esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Design  
The experimental research presented in this section combined robotic machining technology with the CCOS process to achieve the high-efficiency and high-precision polishing of large-diameter aspheric optical parts. Figure 3 shows the experimental robotic polishing device for the CCOS process, which consisted of a six-degrees-of-freedom hybrid robot system, a polishing slurry supply and circulation system, a polishing head, and a computer numerical control (CNC) system. During the experiment, the polishing slurry was pressurized by the slurry supply pump and then injected into the center hole of the polishing tool, and the rotational speed, pressure, and motion trajectory of the polishing tool were controlled by the robot numerical control system. The K9 optical glass sample with a diameter of 120 mm and a curvature of 0.01198 was polished by cerium oxide polishing slurry with a concentration of 15% and a particle size of 1 \(\upmu\)m. A polyurethane polishing tool with a radius of 10 mm was used in the experiment, which had a revolution speedof 60 rpm, a rotation speed of 600 rpm, a load of 0.15 Mpa, and an eccentricity of 4 mm. The polished samples were measured with a Nanovea white-light interferometer with a sampling spacing of 20 \(\upmu\)m.  
To study the influence of the robot motion accuracy on the machining accuracy of the optical components, we carried out processing experiments using polishing tools with different normal errors for experimental groups A, B, and C. As shown in Figure 4, the polishing paths of the three groups of experiments were discretized into 11 \(\times\) 11 machining traces. The position and normal vector of each processing track point could be calculated according to the geometric features of the optical element to determine the polishing position and attitude control when the polishing robot scanned each track point. In the group A polishing experiment, the normal error of the polishing tool when the polishing robot scanned each track point was 0. In the group B polishing experiment, the normal error of the polishing tool at different track points was randomly assigned values between \(-0.5^{\circ}\) and \(0.5^{\circ}\). In the group C polishing experiment, the normal error of the polishing tool at different track points was also randomly generated, but its value was kept between \(-1^{\circ}\) and \(1^{\circ}\). Figure 5a,b present the random normal-error distributions generated for the group B and C polishing experiments, respectively.  
Figure 3: Experimental robotic polishing device for the CCOS process.
## 4 Discussion
When there is a big disparity in the properties of the impacting and receiving liquids, proper definition of these numbers become somewhat difficult. The Weber number characterizes the droplet stability due to surface tension effects, and it is logical to define it in terms of water properties; on the other hand, surface tension can also influence the melt flow. As a result, two Weber numbers can be introduced,\(\text{We}_{k}=\rho_{k}u^{2}D/\sigma_{k}\), where the subscript \(k=w\) denotes water, and \(k=m\) denotes melt. The Froude number \(\text{Fr}=u^{2}/gD\) characterizes the gravity effects and it is more relevant to the flow of the heavier liquid (melt). This number contains no fluid properties; however, it can be argued that the velocity \(u\) in the nominator must be the characteristic velocity acquired by the melt upon impact of water. In the case where the droplet and the receiving liquid possess identical or close densities, this velocity can be taken as equal to that of the impacting droplet; however, when the melt-water density ratio is as high as 9:1, it can be expected that the melt velocity is lower than the impact one. Proper scaling of these velocities is not known at present and should be the subject of special studies, both experimental and numerical. To elucidate the differences between the melt-water and water-water interactions, we present the values of the non-dimensional parameters (evaluated for water properties \(\rho_{w}=10^{3}\) kg/m\({}^{3}\), \(\sigma_{w}=72.8\) mN/m, and melt properties from Table 1). For the cases D1-D9 (see Table 2) the calculated ranges are \(\text{We}_{w}=517-720\), \(\text{We}_{m}=893-1243\), \(\text{Fr}=191-288\). For water-water interactions, the experiments generalized in [10] show that the lower boundary of the regime where a thick central jet is formed with a detaching droplet at its top, is described by \(\text{We}=63.1\cdot\text{Fr}^{0.257}\) (see Figure 8a, page 506 in [10]). The above range of Froude numbers corresponds to \(\text{We}=243-270\), which is well below both \(\text{We}_{w}\) and \(\text{We}_{m}\) quoted above. As was shown in the current experiments (see Section 3.1), the perturbations of the melt surface caused by the impact of free-falling droplets were rather weak, and despite the high Weber and Froude numbers, no central jet was registered in any of the cases, D1-D9. For the cases with jet impact, the corresponding non-dimensional parameters are listed in Table 5, together with the calculated boundary values of the thick central jet [10]. It can be seen that, generally, the experimental conditions correspond to high Weber and Froude numbers, well above the boundary \(\text{We}=63.1\cdot\text{Fr}^{0.257}\) obtained for the water-water interactions. Nevertheless, in Case J2 the impact was not strong enough to cause the formation of the central melt jet; a floating bubble was obtained instead (see Section 3.2).
