esa-sceva/satcom-chunk-collection huggingface dataset:
# A Watermarking Method for 3D Printing Based on Menger Curvature and K-Mean Clustering
## 4 Experimental Results and Analysis
### Invisibility Evaluation  
We embedded differential watermarks into test models according to the number of groups. This means each test model will be embedded into a watermark where the length of the watermark bits is equal to the number of groups corresponding to that model. Each watermark is randomly generated. In order to evaluate the invisibility of the proposed method, we computed the mean distance error \(d^{m}(v,~{}v^{\prime})\) between the original 3D triangle mesh and the watermarked 3D triangle mesh. The mean distance error \(d^{m}(v,~{}v^{\prime})\) is calculated by Equation (12). Therein, \(v,v^{\prime}\) are the vertices of the original 3D triangle mesh and the vertices of the watermarked 3D triangle mesh, respectively.  
\[d^{m}(v,~{}v^{\prime})=\frac{1}{3\times|\mathbf{M}|}\sum_{i=1}^{|\mathbf{M}|} \sum_{j=1}^{3}||v_{ij}-v^{\prime}_{ij}|| \tag{12}\]  
The computation result of the mean distance error between the watermarked 3D triangle mesh and the original 3D triangle mesh for test models in Figure 5 is shown in Table 1. Overall, the mean distance error between the watermarked 3D triangle mesh and the original 3D triangle mesh is very small. With the test models in Figure 5 divided into 19-887 groups according to the number of facets, the mean distance error is formed from 2.4931 \(\times\) 10\({}^{-6}\) to 4.113 \(\times\) 10\({}^{-6}\). This proves that the difference between the watermarked 3D triangle mesh and the original 3D triangle mesh is very small. Therefore, it proves that the invisibility of the proposed method is very high. Based on Equation (12), we concluded that the mean distance error is dependent on the number of watermarked vertices and the number of vertices. The number of watermarked vertices is dependent on the number of groups and the mean Menger curvature of each group. Therefore, we concluded that the mean distance error (the invisibility of the proposed method) is dependent on the number of groups. From Table 1, we concluded that the mean distance error is decreased according to the number of groups. Figure 6 shows the mean distance error according to the number of groups.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name Model** & **of Facets** & **of Groups** & **Mean Distance Error** \\ \hline Orient Tube & 464 & 19 & 4.0952 \(\times\) 10\({}^{-6}\) \\ Orient Holder & 520 & 21 & 4.0452 \(\times\) 10\({}^{-6}\) \\ Number 7 & 526 & 21 & 4.0042 \(\times\) 10\({}^{-6}\) \\ Fidget & 750 & 31 & 4.1330 \(\times\) 10\({}^{-6}\) \\ Valve Tube & 2062 & 64 & 3.1083 \(\times\) 10\({}^{-6}\) \\ Pitco & 7442 & 232 & 3.1329 \(\times\) 10\({}^{-6}\) \\ Diamond Grip & 8870 & 277 & 3.1274 \(\times\) 10\({}^{-6}\) \\ Holder & 12,392 & 309 & 2.4931 \(\times\) 10\({}^{-6}\) \\ Lion & 15,366 & 384 & 2.4991 \(\times\) 10\({}^{-6}\) \\
3D Printer & 35,482 & 887 & 2.4993 \(\times\) 10\({}^{-6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mean distance error.  
Figure 5: Test 3D triangle meshes.
# Phased Array Antenna Analysis Workflow Applied to Gateways for LEO Satellite Communications
## 3 System Architecture
### Telesat's System  
Lightspeed is the mega-constellation of the Canadian satellite communications company Telesat. According to FCC filings, it will be developed in two phases: an initial stage, with 298 satellites, and a second phase, having 1671 satellites [25; 26]. It is scheduled to begin services in 2023 [27], having launched the first satellite in 2018 [28].  
Satellites will connect to earth stations with four steerable spot beams with a minimum elevation of 10 degrees [29]. Their gateways will use K-band in the downlink, from 17.8 GHz to 20.2 GHz, and from 27.5 GHz to 30 GHz in the uplink [2].
## 3 Methodology
### Clustering of Adversarial Samples: Distance Metric and Degradation Potential
The degradation potential of an adversarial example could be represented as a vector of probabilities over the neural network's outputs, possibly obtained by applying a softmax function to the output logits of the network when fed with the adversarial example. A crucial thing to note is that KL divergence is not symmetric, i.e., \(D_{KL}(P||Q)\
eq D_{KL}(Q||P)\). This means that the order in which the adversarial examples are compared will affect the result. This could be leveraged to encode some form of directionality into the clustering process, for example, by always placing the adversarial example that is deemed more harmful (causes a larger increase in the loss) as \(P\) and the less harmful one as \(Q\). This way, clusters will be more influenced by the more harmful examples, which could potentially enhance the robustness of the resulting model. One practical consideration when computing KL divergence is that it is not defined when \(P(i)>0\) and \(Q(i)=0\). A common solution is to add a small constant to the distributions, known as smoothing, to ensure that all probabilities are nonzero. This needs to be done carefully to avoid significantly altering the original distributions. This version of \(D_{adv}\) allows the clustering algorithm to capture the divergence between the ways different adversarial samples affect the model's behavior, thereby providing a richer characterization of the adversarial sample space. The overall ASC clustering algorithm is given in Algorithm 1. ```
1:Set of adversarial examples \(\{x_{adv}\}\), neural network model \(f\) with parameters \(\theta\), \(l_{p}\) norm \(p\), trade-off parameter \(\lambda\)
2:Clustering of adversarial examples \(\{C\}\)
3:Compute the degradation potential for all adversarial examples:
4:for\(x_{adv}\) in \(\{x_{adv}\}\)do
5:\(\Delta_{adv}(x_{adv}.f)=\
abla L(f(x_{adv};\theta),y)\)
6:endfor
7:Compute the distance matrix using the novel distance metric:
8:for pair of adversarial examples \((x_{adv1},x_{adv2})\) in \(\{x_{adv}\}\)do
9:\(D_{adv}(x_{adv1},x_{adv2})=||x_{adv1}-x_{adv2}||_{p}+\lambda\cdot D_{KL}( \Delta_{adv}(x_{adv1},M)||\Delta_{adv}(x_{adv2},M))\)
10:endfor
11:Apply any clustering algorithm to \(\{x_{adv}\}\) using the distance matrix \(D_{adv}\):
12:\(C=\text{clustering}(\{x_{adv}\},D_{adv})\)
13:return the clusters \(\{C\}\)
```  
**Algorithm 1** Density sensitive adversarial clustering  
It is important to note that the methodology is not limited to a specific model or dataset and is generalizable across different machine learning tasks.
## 4 Discussion
As such, it is crucial to explore the quantitative relationship between tomato yield, fruit quality, and water deficit to create an irrigation plan that promotes high-quality tomato production [30]. In our research, we utilized yield quality as a combined indicator of total yield and brix degree, the major determinant of processing tomato quality. PlaSoMan displayed the highest YQ value, indicating that the evaluation of both plant water status and soil moisture level effectively distributed water to achieve the optimal balance between quantity and quality, while also saving water resources. In addition to the above, PlaSoMan had the highest WP\({}_{\text{YQ}}\) value compared to other irrigation scheduling methods. This value indicates how much totalwater, including precipitation and irrigation, was utilized to achieve YQ [4]. PlaSoMan's superior performance in achieving high-quality and water-friendly production placed it on the positive side of PC2 in the principal component analysis. Additionally, the YQ and WPYQ responses remained stable over two years with the new smart irrigation scheduling method, which is particularly noteworthy considering ongoing climate change. Finally, to provide a comprehensive understanding, it is worth mentioning that the PlaSoMan system also yielded the greatest levels of lycopene, \(\beta\)-carotene, and vitamin C content in the fruit, which are nutrients beneficial for humans [30].
## 3 Results
### Sensors and Sensor Type
The multiband data archive of AVHRR starts in 1981. Different products offer spatial resolution between 1.1 and 8 km, with up to daily revisit cycles [98; 99; 100]. When all studies employing optical sensors are aggregated, the proportion is 96%. Of these studies, over 80% utilize the Normalized Difference Vegetation Index (NDVI) or Enhanced Vegetation Index (EVI). The donut chart on the left in Figure 5 shows the distribution of the platforms. The employed sensors revealed that a large proportion of the data are spaceborne. A total of six studies used aircrafts as carrier systems, and four used UAV. Different sensor types were rarely combined (4.7% of all studies). Thermal data were combined with multispectral data three times. Each Sentinel-1 was combined with Sentinel-2, MODIS was combined with AMSR, and ALS was combined with AHS, represented by the donut chart on the right.
## 3 Results and Discussions
### Loss of Flow Accident
#### 3.2.1 Unprotected Loss of Flow  
Figure 10 shows the transient responses of ADS and CNES for unprotected loss of flow due to primary pump coastdown. The mass flow rate of the reactor core drops rapidly after the primary coolant pump trips, which causes the heat produced by fission reaction in the fuel not to be able to be taken out in time, and the cladding temperature rises rapidly. As shown in Figure 10c, the maximum cladding temperature in the ADS core reaches 1439 K, which means it has exceeded the damage limit. For one thing, the power drop is small due to the deep subcriticality of the ADS core and low sensitivity to negative reactivity feedback, which is from 100% full power to 90% level. Furthermore, the loss of flow rate prevents the heat transferral from the pellet to the coolant, so the temperature of the cladding rises to a very high value. Due to the negative reactivity introduced by the Doppler effect and the coolant temperature effect, the reactor power of CNES quickly decreases from 100% to 41%, and the cladding temperature rises to the peak point and then gradually decreases. The peak temperature of the cladding in the CNES core is only 1005 K,  
Figure 9: Transient responses of protected reactivity insertion of 700 pcm between the ADS core and the CNES core.  
which is lower than the breakage limit. Results of comparison show that the inherent safety characteristics of the CNES core are better than those of the ADS core under loss of flow accident, and the protection system of ADS should be quickly activated to achieve an emergency shutdown after the accident occurs.
## 2 Related Works
Precision and Recall measures are also reported. In [19], an Intelligent Intrusion Detection System (IDS) that combines deep learning and machine learning techniques is proposed. Convolutional Neural Networks (CNN) and the Adaptive Neuro Fuzzy Inference System (ANFIS) are both used in the system. This strategy overcomes this constraint by making use of soft computing techniques and an Intelligent IDS system, in contrast to previous systems that focus on identifying known threats in the VANET environment. A hybrid technique is used to identify several attack types, such as Denial-of-Service (DoS), Botnet, PortScan, and Brute Force assaults. Real-time data from the CIC-IDS 2017 dataset are used for evaluation, and the proposed methodology shows effectiveness compared to other state-of-the-art techniques. In study [20], a practical and effective machine learning (ML)-based method for detecting malicious behavior is suggested. The Vehicular Reference Misbehavior Dataset (VeReMi) is utilized by the proposed machine-learning-based misbehavior detection system. The VeReMi dataset contains labeled examples of five different types of position falsification attacks with varying vehicle and attacker densities. The authors propose a model that employs two sequential BSM approaches to detect these attacks. The categorization of the roadside unit's model enables the identification and removal of harmful nodes from the network, reducing computational overhead on moving vehicles. Researchers used SVM in the study [21] to identify false message attacks using the driving status, speed, acceleration, vehicle type, reputation, and distance as features. Additionally, they investigated message suppression strategies involving packet loss, packet delay, and packet forwarding. In order to combine various assessments, their proposed vehicle trust model calls for both a local vehicle trust module and a central Trust Authority (TA). In another paper by [22], authors provide a machine learning (ML) mechanism that makes use of three new features, namely those that relate to the sender position, to improve the performance of IDS against position falsification attacks. Additionally, it compares K-Nearest Neighbor (KNN) and Random Forest (RF), two distinct machine learning (ML) algorithms for classification that are used to identify malicious vehicles using these features. All the aforementioned studies used approaches that have proven to be effective in detecting known and unknown attacks. However, they face several challenges in VANETs because of the evolving nature of attack patterns as well as the complexity of the data which can increase rapidly over time, making it difficult to scale traditional machine learning algorithms to handle data generated in real-time. In the next section, we introduce the use of incremental online machine learning algorithms in the context of attack detection in VANETs.
# Feasibility, Design, and Deployment Requirements of TCR for Bistatic SAR radiometric Calibration
## 4 Error Factors of TCR RCS
### Pointing Deviation  
In radiometric calibration, it is important to align the TCR boresight with respect to the SAR antenna boresight. Either the attitude measurement errors of the SAR platform and antennas or the pointing of the TCR in the field deployment process will cause the pointing deviation and the final radiometric calibration errors. Here, we focused on the latter from the view of the TCR deployment requirements.  
Using a TCR with an interleg length of 0.4 m and a bistatic angle of 2\({}^{\circ}\) as an example, the bistatic peak RCS is 20.25 dB, and the corresponding boresight is in the direction: \(\theta=54.62^{\lx@math@degree},\varphi=44^{\lx@math@degree}\), which were obtained from the simulation results in Figure 8. The bistatic RCS pattern was calculated in the range of \(50^{\lx@math@degree}\leq\theta\leq 60^{\lx@math@degree}\) and \(40^{\lx@math@degree}\leq\varphi\leq 50^{\lx@math@degree}\), and compared with the peak RCS. The contour map of the azimuth and elevation misalignments is shown in Figure 11.  
The RCS reduction becomes greater with the incident direction deviation from the optimum boresight increasing. As shown in Figure 12, we can also get the effect of the pointing deviation on the RCS for different bistatic angles. In the radiometric calibration, it is possible to control the pointing  
Figure 11: The contour map of the RCS loss due to pointing deviation.  
Figure 10: The effect of machining error of length on the RCS at different bistatic angles.  
deviation within 0.5\({}^{\circ}\) generally. Furthermore, an azimuth deviation of less than 0.5\({}^{\circ}\) and elevation deviation of less than 0.5\({}^{\circ}\) could introduce no more than 0.5-dB RCS loss at the X-band. However, a pointing deviation of less than 0.5\({}^{\circ}\) in azimuth and elevation could make the resulting RCS within about 0.1 dB of the peak value for monostatic calibration [25]. By comparing the two results, we found that the RCS loss due to pointing deviation was more severe for bistatic calibration. Moreover, the RCS loss was more severe for elevation misalignments than for azimuth misalignments, so the elevation angle should be adjusted as accurately as possible in deployment.
## 3 Results and Discussion
### Effects of CuO NPs on Ammonia Assimilation in Soybeans  
The presence of CuO NPs lowered the NH\({}_{4}\)+-N contents in both shoots and roots when compared to the control; additionally, the lowest NH\({}_{4}\)+-N contents were evident in the 50 nm CuO NP treatments (Figure 3A,B). At 1 mg/kg, the addition of 20 nm CuO NPs decreased the NH\({}_{4}\)+-N contents by 19.6% and 3.7% in shoots and roots, respectively, relative to the control, while 50 nm CuO NPs decreased the NH\({}_{4}\)+-N contents by 43.9% and 32.8% in shoots and roots, respectively, relative to the control. At 10 mg/kg, 20 and 50 nm CuO NPs decreased the NH\({}_{4}\)+-N contents by 36.1-48.4% and 13-25.9% in shoots and roots, respectively, relative to the control (Figure 3A,B). It is reasonable to speculate that the decreases in the NH\({}_{4}\)+-N contents were because the CuO NP treatments promoted ammonia assimilation and accelerated the reduction of NO\({}_{3}\) -N in plants. Thus, the key enzymes (GS and GOGAT) in ammonia assimilation were determined across all the CuO NP treatments.  
At 1 mg/kg, both 20 and 50 nm CuO NPs increased the GS enzyme activity in both shoots and roots, except for the 20 nm CuO NP treatment, which decreased the shoot GS activity by 16.8% relative to the control (Figure 3C), while the 50 nm CuO NP treatment increased the GS activity by 59.4% and 13.3% in shoots and roots, respectively. At 10 mg/kg, the highest GS activity in shoots was evident in the 50 nm CuO NP treatments, where the GS activity was 76.9% higher than the control (Figure 3C); the 20 nm CuO NP treatments increased the GS activity by 22.3% (Figure 3D) in roots. Changes in the GOGAT enzyme activity in roots were similar to the GS enzyme activity as a function of CuO NPs. In shoots, root application of 10 mg/kg 20 nm CuO NPs increased the GOGAT enzyme activities by 53.3%, while 50 nm CuO NPs increased them by 58.6% and 63% at 1 and 10 mg/kg, respectively, relative to the control (Figure 3E). In roots, at 1 mg/kg, 50 nm CuO NP treatments increased the activity of the GOGAT enzyme by 95%, while 20 nm CuO NP treatments had no impact. At 10 mg/kg, the activity of the GOGAT enzyme was increased by 35% by both sizes of CuO NPs (Figure 3F).  
Figure 2: NO\({}_{3}\) – content (**A**,**B**), NR enzyme activity (**C**,**D**), and NiR enzyme activity (**E**,**F**) in soybean shoots and roots in the 20 and 50 nm CuO NP treatments at 1 and 10 mg/kg for three weeks. Error bars represent the standard error of 5 replicates, and the different letters in each panel represent significantly different values at \(p<0.05\).  
Ammonia nitrogen (NH4+-N) is toxic and cannot be accumulated in plants; it needs to be assimilated to transform into a form that can be absorbed and utilized by plants [42]. The assimilation of NH4+-N in plants mainly depends on the GS/GOGAT cycle, which converts the inorganic N into organic N compounds [43]. The content of NH4+-N in roots was higher than in shoots, demonstrating that the activities of GS and GOGAT enzymes in roots were usually significantly higher than those in shoots.
## 2 Protocols for Synchrotron X-Ray Experiments
### Pdf
#### 2.5.3 Results and Discussion  
The effectiveness of the proposed protocol is validated using a standard sample, TEC10V30E (Pt-loaded carbon, hydrogen-reduced, supplied by TANAKA Precious Metals). The structure factor \(S(Q)\), derived after corrections, exhibited well-resolved high-\(Q\) oscillations, ensuring reliability of the reduced PDF G(\(r\)). The total radial distribution function \(T(r)\), obtained through Fourier transformation of \(S(Q)\), revealed peaks corresponding to local structural features. Gaussian fitting of the first peak (representing Pt-Pt nearest-neighbor  
Figure 12: (**a**) PDF \(G(r)\) for the standard sample TEC10V30E, a hydrogen-reduced material supplied by TANAKA Precious Metals. (**b**) \(T(r)\), where each peak is fitted using a Gaussian function combined with a baseline. The integration range for each peak, corresponding to \(\pm 3\sigma\) from the peak center where \(T(r)>0\), is highlighted with distinct background colors. The integrated values represent the relative coordination numbers, offering insights into local structural trends.  
distance) yielded a coordination number of 11.1, agreeing with the theoretical value of 10.8 for 3 nm Pt nanoparticles. This demonstrates the robustness of PDF analysis in determining local atomic correlations and coordination environments.  
In addition to structural characterization, the potential of PDF analysis for particle size evaluation was explored. By applying a spherical nanoparticle correction factor \(f(r)\), the attenuation of atomic correlations near the particle boundary was modeled. Initial findings suggest that PDF analysis could complement SAXS by providing localized structural information reflecting nanoparticle size trends. Future studies will aim to refine this methodology and establish its broader applicability to diverse materials systems.  
While the current results demonstrate the protocol's capability for structural characterization, challenges remain. The manual nature of data correction and fitting processes poses significant time and labor demands. The particle size estimation approach requires further refinement and validation across diverse samples and particle size distributions. Addressing these issues through automated workflows is critical for enhancing the methodology's accessibility and efficiency.  
In conclusion, the PDF analysis protocol has proven effective for high-precision structural analysis and holds promise for broader applications in materials research. Ongoing efforts to streamline data processing and expand particle size estimation techniques will further solidify their roles as robust tools in studying complex material systems.
# Bibliometric Trends in Industry 5.0 Research: An Updated Overview
## 1 Introduction  
Industry 5.0 (I5.0) is a new, innovative concept aimed at making the industry more \"sustainable, human-centered, and resilient\" [1]. As [PERSON] and [PERSON] [1], p. 1, point out, \"while a significant number of companies around the world are still trying to adapt to Industry 4.0, the discussion about Industry 5.0 has already begun\". The concept of Industry 5.0 originated from mid-2010s social media posts by [PERSON] and others [2; 3; 4; 5] and in the years thereafter these ideas were picked up by the academic community, with the first academic article published in 2016 [6]. Since the outbreak of the pandemic, I5.0 has received increasing levels of attention in business and society [7; 8]. In 2021, the academic literature on I5.0 was still in an embryonic phase but on a clear upward trajectory [9].  
Therefore, in this study, we provide an updated overview of bibliometric trends in the I5.0 literature. While there have been a few prior bibliometric analyses mapping different aspects of the early phase of I5.0 [9; 10; 11; 12], this research field is very fast-moving and is rapidly expanding. Therefore, it is useful to establish an updated picture, explore the scientific landscape, and develop an understanding and overview of the trajectory and hot topics in the scientific literature on I5.0. This is interesting because I5.0 resonates with the concept of sustainability through its broad stakeholder-oriented approach. As such, it intersects two influential contemporary trends in business and society: sustainability and digitalization.  
The structure of the paper is as follows: Section 2 offers a brief background on the concept of I5.0 and summarizes the findings of prior bibliometric studies, which provide acontext for comparison with the current study's results. Section 3 outlines the bibliometric methods and data used for the analysis. Section 4 presents the results of the citation-based bibliometric analysis, including overall trends and identification of key sources, documents, authors, and countries. Section 5 provides a thematic analysis of the I5.0 research field and an analysis of the evolution of research themes. Section 6 delves into the findings of the study and outlines potential avenues for future research. Lastly, Section 7 concludes the paper by emphasizing its contributions and reflecting on the study's limitations.