esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Axial Modification on the Meshing Performance of Involute Developid Gear Pair
## 1 Introduction
[PERSON] introduced the tool with parabolic modification into the design of crossed leveloid gear pairs and investigated the influence of the modification parameter on meshing performance, including transmission error, meshing stiffness, contact force, and other factors [16]. [PERSON] constructed a gear pair model of meshing between an intersected leveloid gear and a cylindrical gear and investigated the impact of varying modification quantities and techniques on contact marks, transmission error, and root stress [17]. [PERSON] developed a time-varying meshing stiffness solution model for beveloid gears based on the slicing method, and the time-varying meshing stiffness was calculated and verified using the improved Weber energy method [18]. [PERSON] proposed a numerical design method to enhance the meshing performance of crossed leveloid gears through modification [19]. The following studies have been conducted on the machining of thickened gears: [PERSON] studied the processing of beveloid gears and provided the relation of gear and processing parameters, all of which give a convenient for mass production of beveloid gears [20]. [PERSON] investigated a method for processing non-involute beveloid gears using common gear grinding machines, analysing the impact of grinding parameters on the modified tooth surfaces and optimising the shape parameters and grinding wheel feed to achieve the ideal profiles [21]. [PERSON] proposed a hobbing method that modifies ordinary hobbing machines by incorporating a hanging wheel mechanism with a synchronous belt drive system [22]. Involute beveloid gears are utilized in numerous applications, and the research system is highly efficient. However, to date, there has been a scarcity of research on the shaping of the involute beveloid gear. The objective of this paper is to improve the meshing performance of the beveloid gear by means of axial modification. This involves the establishment of a mathematical model of the beveloid gear, which takes axial modification into account based on the principle of gear shaping. Additionally, the influence of varying modification parameters on the meshing performance before and after axial modification will be examined. The aim is to establish a comprehensive theory of shaping for paralleled involute beveloid gears, thereby facilitating the advancement and application of the beveloid gear.
# Soft Semi-Supervised Deep Learning-Based Clustering
## 4 Experiments
### Results and Discussion  
As outlined above, extensive experiments were conducted to assess the performance of the proposed approach, SC-DEC. The results were quantitatively analyzed, visualized, and compared to those achieved using relevant unsupervised and semi-supervised deep clustering algorithms. Namely, the proposed approach was compared with DEC [21], Semi-Supervised Deep Embedded Clustering (SDEC) [38], and Improved Deep Embedded Clustering with Local Structure Preservation (IDEC) [37]. Additionally, we added the results of applying simple k-means [8] for clustering the deep embeddings of the respective datasets (called DL + k-means). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**MNIST**} & \multicolumn{2}{c}{**USPS**} & \multicolumn{2}{c}{**STL-10**} \\ \hline
**\# of Pretraining epochs** & **ACC** & **\
\begin{tabular}{c} **\# of Pretraining Epochs** \\ \end{tabular} & **ACC** & **
\begin{tabular}{c} **\# Pretraining Epochs** \\ \end{tabular} & **ACC** \\ \hline
20 & 83.44\% & 20 & 74.45\% & 20 & **91.02\%** \\ \hline
30 & 83.51\% & 30 & 75.73\% & 30 & 78.67\% \\ \hline
50 & 85.54\% & 50 & **76.62\%** & 50 & 77.62\% \\ \hline
100 & 85.45\% & 100 & 75.16\% & 100 & 62.25\% \\ \hline
200 & 86.08\% & 200 & 75.15\% & 200 & 74.98\% \\ \hline
300 & **89.00\%** & 300 & 75.04\% & 300 & 66.87\% \\ \hline
400 & 87.00\% & 500 & 75.78\% & 500 & 66.58\% \\ \hline
1000 & 85.47\% & 1000 & 74.70\% & 1000 & 66.52\% \\ \hline
2000 & 87.41\% & 2000 & 60.82\% & 2000 & 65.25\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance achieved via the proposed approach using different numbers of AE pretraining epochs on the different datasets. Bold formatting indicates the best obtained results. \begin{table}
\begin{tabular}{l c c c} \hline \hline \multicolumn{1}{c}{**Hyper-Parameter**} & \begin{tabular}{c} **Default Value** \\ **MNIST** \\ \end{tabular} & \
\begin{tabular}{c} **Default Value** \\ **USPS** \\ \end{tabular} &
\begin{tabular}{c} **Default Value** \\ **STL-10** \\ \end{tabular} \\ \hline Number of AE pretraining epochs & - & - & - \\ \hline Data batch size & 256 & 256 & 256 \\ \hline Gamma \(\gamma\) & 1 & 1 & 1 \\ \hline Beta \(\beta\) & 1 & 1 & 1 \\ \hline Fuzzifier \(m\) & 2 & 2 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Hyper-parameters considered for hyper-parameter tuning along with the default values. ```
1:Input: Dataset \(\mathbf{X}\); hyper-parameter set considered for tuning: {number of AE pretraining epochs; \(\mathbf{\gamma}\): the constraint term weight; \(\mathbf{m}\): the fuzzifier; \(\mathbf{\beta}\): number of pairwise constraints; data batch size}; default values of the hyper-parameters from Table 5; random sets of seed values. 2:OUTPUT: Hyper-parameters values that achieve the highest accuracy, per dataset. 3: STEP1: Tuning of the (number of pretraining epochs) hyper-parameter.