# Multimodal Model for Automated Pain Assessment: Leveraging Video and fNIRS
## 4 Experiment and Results
### Visual Explanation Using Grad-CAM  
We employed Grad-CAM (Gradient-weighted Class Activation Mapping) [34] to visualize which regions of the input video frames most influenced the model's predictions. Grad-CAM computes the gradients of the target class score with respect to the convolutional feature maps and uses them to generate class-discriminative localization maps. These maps highlight the spatial regions that the model focuses on when making its decision.  
In our experiments, we applied Grad-CAM to the output of the MARLIN encoder to generate attention heatmaps for the pain classification task. As illustrated in Figure 8, the model consistently showed moderate attention to facial regions such as the eyes and mouth corners when classifying High Pain inputs. These regions became more pronounced and visually distinguishable when the subject exhibited clear facial expressions associated with pain. However, the overall Grad-CAM activations were relatively abstract and lacked sharp localization, suggesting that the model's attention was distributed rather than highly focused. In contrast, for No Pain inputs, the model displayed diffuse or minimal activation, often spreading attention across the entire face without clearly defined focal points. This indicates that the absence of distinctive facial features in neutral expressions may hinder the model's ability to confidently localize informative regions, contributing to lower discriminative capacity for this class.
## 6 Palladium
### Aryl Amination (C-N Coupling)  
[PERSON] is one of the more established researchers in the area of catalysis using supported organometallic complexes, and in a recent paper together with [PERSON] [72] he describes the development of a PS-PEG supported palladium catalyst for the coupling of aryl halides with anilines using the Buchwald-Hartwig aryl amination under aqueous conditions. PS-PEG-(dicyclohexyl)phosphine as well as PS-PEG-(di-_tert_-butyl)phosphine were prepared and pre-treated with di(u-chloro)bis(\(\eta^{3}\)-allyl)dipalladium(II) and subjected to the catalytic amination of aryl bromides. Only the (di-_tert_-butyl) substituted phosphine palladium complex was found to be efficient in the reaction, however. Coupling of aryl bromides with diphenylamine in general afforded the desired products in high yields (85-95%) if the aryl group was _meta_- or _para_-substituted, while a substituent in the _ortho_-position in general lowered the yield. Iodobenzene as well as chlorobenzene also worked well in the reaction. The double aylation of primary anilines using an excess of aryl bromide could also be effected with excellent results. To demonstrate the utility of the method, several TPD derivatives (TPD = (_N_,\(N\),_N_',_N_')-tetraphenyl-1,1'-biphenyl-4,4'-diamine), of interest as organic materials in optoelectronic devices, were prepared (one example is shown in Scheme 23). Metal-contamination is generally a problem in the preparation of TPDs, but by using a polymer-bound catalyst, leaching could be kept to a minimum.  
