esa-sceva/satcom-chunk-collection huggingface dataset:
# BrushGaussian: Brushstroke-Based Stylization for 3D Gaussian Splatting
## Appendix A  
```
Input: Pretrained 3D Gaussian Splatting primitives: positions \(\mu\), scales \(S\), rotations \(R\), opacities \(o\), colors \(c\) Output: Clustered and pruned 3 DGS
1 Compute Importance Score:
2\(L\leftarrow\)build_scaling_rotation\((S,R)\)
3\(\Sigma\gets LL^{\top}\) ; // 3D covariance
4\(T\leftarrow\)trace\((\Sigma)\) // 3D covariance
5\(\ell\leftarrow\)0.2126 \(\cdot c_{R}+0.7152\cdot c_{G}+0.0722\cdot c_{B}\) ; // Luminance
6\(I\gets o\cdot T\cdot\ell\) ; // Importance score
7 Prune low-importance Gaussians:
8 Remove bottom 30% of Gaussians based on \(I\)
9K-Means Clustering:
10 Features \(\leftarrow\left[\mu,S,c\right]\) ; // Feature vector
11\(K\gets\)10000 ; // Cluster count
12 Perform \(K\)-means clustering
13 Step 4: Representative Gaussian Extraction
14 foreach cluster \(i\)do
15\((\mu^{\prime},R^{\prime},o^{\prime},c^{\prime})\leftarrow\)Mean\((\mu_{i},R_{i},o_{i},c_{i})\) ; // New positions, rotations, opacities and colors
16\(S^{\prime}\gets\)Mean\((S)\cdot\sqrt[3]{N_{i}}\), where \(N_{i}\) is the number of Gaussians in the cluster
17 Store the result as a new 3 DGS
```  
**Algorithm A1**Densification and Clustering  
```
Input: Conic matrix \(Con\), opacity \(o\), position \(\mu\), color \(c\), texture map Tex Output: Accumulated color \(C\)
1 foreachGaussian in batchdo
2\(\mathbf{d}\leftarrow\)compute_dist\((\mu,x)\) ; // Gaussian center to pixel
3\(\Sigma\leftarrow\)conic_to_covariance\((Con)\)
4\(\theta\leftarrow\)compute_rotation_angle\((\Sigma)\)
5\((x^{\prime},y^{\prime})\leftarrow\)rotate(\(\mathbf{d},\theta)\) ; // Rotate offset into ellipse frame
6\((\lambda_{1},\lambda_{2})\leftarrow\)compute_axes\((\Sigma)\)
7\((u,v)\leftarrow\)map_to_texture\((x^{\prime},y^{\prime},\lambda_{1},\lambda_{2})\)
8 mask \(\leftarrow\)sample_texture\((\text{Tex},u,v)\) ; // Texture lookup
9 ifinside\((x^{\prime},y^{\prime},\lambda_{1},\lambda_{2})\)then
10\(\alpha\leftarrow\)opacity \(\times\)mask ; // Apply mask to opacity
11 if\(\alpha<\epsilon\)then
12 continue
13\(T^{\prime}\gets T\cdot(1-\alpha)\)
14 if\(T^{\prime}<\delta\)then
15 break
16\(C\gets C+c\times\alpha\times T\) ; // Color accumulation
17\(T\gets T^{\prime}\) ;
```  
**Algorithm A2**Texture Mapping Algorithm for 3 DGS
## 3 Results and Discussion  
### Differences in Hydrometeorological Effects on \(G_{\rm s}\) in Different Biomes  
Hydrometeorological effects on \(G_{\rm s}\) were investigated using the method described in Section 2.4.2, and the results are shown in Figure 2. The most important influences on \(G_{\rm s}\) of evergreen broadleaf forests (BR-Sa3 site) were RH, VPD, and TA. For grassland, \(G_{\rm s}\) is more dependent on soil moisture, especially deep-layer soil moisture, as the most important influences on \(G_{\rm s}\) were SWC2 and Rn (US-AR1, US-AR2, and US-ARb site). This dependence on soil moisture is reduced as TA and Rn were the most important influences on \(G_{\rm s}\) in the farmland (US-ARM site), where abundant irrigation could be attributable.  
Figure 2: _Cont._From three sets of daily \(T\) estimates, TA, RH, SWC, CO2, and Rn showed high importance in a larger number of cases, which aligns with findings from [PERSON] et al. (2018), [PERSON] et al. (2019), and [PERSON] et al. (2019), and the WS importance was at the bottom position in most cases. In addition, Rn was also consistently higher in importance than Rg, except for the US-IB2 site and the Perez-Priego method at US-SRM, which is also consistent with the use of Rn rather than Rg in the Penman-Monteith formula ([PERSON] et al., 2019).  
In terms of precipitation, the eight sites can be categorized into four groups: humid (Cfa and Dfa), monsoonal (Am), Mediterranean (Csa and Dsa), and semi-arid (BSk). Among them, the main controller for \(G_{\mathrm{s}}\) shows no significant pattern in the humid type (US-ARb, US-ARM, and US-IB2). In contrast, for the Mediterranean type (US-AR1, US-AR2, and US-Ton), the moisture-related environmental factor such as SWC2 ranked higher among factors influencing \(G_{\mathrm{s}}\), and the monsoonal (BR-Sa3) had higher impact of VPD and RH on \(G_{\mathrm{s}}\), while the semi-arid type (US-SRM) had higher RH. This aligns with [PERSON] et al. (2019),  
Figure 2: Histogram of the importance of \(G_{\mathrm{s}}\) characteristics at each site. The y-axis starts at \(-0.5\) because the importance and error bars exceed \(0\) for some sites, and the negative y indicates that the feature hurts the RF prediction results.  
who found that in the U.S. Corn Belt's maize and soybean fields, atmospheric conditions predominantly regulate \(G_{\rm s}\), underlining the complex interaction between atmospheric moisture demand and soil water availability that dictates plant water use efficiency.
# New Methods for the Comprehensive Analysis of Bioactive Compounds in _Camabis sativa_ L. (hemp)
## 2 Results and Discussion
### HPLC Methods for the Analysis of Non-Psychaoctive Cannabinoids and Flavonoids in Hemp Extracts  
The aim of the present study was to provide a comprehensive analysis of the main bioactive compounds in fibre-type _C. sativa_ inflorescences. In this perspective, the development of HPLC methods for the determination of non-psychoactive cannabinoids and hemp-specific phenolics was a critical aspect of the research. RP-HPLC on C\({}_{18}\) columns, with acidic methanol (MeOH) or acetonitrile (ACN) used as the strong solvent eluent and acidic H\({}_{2}\)O as the weak one, is frequently used for the analysis of cannabinoids in _C. sativa_ extracts [3; 9; 13; 16]. Indeed, the use of an acidic mobile phase improves the separation performance, providing better peak shape and enhanced resolution, along with the enhancement of their ionization in HPLC-ESI-MS and MS\({}^{2}\) experiments [3; 9; 13]. For acidification, HCOOH is usually employed [3; 9; 13]. For our purpose, an Ascents Express C\({}_{18}\) (150 \(\times\) 3.0 mm I.D., 2.7 \(\upmu\)m, Supelco) as the chromatographic column and a gradient mobile phase composed of H\({}_{2}\)O and ACN, both containing 0.1% of HCOOH were selected, on the basis of a previous work [9].  
The gradient elution with the same mobile phase was then properly modified for hemp flavonoids, since peaks corresponding to cannflavins A and B were observed to elute with the solvent front. Under the final conditions, a good resolution of peaks belonging to cannflavins A and B and other hemp phenolics was achieved. Representative chromatograms obtained by using the two HPLC methods are shown in Figure 7.
## 2 Methods
### Vehicle Lateral Positioning Algorithm  
Assume that the angular difference of the heading angle in adjacent time is \(\alpha\), and the gyroscope sampling frequency is 50 Hz. Since the steering phase is a uniform rotation, we divide the angle of the vehicle in the sampling interval of 0.02 s into 10 more parts to obtain a more accurate steering lateral distance S0, and the calculation process is shown in Equations (13)-(17).  
\[S0_{1}=\sum_{k=1}^{10}\sin(\frac{\alpha_{1}}{10}\times k)\times\frac{S0_{1(od) }}{10} \tag{13}\]  
In the above formula, \(\alpha_{1}\) is the amount of change in the heading angle of the car in the first 0.02 s, \(S0_{1(od)}\) is the distance traveled in the first 0.02 s time, which can be obtained by the odometer, and \(S0_{2}\) can be obtained by the same reasoning, as shown in the following equation:  
\[S0_{2}=\sum_{k=1}^{10}\sin(\alpha_{1}+\frac{\alpha_{2}}{10}\times k)\times \frac{S0_{2(od)}}{10} \tag{14}\]  
The \(\alpha_{2}\) in the above equation is the amount of change in the vehicle's heading angle in the second 0.02 s, and so on to obtain \(S0_{n}\).  
\[S0_{n}=\sum_{k=1}^{10}\sin(\alpha_{1}+\alpha_{2}+\cdots\cdots+\alpha_{n-1}+ \frac{\alpha_{n}}{10}\times k)\times\frac{S0_{n(od)}}{10} \tag{15}\]  
Adding \(S0_{1}\), \(S0_{2}\)...... \(S0_{n}\) to obtain \(S0\).  
\[S0=\sum_{i=1}^{n}S0_{i} \tag{16}\]  
In the equation \(n=\)\(\frac{t_{0}}{0.02}\), \(t_{0}\) is the steering time.  
The lateral distance S1 traveled during the holding phase is shown in the following equation:  
\[S1=S1_{(od)}\times\sin\theta \tag{17}\]  
Figure 2: Vehicle lane change diagram.  
In the above equation, \(S1_{(od)}\) is the distance traveled by car in the holding phase, which can be obtained from the odometer, and \(\theta\) is the difference between the heading angle at the beginning and end of the steering phase, \(\theta=\alpha_{1}+\alpha_{2}+\cdots\cdots+\alpha_{n}\). The calculation of the lateral distance S2 in the return phase is the same as that of S0.  
We have to set a threshold value for the steering angle of the vehicle at different speeds and determine whether the vehicle is changing lanes by the relationship between the difference of the heading angle in adjacent time and the threshold value. As shown in Figure 3, it is the maximum steering speed corresponding to different front wheel turning angles when the car is driving on roads with different adhesion coefficients. The tunnel is dry asphalt pavement, in general, the adhesion coefficient of good dry asphalt pavement is from 0.7 to 0.8. We take the median 0.75. According to Figure 3, taking into account the comfort of the driver in the car when changing lanes, we assume that the time of the car in the steering phase and the return phase is 0.2 s, and the sampling frequency of the inertial components is 50 Hz. We can obtain the front-wheel turning angle thresholds corresponding to different speeds of the car when changing lanes in the tunnel with 0.02 s as the unit time, as shown in Table 1.
## 5 Conclusions  
This study used computational analysis methods to examine the thermal behavior of a large-area hot plate with a working area of 240 mm \(\times\) 240 mm that was equipped with straight cartridge heaters arranged vertically, as well as cooling holes through which refrigerant flowed. The hot plate was made from stainless steel which has a low thermal diffusion coefficient but also has high surface hardness and strong pressure resistance. Computational analysis methods were used to assess whether the hot plate has thermal characteristics which satisfy the demands of usage as TH-NIL equipment. When PID control was used and 20 cartridge heaters with 300 W heat generation capacity were arranged in parallel, the hot plate could be heated in a stable manner to the target temperature of 200 degrees within 5 min, but the temperature deviation within the hot plate during its normal state was fairly large. In the cooling tests, the temperature deviation was large in the transient states of both air cooling and water cooling using the cooling holes, and the process requirements were not satisfied. To resolve this problem, a new hot plate model was proposed in which a heat pipe is installed on the cooling holes and part of the heat pipe is cooled during cooling mode, and the thermal characteristics of this model were assessed. The hot plate with the heat pipe installed reduced the temperature deviation during heating mode to within the allowable range. During cooling mode, it reduced the temperature deviation greatly compared to the hot plate that was directly cooled using air holes. This study has confirmed that there is a need for additional improvements to the usefulness of the heat pipe-equipped hot plate in terms of temperature uniformity, as well as improvements to the hot plate's cooling performance and temperature uniformity through optimization of the heat pipe.  
Conceptualization, G.P.; methodology, C.L.; investigation, C.L.; data curation, G.P.; writing--original draft preparation, C.L.; writing--review and editing, C.L.  
This research received no external funding.  
The authors declare no conflicts of interest.  
Figure 20: Temperature distribution on the surface of the hot plate with heat pipes installed in cooling holes. The time is 200 s after heating with PID control.
# Sixth-Generation (6G) Networks for Improved Machine-to-Machine (M2M) Communication in Industry 4.0
## 3 Results of the Review  
In line with the latest knowledge and experience, the key purpose of IIoT is to interconnect machines, anywhere and anytime, within the production system in order to improve its productivity, efficiency, safety, and intelligence (also as part of the implementation of previously unused solutions, such as 3D printing). IIoT has unique types of built-in intelligent devices (production, control/design, sensors, effectors, as well as e-commerce for cooperation with the customer in order to select the final shape and configuration of the product, service, or process), network technologies, quality of services, and command and control processes. The IIoT architecture becomes the basis for IIoT applications--factory automation (FA) and process automation (PA)--in three areas: control, network, and IT. Industrial control systems, including those based on 6G, provide M2M communications, software-defined networks (SDNs), and--from a computing perspective--cloud and edge and hybrid platforms in cloud and edge computing. In the context of a cyber-physical system (CPS), control, network, and computing systems are interactively built and shaped into IIoT systems to achieve the operator's design goals. Automation in dynamic industrial operations uses techniques such as reinforcement learning for the faster and more efficient automatic reconfiguration of control systems and networks in dynamic industrial environments.
# Privacy-Preserving Data Analytics in Internet of Medical Things
## 2 Background
### Cryptographic Techniques  
Before making data available to the public, the data curator could encrypt them [10]. However, it is extremely difficult to encrypt vast amounts of data using standard encryption techniques, and such methods must only be put into practice when gathering data. Homomorphic encryption allows calculations to be performed on encrypted data, producing an encrypted output with final results equivalent to a plaintext operation after re-encryption.  
Similarly, secure multiparty computation permits several parties to work together to jointly compute a function over their private inputs. Moreover, Blockchain technology is used in privacy preservation of data, employing cryptographic hash functions to ensure data integrity and immutability. Cryptographic hash functions like SHA-256 convert data into a fixed-size hash, guaranteeing that tampering is readily identifiable by producing a totally distinct hash for every alteration of the input data. It provides transparency and security in data sharing and transactions. However, encryption decreases the utility of the data, in addition to being difficult to execute.
## 4 Results and Discussion
### Damage Identification
However, as the fracture stage approaches, the rapid growth of the crack generates a significant number of impulse signals, resulting in a significant increase in the impulse index before 30,000 cycles, peaking at almost 900, as illustrated in Figure 15d. A comparative analysis of the evolution of various time-domain parameters suggests that the AE counts and energies provide a more direct and intuitive means of identifying rapid crack extension. Simultaneously, the impulse factor demonstrates the ability to characterise the emergence of crack-extending impact-like AE signal components at an earlier stage. As shown in Figure 16, upon a sudden change in the characteristic parameters of the AE signals, the compressor was stopped, and an examination of the prefabricated fractured rods was conducted using the dye-penetrant testing method [59]. Surface cracks with an approximate length of 1.17 mm were detected. Therefore, the use of these four parameters allows for the accurate identification of rod crack existence during compressor operation, facilitating the early warning of rod failure. In this experiment with 316 rpm, the crack propagation warning can be provided approximately 30 min (around 10,000 cycles) before the fracture, providing sufficient operating time to shut down the compressor for maintenance. Figure 15: The variation in the different AE parameters to the fatigue loading times. AE counts (**a**); AE energy (**b**); peak-to-peak value (**c**); impulse factor (**d**).
# Mid-Infrared Grayscale Metasurface Holograms
## 3 Results  
The completed hologram in Figure 3a is illuminated by the laser beam impinging on the interface at normal incidence. The far-field calculated image in Figure 4a shows that some information is lost during the far-field projection, as observed after comparing the image quality obtained numerically and the input object in Figure 2a. This is due to the phase discretization. The ideal resolution of the simulated image is 32.5 \(\upmu\)m, which is determined by the total size of the hologram, the pixel size of the hologram and the optical resolution of the lens. At the back foci plane of our imaging setup, an MCT detector is attached on a microdisplacement platform with a scanning step of 25 \(\upmu\)m for scanning the far-field image of the hologram. The detected image with size \(2\times 2\) mm is shown in Figure 4b. The image has been experimentally reconstructed by considering point-by-point intensity distribution, and assembling the spatial-intensity distribution to recover our expected smiling logo. Note that the bright nose spot in the center of the image is caused by the zeroth-order diffraction of the hologram sample. From the raw data of the measured image, we estimate that the unwanted zeroth-order perturbation is \(\sim\)19%, indicating that the total efficiency of our metasurface-based hologram is considerably high, up to 81%. We would like to underline the fact that the reported performance could be further increased,  
Figure 3: (a) Sketch of the on-axis optical scanning experiment setup for demonstration of the hologram. (b,c) The SEM images of the fabricated hologram in oblique view and top view.  
notably by properly considering near-field coupling of metasurface building blocks using optimization design methods [30; 31; 32; 33; 34; 35].
## 7 Performance Analysis
### Communication Costs  
A comparative analysis of the communication overhead was conducted between the proposed scheme and existing works [15; 32; 34; 35; 36; 37; 39]. We assumed a reliable communication environment and measured the ideal communication costs based on the bits of messages transmitted in the authentication phase. Following the assumptions in [35; 36], we defined the sizes of the hash value, symmetric key encryption/decryption message, and ECC point as 160, 128, and 320 bits, respectively. The timestamp was 32 bits, and the identity and random nonce were assumed to be 64 bits each. In the proposed scheme, the login request message \(\{UID_{i},A_{i},V_{1},T_{1}\}\) required \(160+160+160+32=512\) bits. Similarly, the messages exchanged during the authentication phase \(\{UID_{i},B_{i},V_{2},T_{2}\}\), \(\{C_{i},V_{3},T_{3}\}\), and \(\{D_{i},E_{i},F_{i},V_{4},T_{4}\}\) required 512 bits, 352 bits, and 672 bits, in order. Thus, a total of 2048 bits was incurred as the communication overhead in our scheme comprising four messages. Table 6 indicates the comparative results with related schemes. Although [PERSON] et al.'s scheme [15] incurs a slightly lower communication overhead than ours, it fails to defend against various security attacks. Therefore, our scheme achieved a well-balanced trade-off between the communication overhead and security.
## 3 Results and Discussion  
Three CDA modes were employed and to be compared with the normal engine work (N--all cylinders work normally) at similar steady state conditions:  
1. CDAo--cutting off the fuel supply in the deactivated cylinders;
2. CDAc--cutting off the fuel supply and disabling intake and exhaust valves in the deactivated cylinders;
3. CDAc + EGR--cutting off the fuel supply and disabling intake and exhaust valves in the deactivated cylinders incorporated with controlling the EGR rate.  
It is worth noting that for the investigated diesel engine D-49, the injection timing and rail pressure vary with engine load and engine speed. These change characteristics are maintained under CDA operations. As an example, Figure 6 shows the injection profile under the normal operation and the injection profiles in the active cylinder under two CDA modes at a speed of 450 rpm and a load of 1000 N-m.
# Common Gabor Features for Image Watermarking Identification
## 4 Results and Discussion
### Evaluation Results  
Because different classifiers have differing classification performance, we employ the discriminant analysis (DA) and random forests classifiers to classify distinct Gabor features vectors such as mean squared energy, mean amplitude, and combined feature. In order to obtain accurate findings, we used 10-fold cross-validation. Table 1 shows the outcomes of the proposed evaluation measures (TPR and FNR) over the VOC2008 database.  
Despite that all classifiers use the same feature vector, they yield different outputs. This is due to each classifier having its own set of characteristics. Figures 10 and 11 depict the visual impact of various feature types on various classifiers.  
The TPR value for DA classifier fluctuates between mean squared energy, mean amplitude, and combined feature, with values of 82.15, 88.54, and 93.71, respectively. The TPR value for Random_forest classifier fluctuates between mean squared energy, mean amplitude, and combined feature, with values of 78.82, 82.92, and 86.35, respectively. The TPR for both classifiers increased significantly, showing significant variations in those three features.  
The FNR value for DA classifier fluctuates between mean squared energy, mean amplitude, and combined feature, with values of 17.85, 11.46, and 6.29, respectively. The FNR value for Random_forest classifier fluctuates between mean squared energy, mean amplitude, and combined feature, with values of 21.18, 17.08, and 13.65, respectively. For both classifiers, the FNR declined considerably, indicating significant differences in those three features.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Classifiers**} & \multicolumn{2}{c}{**Mean Squared Energy Feature**} & \multicolumn{2}{c}{**Mean Amplitude Feature**} & \multicolumn{2}{c}{**Combined Feature**} \\ \cline{2-7}  & **TPR (\%)** & **FNR (\%)** & **TPR (\%)** & **FNR (\%)** & **TPR (\%)** & **FNR (\%)** \\ \hline Discriminant analysis (DA) & 82.15 & 17.85 & 88.54 & 11.46 & **93.71** & **6.29** \\ \hline Random\_forest (RF) & 78.82 & 21.18 & 82.92 & 17.08 & 86.35 & 13.65 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The Results of TPR and FNR of two classifiers across the VOC2008 Database.  
Figure 10: Effect of Feature kind on TPR values for different classifiers.  
The proposal attained a highest TPR of 93.71 and a lowest FNR of 6.29 when the feature combination of mean squared energy and mean amplitude feature was applied. As a result, it is natural to conclude that the DA classifier outperforms the random forest classifier in all three features vectors. According to the results, the DA classifier with combined features is the best of our proposed method. This demonstrates that the performance outcomes of the proposed approach are relatively stable.