# Technical Interoperability for Machine Connectivity on the Shop Floor
## Background
### Technical Interoperability and Enabling Technologies
Frameworks for CPS and IoT often include a middle layer that connects objects and applications through services [31; 32; 33]. This is called a Service Oriented Architecture (SOA), whose principle is to connect business functions (services) between providers and consumers by using service locators or service brokers [34]. A definition of a service, therefore, needs to account for business and technical perspectives as well as the consumer and provider perspectives. However, from the technical perspective, a service is an encapsulation of functionality [35]. SOA is often used synonymously with Web-services, but this is only one implementation. Web service API's (Application Programming Interface) often comply with the Representational State Transfer (REST) style that implies stateless interactions and a hierarchical resource representation [36]. SOA is also supported by the above-mentioned communication protocol for industrial automation, OPC UA. New communication protocols have also been developed to support ubiquitous and sometimes limited hardware, such as Message Queuing Telemetry Transport (MQTT) and Constrained Application Protocol (CoAP) [37; 38]. Interconnection of all the ubiquitous equipment that is assumed in the IoT paradigm requires a fast, robust, and scalable connectivity platform. The next generation open telecommunication standard of cellular wireless technologies, 5G, is estimated to be commercially available around 2020. As in previous cellular telecom standards, 5G is developed within the 3G Partnership Project (3 GPP) framework, building on components from 4G/LTE, the prevalent telecom standard. Some of the key characteristics of 5G are higher data rates, shorter delays, and high reliability [39]. Requirements integrated into the new standards reflect the needs of tomorrow, i.e., \"a fully mobile and connected society\" [40]. Ongoing standardization efforts have defined several scenarios and connected them with sets of requirements [1]. These requirements need several solutions, most of which already partially exist [41].
## 3 Results
### Commercial Sprays
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Brand** & **Pigment** & **Composition** & **Raman Bands** \\ \hline \multirow{4}{*}{Violet (ZT131)} & & 317, 416, 484, 528 s, 591, 672, 724, 748, 921, 934, 990, 1108, \\  & & Dioxazine (PV23) & 1122, 1131, 1152, 1167, 1208 s, 1258 s, 1334 s, 1348 s, 1394 \\  & & vs, 1433 s, 1445, 1593 cm\({}^{-1}\) \\ \cline{2-3}  & & Alkyd binder, most likely & 1003, 1042 cm\({}^{-1}\) \\  & & CHS-Alkyd S 471 & 1003, 1042 cm\({}^{-1}\) \\ \hline \multirow{4}{*}{Fuchsia (ZT134)} & & 317, 485, 529, 592, 614, 681, 726, 746, 812, 922, 935, 1110, \\  & & Dioxazine (PV23) & 1133, 1165, 1208, 1260, 1335, 1349, 1394 s, 1433, 1447, \\  & & 1461, 1581 s cm\({}^{-1}\) \\ \cline{2-3}  & & Alkyd binder, most likely & 1004, 1042, 1601, 1726 cm\({}^{-1}\) \\ \cline{2-3}  & & Unidentified & 347, 442, 1062, 1231, 1284, 1359, 1484, 1553 cm\({}^{-1}\) \\ \hline \multirow{2}{*}{Bordeaux (ZT148)} & & 348, 431, 529, 575, 620, 682, 720, 747, 814, 967, 1063, 1163, \\  & & 1231, 1245, 1358, 1375, 1393, 1403, 1485, 1555, 1581 cm\({}^{-1}\) \\ \cline{2-3}  & & Alkyd binder & 651, 1004, 1729 cm\({}^{-1}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._but with an incomplete identification. In the XRF, it was also clear that the composition of both sprays was different even though the same pigment was present. In this sense, violet presented zinc, and fuchsia presented chlorine and chrome. The binder used could be identified in many of the sprays. In all cases, they appeared to be alkyl binders [6; 21; 22], among the most common binders used in art due to their low cost, fast drying, and good optical properties. In this sense, the most repeated binder identified was Alkyd S471, with a 1042 cm\({}^{-1}\) Raman band related to phthalate and a 1004 cm\({}^{-1}\) band related to isophthalate [22]. Nevertheless, other alkyld binders were observed, with very similar Raman spectrum to S471 but slightly different, with the most relevant modifications being the appearance or disappearance of one of these two mentioned characteristic bands (1004 or 1042 cm\({}^{-1}\)), or the movement of the 1725-1732 cm\({}^{-1}\) broad band, but related in any case to alkyld binders [22]. Taking all this into account, all the selected sprays could be identified, and although the complete composition was not achieved, information for pigments and binders was obtained, as well as for other additional compounds. This data increases the public database, with additional information to the usual information available as the brand or elemental composition, emphasizing the need to compile databases of real sprays and not just pure pigments. Moreover, all of this was important to do in a total open-access way, so the spectra of these studies are accessible in the Supplementary Materials (Table S1). Furthermore, as occurred with the PG7 pigment, it is important to provide not only the bands, but also the spectra, since, given the complexity of the spectra, discrepancies can be found in the literature, and without the raw spectra, the comparison is more difficult.
## 3 Results and Discussions
### Outcomes of Scheme II  
In scheme II, the model was evaluated within subject. The TBLSTM model's performance of the five subjects was shown in Figure 7. Where the blue solid curve was the measured RVSBP values, and the red solid curve was the estimated RVSBP values. It was seen that the estimated RVSBP followed the measured RVSBP very well. The MAEs and the SDs were also listed in Table 2 for further analysis. The outcomes showed that although the features were not extracted by hand, we used the Bi-LSTM model which can track changes of the RVSBP with low MAE and SD. Hence, RVSBP can be accurately predicted even when not using the HS features.
## 9 Electronics
### Power Board
#### 9.1.2 Optical Attenuation Circuit  
This subsystem is responsible for supplying and controlling the optical attenuator. The optical attenuator requires a square signal with a frequency of 2 kHz and voltage levels ranging from \(-10\) to 10 V. The subsystem is divided into four blocks:  
Block A: This block generates a 2 kHz signal using a 555 timer configured in astable mode. The 555 timer's output is connected to the base of an NPN-type BJT transistor, which resets the output to zero each time the 555 timer output goes high. The choice of the 555 integrated circuit is due to its robustness, low cost, and ease of implementation. The 2 kHz signal is generated using the following formulas:  
\[t1=0.693\times R1+R2\times C \tag{10}\]  
\[t2=0.693\times R2\times C \tag{11}\]  
where:  
* \(t1\) is the positive time interval;
* \(t2\) is the negative time interval;
* \(0.693\) is a constant;
* \(R1\) and \(R2\) are the resistors to calculate;
* \(C\) is the capacitor value.  
Block B: This block incorporates a 12-bit resolution DAC controlled via I\({}^{2}\)C. It operates at 5 V, similar to the 555 timer, and produces a voltage at its output, linked to the input of the differential amplifier in Block C, through I\({}^{2}\)C frames. This output voltage's amplitude is configurable via I\({}^{2}\)C communication. Block B also features two 2.2 k\(\Omega\) resistors configured as _Pull Up_ for the I\({}^{2}\)C bus, along with a similar resistor at the DAC's output.  
Block C: This block adapts the signal for the optical attenuator. It combines the signals received from the 555 circuit and the DAC output. The outcome is a 2 kHz square signal modulated in amplitude by the DAC output. A 100 k\(\Omega\) potentiometer in the inverting amplifier enables fine adjustments, ensuring a maximum V\({}_{\text{DAC}}\) amplitude of 20 V\({}_{\text{pp}}\), suitable for the optical attenuator. The resulting signal maintains a 2 kHz frequency and a 20 V\({}_{\rm pp}\) amplitude, with variations based on the V\({}_{\rm DAC}\) amplitude controlled through I\({}^{2}\)C communication from the control board's microcontroller.
## 2 Materials and Methods
### Proposed Framework Architecture
#### 2.3.1 AI for Predictive Maintenance
AI-based predictive maintenance has significantly lowered equipment downtime and streamlined campus operations by enabling data-driven asset management [16]. By uniting IoT-based monitoring with analytics-driven forecasting, the framework transforms maintenance from a reactive task into a strategic, cost-effective initiative aligned with the broader goals of a smart campus.
# Web3 DGIS-Based System for Reservoir Landslide Monitoring and Early Warning
###### Abstract  
Landslides are the most frequent type of natural disaster, and they bring about large-scale damage and are a threat to human lives and infrastructure; therefore, the ability to conduct real-time monitoring and early warning is important. In this study, a Web3 DGIS (Web3D geographic information systems) system for monitoring and forecasting landslides was developed using the Danjiangkou Reservoir area as a case study. The development of this technique involved system construction, functional design, organizing and managing multi-source spatial data, and implementing a forecasting plan and landslide-forecasting model. By integrating sensor technologies, spatial information technologies, 3D visualization technologies, and a landslide-forecasting model, the results of this study provide a tool for real-time monitoring at potential landslide sites. When relevant data from these sites reach threshold values, the model automatically initiates forecasting procedures, and sends information to disaster prevention sectors for emergency management.  
Keywords:landslide monitoring; landslide early-warning; natural disasters; Web3 DGIS +
Footnote †: journal: Applied
# Modeling and Simulation of Autonomous DC Microgrid with Variable Droop Controller
###### Abstract  
The emergence of highly efficient and cost-effective power converters, coupled with the growing diversity of DC loads, has elevated the importance of DC microgrids to a level comparable with AC microgrids in the modern power industry. DC microgrids are free from synchronization and reactive power dynamics, making them more reliable and cost-effective. In autonomous mode, achieving effective voltage regulation and satisfactory power sharing is critical to ensuring the overall stability of the microgrid. As the common DC bus of the microgrid connects distributed generators (DGs), storage devices, and loads through power electronic converters (PECs), the controllers of these PECs must regulate the bus voltage effectively, track reference signals to meet power demands, and enable satisfactory load sharing. In this work, a real time decentralized droop controller is implemented for an islanded DC microgrid to enhance the voltage regulation at the DC bus and current sharing efficacy between the sources subject to load transients. A novel control strategy is presented in which the conventional droop control is modified considering the load dynamics. The performance of the proposed control strategy is compared with the conventional voltage droop control strategy. The fluctuations in the DC bus voltage, which is the major cause of voltage instability of the DC microgrid is effectively reduced by the proposed strategy. The proposed strategy is validated by comparing it with the conventional fixed droop control method on the MATLAB Simulink platform. The variable droop control strategy outperforms the fixed droop method by addressing sudden voltage fluctuations in the DC bus, which occur due to the inherent load current dependency of the fixed droop approach. This technique achieves enhanced voltage regulation, which is crucial for microgrid stability.  
DC microgrid; variable droop control; decentralized control; voltage regulation +
Footnote †: journal: Applied sciences
# Dual-Tracer PET Image Separation by Deep Learning: A Simulation Study
## 4 Results
### Bias and Standard Deviation Analysis (ROI-Level)  
Finally, a comparison between the proposed DL-based method (with 3 training reruns, 80 training pairs) and the CM-based method (10 reruns with different random initial values of the kinetic parameters) was conducted for \(\Omega_{\text{TM}}\) at the ROI level. To investigate the impact of the noise for the CM-based method at the ROI level, a full width at half maximum (FWHM) Gaussian kernel was used to denoise the separated images as a post-processing step. Figure 13 shows that the average \(\text{SD}_{\text{ROI}}\) of the CM-based separation results were sufficiently reduced by introducing an FWHM kernel after separation, and lower average bias was obtained for longer \(\Delta t\) for the CM-based method with post-denoising. However, using a large FWHM kernel (8 mm) leads to higher average bias. The average \(\text{SD}_{\text{ROI}}\) of the DL-based method stays at a similar level for all \(\Delta t\), showing as the aleatoric uncertainty. For both [\({}^{18}\)F]FDG and [\({}^{11}\)C]MET, the average \(\text{bias}_{\text{ROI}}\) of the DL-based method is comparable to the CM-based method with a 4 mm FWHM kernel for different \(\Delta t\), which again indicates the DL-based method implicitly denoises the separated images as discussed in Section 4.1. For simultaneous injection (\(\Delta t\) = 0 min), the DL-based method overall delivers better separations at the ROI level than the CM-based method (with and without post-denoising) in terms of lower average \(\text{bias}_{\text{ROI}}\) and average \(\text{SD}_{\text{ROI}}\). However, the CM-based method can still potentially surpass the DL-based method for \(\Omega_{\text{TM}}\) at the ROI level by introducing an FWHM kernel (2-4 mm) as post-denoising for \(\Delta t\geq 5\) min.
###### Abstract  
Land Use and Land Cover (LULC) classification is vital for environmental and ecological applications. Sentinel-2 is a new generation land monitoring satellite with the advantages of novel spectral capabilities, wide coverage and fine spatial and temporal resolutions. The effects of different spatial resolution unification schemes and methods on LULC classification have been scarcely investigated for Sentinel-2. This paper bridged this gap by comparing the differences between upscaling and downscaling as well as different downscaling algorithms from the point of view of LULC classification accuracy. The studied downscaling algorithms include nearest neighbor resampling and five popular pansharpening methods, namely, Gram-Schmidt (GS), nearest neighbor diffusion (NNDiffusion), PANSHARP algorithm proposed by [PERSON], wavelet transformation fusion (WTF) and high-pass filter fusion (HPF). Two spatial features, textural metrics derived from Grey-Level-Co-occurrence Matrix (GLCM) and extended attribute profiles (EAPs), are investigated to make up for the shortcoming of pixel-based spectral classification. Random forest (RF) is adopted as the classifier. The experiment was conducted in Xitiaoxi watershed, China. The results demonstrated that downscaling obviously outperforms upscaling in terms of classification accuracy. For downscaling, image sharpening has no obvious advantages than spatial interpolation. Different image sharpening algorithms have distinct effects. Two multiresolution analysis (MRA)-based methods, i.e., WTF and HFP, achieve the best performance. GS achieved a similar accuracy with NNDiffusion and PANSHARP. Compared to image sharpening, the introduction of spatial features, both GLCM and EAPs can greatly improve the classification accuracy for Sentinel-2 imagery. Their effects on overall accuracy are similar but differ significantly to specific classes. In general, using the spectral bands downscaled by nearest neighbor interpolation can meet the requirements of regional LULC applications, and the GLCM and EAPs spatial features can be used to obtain more precise classification maps.  
Sentinel-2; LULC classification; downscaling; pan-sharpen; GLCM; EAPs 2017 9, 1274; doi:10.3390/rs9121274
# Resolving Gas Bubbles Ascending in Liquid Metal from Low-SNR Neutron Radiography Images
###### Abstract  
We demonstrate a new image processing methodology for resolving gas bubbles travelling through liquid metal from dynamic neutron radiography images with an intrinsically low signal-to-noise ratio. Image pre-processing, denoising and bubble segmentation are described in detail, with practical recommendations. Experimental validation is presented--stationary and moving reference bodies with neutron-transparent cavities are radiographed with imaging conditions representative of the cases with bubbles in liquid metal. The new methods are applied to our experimental data from previous and recent imaging campaigns, and the performance of the methods proposed in this paper is compared against our previously achieved results. Significant improvements are observed as well as the capacity to reliably extract physically meaningful information from measurements performed under highly adverse imaging conditions. The showcased image processing solution and separate elements thereof are readily extendable beyond the present application, and have been made open-source.  
dynamic neutron imaging image processing; denoising; segmentation; low signal-to-noise ratio (SNR); bubble flow; liquid metal; two-phase flow; metohydrodynamics (MHD)
# Reversible Data Hiding in Encryppted Images with Extended Parametric Binary Tree Labeling
## 3 Proposed EPBTL-RDHEI
### Image Encryption and Pixel Grouping
#### 3.1.1 Prediction Error Calculation  
Our approach shares the same framework as IPBTL [27]. The top row and left column of the original image are selected as the reference pixels. The values of the remaining pixels are predicted based on the reference pixels. The median-edge detector (MED) [12] is applied to predict pixel values in the raster scan order. The prediction of MED is illustrated in Figure 5. The pixel marked '\(x\)' is predicted using pixels '\(a\)', '\(b\)', and '\(c\)'. The predicted value \(p_{x}\) is given by  
\[p_{x}=\left\{\begin{array}{c}\max(b,c),a\leq\min(b,c),\\ \min(b,c),a\geq\max(b,c),\\ b+c-a,otherwise.\end{array}\right. \tag{2}\]  
The prediction error is therefore given by  
\[e=x-p_{x}. \tag{3}\]  
Figure 6 shows an example of image \(I\) to calculate the prediction error. The subsequent demonstrations of detailed operations in Section 3 are based on this original image \(I\).