**Scheme 23.** Palladium-catalyzed aryl amination under aqueous conditions.  
[PERSON] and colleagues applied a palladium catalyst, incarcerated in a polymeric structure via microencapsulation and cross-linking, in the amination of aryl chlorides (Scheme 24) [73,74]. The copolymer used for preparing the support material was equipped with pendant bulky phosphines suitable for the aryl amination reaction. A wide range of aryl chlorides could be coupled with primary, secondary as well as anilinic amines in good yields and with a low degree of palladium leaching. No external phosphine was needed for the reaction to proceed. A brief mention will also be made of a study by [PERSON] and co-workers, who evaluated four different commercially available so called FibreCat catalysts, consisting of a polypropylene backbone with appended phosphine ligands coordinated to palladium [75]. The authors found that reused supported catalyst afforded more side products in ensuing reactions, most likely due to leaching of palladium and phosphine from the FibreCat system. Highest selectivity for the desired aryl amination product was formed using a supported triphenylphosphine/palladium(II) chloride catalyst.  
**Scheme 24.** Aryl amination using a polymer-incarcerated palladium catalyst.
## 4 Multi-Variable Thermal Resistances Network Modeling
### Relationships  
Table 3 shows that the separation distances between power components and working currents are the two key impact factors for thermal coupling resistances \(R_{cp-\widetilde{MD}}\) and \(R_{cp-\widetilde{DM}}\). The relationships between them can be described as Equation (7) and Figure 13 gives the relationships based on data curve fitting:  
\[\left\{\begin{array}{l}R_{cp-\widetilde{MD}}=\ f\ (d\,\ I)\\ R_{cp-\widetilde{DM}}=\ g\ (d\,\ I)\\ \end{array}\right. \tag{7}\]  
The relationships between thermal coupling resistances and their impact factors, \(d\) and \(I\), are detailed in Table 4 and summarized as follows.  
1. The values of \(R_{cp-\widetilde{MD}}\) and \(R_{cp-\widetilde{DM}}\) increase when the distance between power components increases, which means the coupling effect decreases when the separation distances increase, and vice versa.
2. The values of \(R_{cp-\widetilde{MD}}\) and \(R_{cp-\widetilde{DM}}\) decrease when the working currents increase, which means the coupling effect increases when the currents increase, and vice versa.
3. \(R_{cp-\widetilde{MD}}\) and \(R_{cp-\widetilde{DM}}\) have decreasing trends when the currents increase and the distances decrease, which means the coupling effect increases when the devices are brought closer with higher current magnitude, and vice versa.
# Optimization Method for Improving Efficiency of Thermal Field Reconstruction in Concrete Dam
###### Abstract
Specifically, the accuracy and efficiency of interpolation methods depend on three main factors: the interpolation algorithm, the number of monitoring points, and their positions. In terms of interpolation algorithms, various algorithms were proposed, and their differences in soil temperature [27], sea surface temperature [28], and air temperature [29] have been analyzed. However, the accuracy of these algorithms in concrete dams still lacks quantitative evaluation. Regarding the number and position of monitoring points, excessive data can increase computational complexity, while poorly arranged monitoring points can result in unrepresentative data [30]. Several optimization methods have been suggested: a comprehensive data symmetry analysis technique was presented for determining the optimal spatial positioning of valid temperature-measuring optical fibers [25]; the multi-population genetic algorithm was used to optimize the temperature measurement points [21]; and a temperature sensor layout method based on cross-validation principles was presented [24]. However, most of these studies focus on optimizing a single factor. The methods that simultaneously consider interpolation algorithms, the number of monitoring points, and their positions are rare in concrete dams. Additionally, temperature data collected via the DTS system is linear, following the optical fiber layout path [31; 32]. Therefore, when optimizing the efficiency of concrete thermal field reconstruction, it is essential to consider site construction conditions and actual fiber layout schemes. Therefore, this study proposes an optimization method for thermal field reconstruction in concrete dams, achieving a favorable balance between efficiency and accuracy. The method utilizes a DTS system for temperature monitoring in concrete. To ensure its validity and feasibility, the analysis is based on the actual optical fiber layout at the construction site. First, the accuracy of three commonly used interpolation algorithms--Kriging [33], Natural Neighbor [34], and Inverse Distance Weighting [35]--is quantitatively compared in the context of concrete thermal field reconstruction. Next, sensitivity analysis and control variable methods are applied to investigate the effects of the number and positions under a \"Z-shaped\" optical fiber layout scheme. The reconstruction results are integrated with a water-cooling system, and lightweight engineering application procedures are proposed to meet the requirements for real-time thermal field reconstruction and precise temperature control. Finally, the method was applied to a concrete dam, and the results showed that it can provide real-time and accurate data support for effective temperature control measures.
# A Novel Dual-Band Implantable Antenna for Pancreas Telemetry Sensor Applications
## 2 Methods and Results
### Radiation Performance Analysis  
#### 2.2.1 Resonance  
In order to understand the antenna behavior, resonance performance dependence on structure modifications was analyzed. For both frequencies, we requested that S11 resonance fell below \(-10\) dB.  
In the MedRadio band, before any structure modification, the antenna showed no resonance, as displayed in Figure 4. When the antenna turned into PIFA, we observed a resonance at 430 MHz. With every U-slot addition, the resonance improved, and the S11 reached \(-20.7\) dB. The 10 dB bandwidth was 30.29 MHz.  
The impact on the reflection coefficient in the ISM band operation is shown in Figure 5. Without adding any elements, we observed a possible resonance although it did not cover the \(-10\)dB requirement. After the structure modifications, the existing resonance optimized and S11 was \(-17.8\) dB at the operating frequency of 2.45 GHz. The bandwidth was 225.2 MHz.  
Overall, the proposed antenna performance on the desired operation frequencies satisfied the \(-10\) dB limit, indicated by the black line in Figures 4 and 5. In addition, the 10 dB bandwidth covered the MedRadio and ISM bands. In addition, at 402.5 MHz the real part of the impedance of the proposed antenna, extracted from our simulations, is 46.52 \(\Omega\) and the imaginary 8.19, and at 2.45 GHz the real part is 40.8 \(\Omega\) and the imaginary part is 7\(\Omega\) as displayed in Figures 6 and 7.
### Research Methodology
#### 1.1.1 Articles Selection Phase  
* **Database sources selection step:** Paper sources and key search parameters might have an impact on the quality of research. To achieve this, the papers in this review are taken from reliable databases: Web of Science ([[https://clarivate.com/webofsciencegroup/solutions/web-of-science/](https://clarivate.com/webofsciencegroup/solutions/web-of-science/)]([https://clarivate.com/webofsciencegroup/solutions/web-of-science/](https://clarivate.com/webofsciencegroup/solutions/web-of-science/)), accessed on 1 June 2021), Scopus ([[https://www.scopus.com/](https://www.scopus.com/)]([https://www.scopus.com/](https://www.scopus.com/)), accessed on 1 June 2021), IEEE Explorer ([[https://ieeexplore.ieee.org/](https://ieeexplore.ieee.org/)]([https://ieeexplore.ieee.org/](https://ieeexplore.ieee.org/)), accessed on 1 June 2021), and ACM digital library ([[https://dl.acm.org/](https://dl.acm.org/)]([https://dl.acm.org/](https://dl.acm.org/)), accessed on 1 June 2021). Moreover, only indexed journals are considered. The query search string and search keywords connected to the study subject are carefully chosen in order to conduct a good search that covers the most relevant material.
* **Articles selection and filtering step:** The search queries are made up of research-related words, brief phrases, and Boolean operators (ANDs and ORs). We carried out search by selecting one of the AI keywords with WSN keywords, along with WSN or Wireless sensor network as a keyword. The search results are limited to journal articles and the predefined range of years. Then all the search results are combined and filtered. The diagram in Figure 1 shows the whole processes of generating the query strings. Query search strings in abstracts, keywords, and titles are run on the specified databases to locate the core relevant publications from 2010 to 2021. Moreover, journal articles indexed in more than one database are included and other types are excluded. The resultant search terms are combined and filtered to choose the primary publications, excluding those that are not directly linked to our subject, duplicated, or of insufficient quality. Moreover, in order to determine the eligibility of the filtered articles to our targets first abstract is read and if it doesn't contain an indicator of its eligibility then the content of the article is investigated. Otherwise, the article is selected. Using this, 55 relevant research articles are chosen as primary papers where 13% of articles are selected from IEEE/ACM journals and 87% of articles are selected from other journal publishers. Figure 2 shows the number of selected journal research articles per year, along with the number of related and review articles per year (note that the selection criteria are not applied on the selection of related review paper).  
Figure 1: Query search string formation diagram.
# Influence of Metal Fear Tooth Geometry on Load and Wear within Metal-Polymer Fear Pairs
## 5 Determination of the Influence of the Gear Radii
### Resulting Combined Load and Wear  
The wear behavior of the pairing when using pinions with varying gear radii is investigated on the gear test rig. Figure 10 shows the calculated load on the tooth flank (a) and the resulting wear behavior (b).  
Figure 8: Influence of modified radii on active flank and targeted variation.  
Figure 9: Influence of gear radii on the tooth flank load.  
Compared to the reference geometry with a theoretical average contact ratio of 1.37, this is increased to 1.45 for minimum radii and reduced to 1.32 for maximum gear radii, leading to an increase of the line load in the area of the tooth root. Since there is a high sliding velocity in this area, the combined load is considerably increased locally (Figure 10a). When using the pinion with maximum radii, the maximum combined load in this area is 25.8 N/mm. With the reference pair, this is only 16.4 N/mm and with the minimum radius pinion, only 6.9 N/mm.  
The resulting wear behavior of the different pairings is shown in Figure 10b. The highest wear occurs within the pairing with the maximum radii pinion, whereas the lowest wear occurs with the minimum radii. Compared to the reference, the resulting total wear volume after 50 h (3.0 \(\times\) 10\({}^{6}\) cycles) rises from 1000.9 \(\pm\) 47.6 \(\upmu\)m\({}^{3}\) to 1132.1 \(\pm\) 53.4 \(\upmu\)m\({}^{3}\). In contrast, when using the pinion with minimum radii, the wear is only 894.2 \(\pm\) 42.9 \(\upmu\)m\({}^{3}\). The evaluation of the decrease of the tooth thickness shows that the increase in wear occurs in the area of elevated combined load.
# Iterative QR-Based SFSIC Detection and Decoder Algorithm for a Reed-Muller Space-Time Turbo System
## 2 Reed-Muller Space-Time Turbo System Model
It is to further devised that the suboptimum decoders, which are based on the LLR function, have both lower computational complexities and fast convergence rates, that serve as the motivation to write this paper.
# Static and Moving Target Imaging Using Harmonic Radar
## 3 Imaging of Moving Targets
### Moving Target Imaging Setup  
MTI data were taken in an anechoic chamber with the nonlinear target placed 4.6 m (15 feet) from the radar, as shown in the setup in Figure 5. To simulate a moving target, the nonlinear target was moved in 2.5-cm (1-inch) increments away from the radar. A commercial RF mixer, manufactured by Mini-Circuits, was used as the nonlinear target. The antenna was connected to the RF port (as is the usual case in a receiver front end), and the local oscillator (LO) and intermediate frequency (IF) ports were terminated in 50 ohms. The mixer was not driven into its nonlinear region since there was no LO power injected. The no LO injection case (our case) is more challenging because the nonlinear response of the mixer diodes is lower than if the diodes were biased due to the injection of LO power [62].  
An in-house built dual dipole antenna was used to get energy in and out of the mixer. Each time the target was moved, complex measurements at each of the 81 frequencies were taken for the linear and the nonlinear response. The second harmonic (\(n=2\)) range resolution was about 6 cm (15 inches), so moving the target 2.5 cm (1 inch) at a time kept it well within one range cell. