esa-sceva/satcom-chunk-collection huggingface dataset:
# Spread Spectrum Image Watermarking Through Latent Diffusion Model
## 3 Experiments  
### Experimental Settings  
To evaluate our scheme, we adopt two public datasets, i.e., MS-COCO [24] and DiffusionDB [25]. We randomly sample 200 images sized \(512\times 512\) from each dataset. The pre-trained stable diffusion model v2.1 [20] and the DDIM sampler [19] with a sampling time step of 50 are employed as the generator of watermarked images. For the weight factors of the loss function, \(\gamma_{1}\), \(\gamma_{2}\) and \(\gamma_{3}\) are set to 10, 1, and 0.1, respectively. The Adam optimizer [26] is employed with an initial learning rate of \(10^{-2}\) for 30 epochs. The learning rate is then decayed at a rate of 0.3 at the 31 st and the 81 st epoch. The framework is implemented by PyTorch 2.0.1 and trained on a single NVIDIA RTX 4080. The inference time of our model for watermarking an image is 404.87 s, and the time cost for watermark extraction is about 2.5 s per image. As robustness is one of the primary goals of watermarking techniques, the bit error rate (BER) is employed to evaluate the robustness of watermarks under various attacks. Some common attacks are considered, e.g., brightness or contrast adjustment, JPEG compression (Q = 50), Gaussian noise with \(\sigma\) = 0.05, blur with a filter size of 5, crop with a ratio of 0.5, and BM3D denoising [27]. Considering the latest attacks from deep generative models, VAE-based image compression with Q = 3 [28,29] and regeneration attack [16] are launched. For imperceptibility evaluation, we choose three metrics, i.e., PSNR, SSIM, and LPIPS [30]. Comparisons are made with four state-of-the-art (SOTA) deep learning-based methods HiDDeN [4], Stegastamp [5], SSL [10], and StableSignature [12]. All methods set the watermark bit length of 32, except that stable signature uses the pre-trained model for 48 bits.
# Enabling Technologies for Operator 4.0: A Survey
## 4 IoT-Based Solutions to Support Operator Activities
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Type of Operator 4.0** & **Type of Sensor** & **Examples** \\ \hline \multirow{2}{*}{Analytical operator} & Infra-red sensors & Discover and predict events [102] \\ \cline{2-3}  & Olfactory sensors & Electronic nose [124] \\ \hline \multirow{4}{*}{Augmented operator} & Microphones & Capturing voices and the location of speakers [125] \\ \cline{2-3}  & & Machine vision systems for quality inspection [126; 127] \\ \cline{2-3}  & Visual sensors & Image processing, e.g., panoramic images [128], create the environment of virtual reality [129] \\ \cline{2-3}  & & Smart camera for probabilistic tracking [130] \\ \hline \multirow{4}{*}{Collaborative operator} & \multirow{4}{*}{Localization sensors} & IPS in manufacturing [95] and hybrid locating systems [131] \\ \cline{2-3}  & & Mapping and localization using RFID technology [132] and efficient object localization using passive RFID tags [133; 134] \\ \cline{1-1} \cline{2-3} Social operator & & Smart and social factories based on the connection between machines, products and humans [135] \\ \hline \multirow{4}{*}{Smarter and healthy operator} & \multirow{4}{*}{Wearable sensors} & Smart watch with embedded sensors to recognize objects [136] \\ \cline{1-1} \cline{2-3}  & & The smart glove maps the orientation of the hand and fingers with the help of bend sensors [137] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Sensors of Operator 4.0 solutions. detailed information about the type and location of risk. The potential applications of these solutions are summarized in the last column of the table. Some companies have been testing these innovative technologies in manufacturing processes. In every case when these techniques are used, the production process is complex, the quality management is strict and there is a wide variety of products. The results are impressive because the efficiency improves while the learning time reduces in every observed situation. In the following, some of these solutions will be introduced. Smart glasses-based augmented reality is used in the manufacturing of high-horsepower wheeled tractors with hundreds of variations by the company AGCO [146]. Presently, 100 pairs of glasses are in use to visualize the next manufacturing step and necessary information for the inspection process. The results in numbers are promising:  
* 50% reduction in learning time (in the case of new workers)
* 30% reduction in inspection time (eliminates paperwork and manual upload)
* 25% reduction in production time (in the case of complex assemblies and low volumes)  
Similar advantages of smart glasses were reported at DHL, which is one of the leading logistics companies in the world [154]. Ten workers who used smart glasses for three weeks managed to
# Multi-Probe Measurement Method for Error Motion of Precision Rotary Stage Based on Reference Plate
## 1 Introduction
These systems provide the substantial benefit of concurrently assessing multi-degree-of-freedom spindle error motions in a single measurement. Nonetheless, they exhibit certain practical constraints: the optical path is exceedingly vulnerable to environmental disturbances, and the systems have intricate installation and calibration processes, eventually leading to diminished measurement efficiency. In summary, the development of a reliable and effective in situ measurement method for tilt error motion is highly significant. Building upon the analysis of the conventional three-probe error separation principle, this study introduces an innovative multi-probe measurement method for the error motion of a precision rotary stage utilizing a reference plate. Experimental validation accurately quantified both tilt and axial error motions. The synchronous measurement error does not exceed 4.69%, indicating the reliability and practical applicability of the method for industrial precision measurement applications. The proposed multi-probe measurement method significantly enhances the feasibility of in situ error motion measurement while establishing a novel research paradigm for precision motion metrology. Its capability to simultaneously deliver laboratory-grade accuracy and high-repeatability performance markedly extends the evaluation limits for precision rotary stage technologies.
# Analysis and Optimization of the Stray Capacitance of Rogowski Coils
## 5 Conclusions  
The stray capacitance \(C_{c}(=C_{AB})\) is one of the main factors affecting the high frequency performance of Rogowski coils. Reducing the value of \(C_{c}\) can expand the bandwidth and improve the high frequency performance of the coils.  
The value of the lumped stray capacitance \(C_{AB}\) is approximately proportional to the length of a single-turn winding \(l_{t}\) (which is approximately equal to the perimeter of the skeleton cross-section \(l\)); thus, the stray capacitance can be reduced via minimizing \(l\). Therefore, this manuscript is devoted to the optimization of the shape of the skeleton cross-section of Rogowski coils, so that \(l\) is minimized without affecting the total magnetic flux as well as the low frequency performance of the coils.  
The stray capacitance of a Rogowski coils of rectangular cross-section skeleton is minimized when the length \(b\) and the width \(a\) satisfy \(b=(R_{1}+a)\ln(1+a/R_{1})\), where \(R_{1}\) is a pre-given inner radius of the skeleton. Similar optimization approach can be applied to the more realistic oval cross-section. On the other hand, the shortest perimeter that minimizes the stray capacitance under fixed magnetic flux does not exist for a circular cross-section skeleton.  
Finally, under the condition that the total magnetic flux and the inductance of the coil are fixed, explicit formulations of the boundary of the optimal cross-section of the skeleton are deduced by solving a conditional variational problem. Numerical results  
Figure 16: Step responses of Rogowski coils with various cross-sections.  
Figure 15: Frequency response of Rogowski coils around lower and upper cutoff frequencies.  
demonstrate that the proposed optimization of the cross-section can effectively reduce the stray capacitance and resistance, leading to optimal bandwidth and dynamic response of Rogowski coils. The oval cross-section, as a combination of the rectangular and circular cross-sections, surprisingly outperforms the latter two options and exhibits very close performance to that of the proposed optimal cross-section.  
Conceptualization, [PERSON] and X.M.; Data curation, [PERSON]; Funding acquisition, [PERSON]; Methodology, [PERSON], H.W. and X.M.; Project administration, [PERSON]; Software, M.M.; Supervision, [PERSON]; Validation, J.W. and M.M.; Writing--original draft, [PERSON] and H.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key R&D Program of China under Grant 2021 YFB2401700, and National Natural Science Foundation of China under grant 52207014.  
Not applicable.  
Data Availability Statement: Data are available upon reasonable request to the corresponding author.  
The authors declare no conflicts of interest.
## 1 Introduction
Another effective strategy is the use of multivariate models, which establish linear or nonlinear relationships between the element of interest and the matrix elements, thereby reducing matrix effects [18; 19; 20]. Soil classification is another successful approach, followed by developing calibration curves for each class. [PERSON] et al. [21] emphasized significant matrix effects in quantification models and recommended classifying soil samples and creating a quantification model for each class. The phase transformation method introduced by [PERSON] et al. [22] involves a solid-liquid-solid transformation process that theoretically eliminates matrix effects. Additionally, [PERSON] et al. [20] implemented a comprehensive data processing sequence that includes log transformation of the spectrum, outlier removal using the median absolute deviation (MAD) method, denoising via Wavelet Transform, baseline removal, and the application of multivariate models. In this research, we evaluated the capability of LIBS for quantitative analysis on diverse soil samples through the calibration curve method. We determined the overall concentrations of As, Mn, and Pb, and these findings were compared with those acquired through ICP-MS. The differences in soil samples' physical and chemical properties, like particle size, heterogeneity, and organic matter content, made it hard to determine an appropriate internal standard for the analyzed soils. Therefore, the methodology chosen included the construction of a calibration curve and the normalization of spectral intensity to the overall integrated signal of plasma emission. In our approach, signal improvement under ambient conditions involves using an inert gas, specifically argon (Ar). With its lower conductivity and specific heat compared to air, argon has a lower breakdown point [23]. Under an Ar gas environment, the spectral intensity of heavy metals (HMs) has demonstrated a doubling effect. Additionally, argon promotes larger and more stable plasma emissions while reducing the self-reversal effect, resulting in a more uniform plasma core temperature [24]. Improving LIBS sensitivity involves either increasing signal intensity or reducing background noise from the plasma [1]. The primary objective of this study is to enhance LIBS signal performance for the quantitative analysis of HMs in soil samples collected from the Superfund site in North Birmingham. The results obtained from LIBS were compared with those from ICP-MS to validate the accuracy and reliability of the experimental results. The major novelty is the demonstration of LIBS's utility for monitoring heavy metal toxic substances in soil samples at levels below EPA regulatory importance to protect both public health and the environment in North Birmingham.
# Mitigating the Scintillation Effect on GNSS Signals Using MP and ROTI
## 2 Materials and Methods
### Mp  
Multipath is a type of interference to GNSS receivers which is caused by reflected signals. MP1 and MP2 are parameters defined by [PERSON] and [PERSON] [30] to quantify the multipath effect. In this study, MP1 and MP2 are generated using the quality control (QC) command of the TEQC software on the Receiver Independent Exchange Format (RINEX) version 2.11 files from the SAO0P station, including both observation and navigation files [31].
## 1 Introduction
### Contribution  
In order to achieve massive scalability and connectivity, we aim to maximize the minimum success probability of all distant LDs and optimize SF allocation in all SF zones of the IoT LoRa network within a predetermined duty-cycle limitation.  
The detailed contributions of this paper can be summarized as follows.  
1. We provide an SF-based network partitioning method to determine the best partitioning threshold point for dividing SF zones of an IoT LoRa network into the LSFZ and the HSFZ. To bridge the performance gap among the SF zones, an optimal SF division method based on a heuristic algorithm is proposed.  
2. We provide a density-based subspace clustering method for grouping adjacent LDs in the HSFZ. To construct clusters of arbitrary shape and to select CHs by a binary score representation, a combination of the DBSCAN algorithm and naive Bayes classifier is proposed.
3. We provide the relay LD selecting method to ideally choose the best relay LD only in the LSFZ for extending transmissions of CHs in the HSFZ to the GW by multihop communications. The harmonic mean based on the packet success probability and the remaining energy is proposed to choose the best one among all candidate relay LDs.
4. We maximize the performance of packet success probability, scalability, and fairness in the SF zones of an IoT LoRa network. To ensure the optimal zone allocation with connection fairness, the packet success probability of each LD in a certain SF zone is examined.
5. We conduct simulations in various environments for evaluating the performance of the proposed SFPCR scheme. Simulation results demonstrate that the proposed SFPCR scheme raises the packet success probability by bridging the packet delivery success probability gap between SF zones while conserving more energy among LDs by reducing the number of packets compared with existing schemes.
# Predictive Speed-Control Algorithm Based on a Novel Extended-State Observer for PMSM Drives
###### Abstract  
To enhance the control performance of permanent-magnet synchronous motor (PMSM) drive systems, achieving high-precision motion control, a generalized predictive control (GPC) method based on a novel extended-state observer (ESO) is investigated for the speed control of PMSM. In this paper, the controller design consists of two steps. Firstly, according to the continuous time model of PMSM, using the Taylor series expansion, the predictive value of motor speed in finite time is derived, and the single-loop speed controller by combining the speed loop and \(q-\)axis current loop is obtained through the defined cost function. The structure of the controller is simple compared to other forms. Secondly, considering the uncertainty of the load torque and the model uncertainties, a novel extended-state observer is designed to compute the actual torque, and the observed value is introduced to the GPC controller. The simulation and experimental results show that the proposed GPC+ESO control method has superior dynamic performance and strong robustness.  
generalized predictive control; novel extended-state observer; PMSM drives Article
## 4 Theoretical Motivation of NBR-Clustering
### NBR Clustering Framework
#### 4.1.1 Transforming Point Data into Graphs  
In applying the NBR-Clust framework to point datasets, we first convert the data into a k-nearest neighbor (kNN) graph \(G\). In a kNN graph \(G_{k}\), vertices \(u\) and \(v\) have an edge between them if is amongst the \(k\) closest vertices to \(u\) with respect to the distance metric considered. While any distance metric may be used to determine nearness of neighbors, we use the \(n\)-dimensional Euclidean distance, where \(n\) is the number of features considered. Min-conn \(k\) implies choosing the minimal \(k\) such that \(\forall\ k^{\prime}\geq k\ \forall_{(u,p\in V)}\ \exists\ u\text{-}v\) path in G\({}_{k^{\prime}}\). The choice of kNN graphs is motivated by [6; 44] and empirical evidence from [15; 19]. Given that NBR measures are meaningful only on connected graphs (the measure VAT has been extended in [10], such that the VAT of a disconnected graph can be considered to be the minimum VAT of any of its components), the smallest \(k\) for which the graph remains connected is chosen. An example of a synthetic point set utilized in this work and its corresponding minimum connectivity kNN graph are shown in Figures 3 and 4, respectively.
## 3 Results
### Integrative Analysis of DAFs and DEGs  
To further explore the relationship between DEGs and DAFs, a correlation analysis was conducted on the common DEGs and DAFs among the three ecotypes. The correlation network between DEGs and DAFs revealed that there were nine core DEGs involved in regulating naringenin and naringenin chalcone synthesis, including _AsqAEX006535-CHS_, _AsqAEX016074-CAH_, _AsqAEX010035-CHI_, _AsqAEX004011-4 CL_, _AsqAEX016790-bHLH181_, _AsqAEX001711-MYB12_, _AsqAEX015868-bHLH60_, _AsqAEX015868-bHLH62_, and _AsqAEX 002220-MYB1R1_ (Figure 6). Among them, _AsqAEX002220-MYB1R1_ markedly negatively regulated the synthesis of both naringenin and naringenin chalcone, while _AsqAEX006535-CHS_, _AsqAEX016074-CAH_, _AsqAEX004011-4 CL_, _AsqAEX001711-MYB12_, and _AsqAEX015868-bHLH62_ markedly positively regulated the synthesis of naringenin (Table S5). Furthermore, _AsqAEX002220-MYB1R1_, _AsqAEX015868-bHLH62_, and _AsqAEX001711-MYB12_ were identified as key TFs that regulated _AsqAEX006535-CHS_, _AsqAEX016074-CAH_, and _AsqAEX004011-4 CL_ to promote the accumulation of naringenin. Among these, _AsqAEX015868-bHLH62_ and _AsqAEX001711-MYB12_ were positively associated with _AsqAEX006535-CHS_, _AsqAEX016074-CAH_, and _AsqAEX004011-4 CL_, whereas _AsqAEX002 220-MYB1R1_ was significantly negatively correlated with these structural genes.  
The flavonoid biosynthesis pathway in sandrice encompassing the 14 compounds detected was constructed according to the DEGs and DAFs (Figure 7). The pathway of sandrice flavonoid biosynthesis initiated with the transformation of phenylalanine to p-coumaoyl-CoA. Subsequently, a series of the enzymes were induced by cold stress to synthesize various stable flavonoids. These enzymes included five out of ten copies of 4 CL, one C4H, five out of eight CHSs, three CHIs, seven out of ten FNSs, three out of four F3 Hs, three out of four FLSs, two out of three CYP75B1s, two out of three UGT76B1s, one out of two OMTs, one out of two ANRs, and one LAR. The upstream pathways of flavonoid synthesis remained consistent across the three ecotypes, whereas the downstream pathways exhibited variability.  
Figure 4: KEGG enrichment analysis bubble map of the three ecotypes of sandrice. (**A**) The bubble map of common up-regulated 1125 DEGs; (**B**) the bubble map of common down-regulated 1478 DEGs. CP, GIP, M., and OS. represent Cellular Processes, Genetic Information Processing, Metabolism, and Organismal Systems, respectively. Dot size represents the number of DEGs, and dot color reflects the \(p\)-value.  
Figure 5: The hierarchical cluster heatmap of structure genes and TFs related to flavonoid synthesis in sandrice. **(A)** Classification of 85 flavonoid synthesis-related genes; **(B)** 85 genes related to flavonoid synthesis; **(C)** 19 common structure genes related to flavonoid synthesis; **(D)** 85 _hHLHs;_ **(E)** 20 common differences in _hHLHs;_ **(F)** 78 _MYBs;_ **(G)** 13 common differences in _MYBs_.
## 8 Conclusions
### Current Limitations of KANs  
The data-driven tool wear modeling based on KANs exhibits several limitations. First, while KANs can derive explicit mathematical formulas compared to black-box models, these derived results do not fully align with conventional physical principles.  
Second, KAN-A/B/C primarily focuses on achieving low training and testing errors within the given dataset, with limited incorporation of physical understanding. For instance, in tests on public datasets, KANs identified cutting speed (Speed) as a non-critical parameter. It does not negate the role of cutting speed but rather reflects its relatively weak influence within the specific data range used.  
Furthermore, the mathematical relationships derived from KAN-A/B/C are strictly valid only within the training data range. Due to the absence of physical constraints in the symbolic formulas, extrapolation beyond these bounds may lead to inaccurate predictions. Compared to traditional MLPs, the optimization of B-spline basis functions and edge-based activation learning increases the computational overhead in KANs.
# Modeling and Resolving Uncertainty in DIKWP Model
## 4 Problem Processing
### Semantic Space
#### 4.3.3 Operation and Application  
Based on the relevant definitions and concepts of the Semantic Space, we attempt to analyze and address the issues faced by financial firms in executing the legislation regarding HFT mapped into the Semantic Space as discussed in the previous section. Here, we focus on analyzing the issue of \"inconsistency in execution due to content interpretation bias\" within the Semantic Space.  
* We define a semantic unit \(v_{SemALawUB}\) to represent interpretation bias, and \(E_{SemAlaw}\) represents the associations and dependency relationships between the semantic unit, which belong to the legal Semantic Space denoted as \(Graph_{SemALaw}\): \[Graph_{SemALaw}=(V_{SemALaw},E_{SemAlaw})\] (27)  
Figure 4: A case of Semantic Space for identification of an HFT firm.  
* We can use query operations to retrieve units of inconsistency in the execution process: \[Query(V_{SemALaw},E_{SemAlaw},q_{UBias})\rightarrow\{v_{SEmALAwLIB}\}\] (28) where condition \(q\) is interpretation bias in law.
* The addition operation can be utilized to enrich the Semantic Space of legal understanding: \[Add(V_{SemALaw},v_{SEmALAwUC})\] (29) where \(v_{SEmALAwUC}\) represents semantic units reflecting accurate legal comprehension.
* Furthermore, the Semantic Space can also be refined through update operations, as illustrated by the following equation. \[Update(V_{SemALaw},v_{SEmALAwUC},v_{SEmALawUB1},e_{UBias})\] (30) where \(e_{UBias}\) is comprehending bias and the purpose of this operation is to establish new semantic units, \(v_{SEmALAwUB1}\), representing the understanding biases existing alongside the accurate legal comprehension \(v_{SEmALAwUC}\).  
The Semantic Space not only aids in identifying and resolving issues encountered in the execution of the Dodd-Frank Act but also provides a methodological approach to clarify the interpretation and application of the act through precise semantic operations. This allows for the identification of misunderstandings, ambiguities, and semantic units in execution, and facilitates continuous optimization and updates through new understandings, enriching the Semantic Space. Such an approach not only helps clarify ambiguous sections of the act but also fosters effective communication among different stakeholders regarding the interpretation and implementation of the act, ensuring regulatory compliance and transparency.