trajectory. For the optical polishing, to guarantee the desired runtime of trajectory, the specific method is displayed as follows:  
**Step 1:** considering that now there is no analytical solution to calculate the length of NURBS, the Simpson formula is used to obtain the estimation of the length through numerical iteration:  
\[p=\frac{up-low}{6}(f(low)+4f(mid)+f(up)) \tag{25}\]  
with:  
\[f(u)=\frac{ds}{du}=\sqrt{x^{\prime}(u)+y^{\prime}(u)+z^{\prime}(u)} \tag{26}\]where \(x^{\prime}(u),y^{\prime}(u),z^{\prime}(u)\) respectively denote the first-order derivatives of \(x(u),y(u),z(u)\), which are the one-dimensional curves of \(\mathbf{C}(u)\) along \(x,y,z\) axes. \(up\) and \(low\) denote the upper and lower boundaries of \(u\), \(mid=(up+low)/2\).  
**Step 2**: the length between the two points corresponding to the parameters \(up\) and \(low\) is calculated:  
\[l=\|\mathbf{C}(up)-\mathbf{C}(low)\| \tag{27}\]  
**Step 3**: the error between the aforementioned two lengths is calculated:  
\[e=|p-l| \tag{28}\]  
The convergence threshold \([e]\) is given. If \(e>[e]\), let \(up=mid\) and repeat Steps 1 and 2. If \(e<[e]\), turn to Step 4.  
**Step 4**: the parameter interval \((0,1)\) was sectioned by the equivalent distance \(up-low\) to generate the knot vector \((u_{0},u_{1},\dots,u_{n})\). Thus, the length of the NURBS is:  
\[s\approx\sum_{i=1}^{n}\|\mathbf{C}(u_{i})-\mathbf{C}(u_{i-1})\| \tag{29}\]  
Equipped with the length at hand, the S-curve motion law is invoked to guarantee that the feed-rates of the adjacent NURBS segments are changed smoothly. Moreover, the initial sections of the trajectory segments are defined as the feed-rate transition zone. Then, the feed-rates remain constant until the end of the trajectory segments. In this case [33]:  
\[s=2v_{low}t_{a}+It_{a}^{3}+v_{up}t_{v} \tag{30}\]  
\[v_{up}=v_{low}+It_{a}^{2} \tag{31}\]  
where \(v_{low}\) and \(v_{up}\) denote the initial and final feed-rates of the trajectory segment. \(J\), \(2t_{a}\) and \(t_{v}\) denote the jerk, acceleration (deceleration) time and the uniform motion time, respectively. Thus, the runtime of the trajectory segment is \(t_{d}=2t_{a}+t_{v}\).  
It is worth noting that previous studies have shown that the feed-rates, when limited by the runtime of the trajectory segments, are much lower than the maximum value which is constrained by the chord error and the driving capacity. Therefore, there is no need to check the feed-rate again.
## 2 Materials and Methods
### Relaxometric Property Measurements  
The original aqueous nanoparticle suspension sample was diluted with triple-distilled water to prepare aqueous nanoparticle suspension samples with various Mn-concentrations (1.0, 0.5, 0.25, 0.125, and 0.0625 mM). The longitudinal (T1) and transverse (T2) water proton spin relaxation times were measured for these solutions using a 3.0 T MRI scanner (Magnetom Trio Tim, Siemens, Munchen, Bayern, Germany). An inversion recovery method was employed to obtain the T\({}_{1}\) relaxation times [29]. A multiple spin-echo method with a Carr-Purcell-Meiboom-Gill pulse sequence was employed to obtain the T\({}_{2}\) relaxation times [29]. Thereafter, 1/T\({}_{1}\) and 1/T\({}_{2}\) were plotted as a function of Mn-concentration to estimate the longitudinal (r\({}_{1}\)) and transverse (r\({}_{2}\)) water proton spin relaxivities from the slopes, respectively.
## 1 Introduction
In 1987, [PERSON] applied the \"Monte-Carlo Method\" to certain \"Two-Player Games\": tic-tac-toe, Othello and Chess, Ref. [16]. [PERSON] proposed the \"Expected-Outcome model\", based on the statistical data of \"_random_ game playouts to the end\", as the basis of strategies for a game player. [PERSON] considered the applicationsof the \"Monte-Carlo Method\" to the board game \"_Go_\" in 1993, but not seriously, Ref. [17]. In 2006, inspired by many predecessors, [PERSON] seriously considered the applications of the \"Monte-Carlo Method\" to the search tree for the board game \"_Go_\", Ref. [18]. See Ref. [19] for similar work by [PERSON] and [PERSON]. The decision-making algorithm proposed in Ref. [18] was named \"Monte-Carlo Tree Search (MCTS)\" by [PERSON]. More modifications and improvements, Refs. [20; 21; 22; 23; 24; 25; 26], were made for MCTS in light of the important contributions by [PERSON] and [PERSON], Ref. [19], and by [PERSON], Ref. [18]. The MCTS algorithm was also applied to certain games, Refs. [27; 28; 29; 30; 31]. By combining MCTS with _reinforcement learning_ based on CNNs (Convolutional Neural Networks), the computer Go player _AlphaGo_, developed by Google DeepMind, defeated [PERSON], [PERSON], one of the most brilliant Go players, in a five game Go match in 2016. See Refs. [32; 33] for further details. The following three pictures (Figures 1-3) were taken from Ref. [34], which can be found on the website of [PERSON]. We sincerely thank Professor [PERSON] for kindly granting us permission to use his explanatory pictures in this article. A figure for the board game _Go_ is shown in Figure 4.  
In 2017, Google DeepMind added a new ingredient \"_Dirichlet noise_\" to _AlphaGo Zero_ to encourage further exploration by MCTS, which could have been omitted by the earlier versions of _AlphaGo_ because of low prior probabilities. This deepened the MCTS reinforcement learning of _AlphaGo Zero_. _AlphaGo Zero_ is considered as a genuine AI because _AlphaGo Zero_ is able to explore the board game Go by MCTS without human knowledge (experience), Ref. [35]. Applications of Artificial Intelligence with MCTS to other games were developed in Refs. [36; 37; 38; 39]. Artificial Intelligence with MCTS was also applied to chemical syntheses of organic compounds, Ref. [40]. Application of MCTS to floor plans was considered in Ref. [41]. More applications of MCTS are still being explored, Refs. [42; 43]. For a recent review of MCTS, see Ref. [44]. Empirical evidence shows that this simple tree search method/algorithm leads to _fast convergence_ of an optimization search and successfully suggests the _most efficient arrangement_ of heat-storage ceramic bricks (with the highest long-term Waste Heat Recovery Ratio). See Section 3 for the details. Thus, this simple tree search method/algorithm may effectively enhance the thermal efficiency of a regenerative combustion system.
## 6 Discussion
### Identification Methods  
The first four groups of experiments in Section 4.3 are the comparisons among the proposed EAEND model (10), MNI model (27), NRI model (28), and ETZNN model (29), which aim to show the identification performance of several models in noise-free and random noise environments. The experimental results reveal that although MNI model (27) exhibits some resistance to noise, its identification performance is still inferior to EAEND model (10) under random noise influence. This disparity in performance can be attributed to the different expansion methods used by the models. EAEND model (10) employs the three-step forward difference formula for discretization, while MNI model (27) utilizes the second-order Euler formula. Consequently, the EAEND model (10) achieves higher accuracy. It should be noted that high-precision identification inherently involves a certain amount of computational complexity, influenced by the complexity of the formula and the size of the image. Despite this, EAEND model (10) maintains a high level of overall identification efficiency and accuracy.  
Compared with the traditional identification methods (e.g., SVM method, Otsu thresholding method, and MD method), the proposed EAEND model-assisted CEM identification method has obvious advantages. First, the core principle of the SVM method lies in finding an optimal hyperplane in the feature space that maximizes the distinction between different classes. Its effectiveness largely hinges on the appropriate selection of feature spaces. The performance of SVM can be compromised if the chosen features fail to adequately differentiate between classes, or if the complexity of the feature space impedes the hyperplane's efficient partitioning. This limitation might account for SVM's difficulty in accurately identifying fragmented and thin sea ice. The Otsu thresholding method fundamentally revolves around selecting a threshold that maximizes the variance between the target and background classes. However, its effectiveness diminishes in images with more than two major histogram peaks, as it may struggle to establish a distinct segmentation threshold. In the sea ice identification experiment, with only water as the background, the Otsu thresholding method demonstrates effective performance. Yet, because the Otsu thresholding method itself does not have an anti-noise function, there will still be some noise on sea ice images due to small noise interference in the recognition process. The effectiveness of the MD method is contingent upon having a precise covariance matrix, which necessitates a sufficiently large sample size for accurate estimation. Inadequate sample sizes can result in miscalculations of this matrix, impairing the accuracy of distance measurements, as demonstrated in Figure 7f. By contrast, the CEM method (1) primarily depends on the target spectrum and does not require extensive samples. The susceptibility of CEM method (1) to noise is significantly diminished, aided by EAEND model (10), bolstering its broad applicability and substantially improving recognition quality.
## 3 Modulation Techniques
### Dual Header-Pulse Interval Modulation (DH-PIM)  
DH-PIM is the modified form of pulse interval modulation, which empty slots between two pulses. In DH-PIM, pulses of different lengths are used in the head slot. Slot error of DH-PIM is given by:  
\[\text{P}_{\text{se,DHPIM}}=\frac{\left(\frac{3\alpha}{2}\right)\left|1+\text{ erfc}\left(\frac{\left(b-\sqrt{s_{t}}\right)}{\sqrt{2\sigma_{n}^{2}}}\right) \right|+\left[\frac{\left(4L-3\alpha\right)}{2}\right]\left|1-\text{erfc}\left( b-\sqrt{\sigma_{n}^{2}}\right)\right|}{4L} \tag{13}\]
## 4 Case Study  
### HUD Interface Design  
In this paper, three in-vehicle HUD interface designs were introduced to verify the proposed methodology. Based on the features of the HUD interface that has been mass-produced in the market at present, some key information was extracted about the interface design and an evaluation indicator system was set up (Table 1). Experts in HUD from a human factor and display technology perspective applied nine-level scales to assign the evaluation indicators and conduct a reliability and validity analysis of each indicator score in SPSS. The Cronbach \(\alpha\) (0.860) and KMO coefficient (0.812) are both greater than 0.80, which showed the high reliability and rationality of these evaluation indicators.  
The three candidate schemes are shown in Figures 2-4 and we applied the Entropy Weight--VIKOR method to evaluate and optimize the design schemes. Each scheme includes the classic, minimalism and sport mode and the design elements and complexity of each mode are different. Here are three schemes as follows:Figure 3: HUD interface design scheme 2. Scheme 2 adopts hollow navigation arrows, which enhance the integration of AR icons and the real environment and avoid visual obstructions to road conditions. Cyclic annular warning icons are designed for the WSP and FCWS. To be specific, icons in sport mode are designed to be more dynamic and powerful.  
Figure 2: HUD interface design scheme 1. This scheme adopts the solid boomerang navigation icon. The arrow thickness is appropriately increased in the design and the light and shadow effect is added to make it visually more stereoscopic. As for the WSP (warning system for pedestrians) and FCWS (forward collision warning system) design, the icons are designed in the form of squares as a whole and the specific presentation forms change with the different HUD system modes, e.g., in minimalism mode, only one semi-transparent warning rectangle is displayed.
## 4 Results and Discussion
### Correlations of Signal Signatures with \(\mathrm{POD_{A}}\) and \(\mathrm{POD_{E}}\)  
Finally, regarding the question of whether instantaneous PoD can be determined based on the signal characteristics during inspection, the correlations between the signal signatures and the detectability of the source damage were investigated. For this, PoD variations with signal-to-noise ratio (SNR) [35] and frequency content were evaluated since  
Figure 10: Variations in \(\mathrm{PoD_{A}}\) and \(\mathrm{PoD_{E}}\) with **(a)** strain rate, **(b)** AE hit rate.  
Figure 9: Variation in \(\mathrm{PoD_{A}}\) and \(\mathrm{PoD_{E}}\) with concrete strain.  
these parameters are related to signal power, the distinguishability of the signal from the noise, and the type of damage.  
To identify which frequency characteristic exhibits a stronger correlation with the signal's SNR, the frequency centroid and peak frequency were initially analyzed, as shown in Figure 11a. Here, the decrease in both frequency characteristics as SNR increases is clear. As it is known from the literature that low-frequency signals are less attenuated than high-frequency signals [39,54], a similar situation can be associated with SNR. For this reason, the increase in detectability as the SNR value increases is a reasonable suggestion.  
Accordingly, Figure 11b, in which SNR vs. PoD relations were plotted, also clarified this phenomenon for both approaches, particularly the higher regression with PoD\({}_{\text{A}}\). The reason for this could be that the SNR value is calculated based on the signal amplitude. Therefore, it is recommended to investigate compatibility with energy-based attenuation values as well. However, since the signals analyzed here were obtained through a passive method and thus the source energies are unknown, attenuation factors could not be calculated.  
As for variation in PoD with the frequency, the centroid frequency was used, which showed higher consistency with SNR in Figure 11a. In these results, higher PoD values were obtained at low-frequency activities. Here, it was also observed that the regression of energy-based PoD\({}_{\text{E}}\) is as high as PoD\({}_{\text{A}}\) based on amplitude. However, it should be noted that because the trend of the PoD\({}_{\text{E}}\) stabilizes after a certain frequency value, PoD  
Figure 11: (**a**) Relations of frequency centroid and peak frequency with SNR, variations of PoD\({}_{\text{A}}\) and PoD\({}_{\text{E}}\) with (**b**) SNR and (**c**) frequency centroid.  
calculation based on energy might indeed lead to a higher detectability bias, as discussed in previous analyses.
# Rotation Invariant Graph Neural Network for 3D Point Clouds
## 4 Methodology  
### Proposed Method  
Our network uses a rotation normalization algorithm that computes the bounding box with the lowest volume that best fits the point cloud. The oriented bounding box is computed using the PCA of the convex hull. A convex hull is a mesh that envelops all the points of the point cloud. The mesh has as vertices a subset of the point cloud. An example of a convex hull for the point cloud is seen in Figure 2(a) and the resulting vertices are seen in Figure 2(b).  
Once the vertices of the mesh have been established, PCA analysis is applied on these points, leading to the eigenvalues and corresponding eigenvectors ordered in descending order. The bounding box is obtained by bringing the mesh in a canonical rotation and then finding the maximum and minimum values on the \(X\), \(Y\), and \(Z\) axes. The canonical rotation is obtained by multiplying the convex hull vertices with the transpose of the eigenvector matrix. Afterward, the dimensions and corner points are computed from the minimum and maximum values on each axis. The bounding box orientation is given by the eigenvector matrix. An example of the oriented bounding box is given in Figure 2(c).  
The convex hull of the point cloud is computed using Open3D, which bases its method on the implementation from Qhull. For each point cloud, we obtain the oriented bounding box and multiply it with the inverse of the rotation matrix, obtaining a rotation  
Figure 3: Oriented bounding box computation. In (**a**), the convex hull is created. In (**b**), the points of the hull are selected and used in a PCA algorithm. The resulting bounding box is shown in (**c**).  
normalization of the input in a canonical position. We use the rotation normalized point cloud with the RGCNN network proposed in [5] for classification and part segmentation, as seen in Figure 4.
## 2 Materials and Methods
### Structure and Working Principle of the Harvester
#### 2.1.2 Overall Structure of GLVs Orderly Harvester  
The GLV orderly harvester was designed based on a previous study, as illustrated in Figure 3a. The GLV orderly harvester is mainly composed of a cutting mechanism, a depth-limiting profile system, an elastic clamping and conveying mechanism, an orderly collection mechanism, a collection box, a height adjustment system for the header, a crawler chassis, a frame, a transmission system, and a remote control. The whole machine provides power for each device through the generator, and each device adopts a DC motor to drive the operation. The device speed is independently adjusted through the motor speed controller, so as to improve the adaptability to the harvesting of various GLVs varieties, planting parameters, and operating environments. The control of in-soil cutting depth is mainly realized by the depth-limiting profiling system; the profiling bar is located at the back of the cutting device, which is always close to the planting ridge surface of GLVs. The cutting device is controlled to lift up and down in real time along with the variations of various terrains, so as to maintain the stability and reliability of the cutting depth. This GLV orderly  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**GLVs**} & \multicolumn{3}{c}{**Leaf**} & \multicolumn{3}{c}{**Root**} \\ \cline{2-7}  & **Length** & **Quantity** & **Density** & **Diameter** & **Length** & **Quantity** & **Density** \\  & **/(mm)** & **/(g)** & **/(kg\(\cdot\)m\({}^{-3}\))** & **/(mm)** & **/(mm)** & **/(g)** & **/(kg\(\cdot\)m\({}^{-3}\))** \\ \hline Minimum & 160 & 72.9 & 760 & 7.86 & 80.8 & 2.04 & 1100 \\ Maximum & 185 & 117.8 & 847 & 10.8 & 110.44 & 2.75 & 1275 \\ Average & 176.53 & 89.97 & 806.20 & 8.88 & 97.39 & 2.44 & 1174.80 \\ Standard deviation & 11.62 & 15.51 & 33.32 & 1.24 & 12.95 & 0.28 & 80.72 \\ Coefficient of variation & 0.07 & 0.17 & 0.04 & 0.14 & 0.13 & 0.11 & 0.07 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Physico-morphological parameters of GLVs.  
Figure 2: Structure diagram of GLVs.  
harvester is able to complete all the functions of the flexible feeding of GLVs, cutting roots into the soil, low-loss transportation, and orderly collection in a single operation.
## 2 Materials and Methods
### Fibers and Experimental Set-Up  
The three analyzed fibers, manufactured by Mitsubishi, were all step-index with 1 mm core diameter, but had different numerical apertures. MH4001 (MH), also called ESKA-MEGA, is a low-aperture fiber with a NA of 0.3. This lower aperture is achieved by a double-cladding design [6]. GH4002 (GH) is a duplex fiber with a NA of 0.5, also termed ESKA-PREMIER. Finally, BH4001 (BH), with the highest NA, 0.58, is a fiber resistant to high temperatures with a thermoset polymer jacket adequate for automotive or avionics applications.  
A diagram of the experimental set-up is schematized in Figure 1, where separate blocks labeled (a) to (d) were used to obtain the different measurements. As the components have been thoroughly explained elsewhere, we only give a very short description here [18; 19].  
The fiber input end was connected to the laser diode using a ST connector, and the output end was connected to a different device depending on the measurement being performed. The optical source used was the laser diode LD520P50 (LD520 in the following) with a nominal output power of 50 mW at 520 nm. The bias current was set to 80 mA at 25 \({}^{\circ}\)C, using the LDC205 laser controller and the LDM9T laser mount from Thorlabs. This laser mount was used to build an optical transmitter by directly modulating the laser diode either using the radio-frequency (RF) output of a vector network analyzer (VNA E5071C from Agilent), or using the data output of a BER Tester (OptoBERTTM OPB3200 from Optellent. Inc., San Jose, CA, USA). Both for the frequency domain characterization and for the BER performance measurements, the receiver used was the SPD-2_650 (SPD) from Graviton with a nominal 3-dB bandwidth of 1.2 GHz, but with a good response of up to 3 GHz. Its responsivity is 1 mV/mW at 650 nm, and 0.6 mV/mW at 520 nm. The SPD RF output was connected either to the VNA to obtain the S\({}_{21}\) transmission parameter or to the BER Tester to measure error-rates, as depicted in Figure 1c,d. The SPD also has an ST connector for the fiber. The received optical power was directly measured using an  
Figure 1: Experimental set-up with different subsystems to obtain (**a**) received optical power, (**b**) FFP images, (**c**) S\({}_{21}\) transmission parameter and (**d**) bit error rate.  
optical power-meter PM100 USB with a S150C sensor, both from Thorlabs, connected to the fiber through a SMA connector, Figure 1a. Finally, as shown in Figure 1b, the output end of the fiber was placed in a VL connector at a distance of 7.5 cm from a white screen, to register the reflected FFP image by means of a 12-bit monochrome camera (QICAM FAST 1394 CCD).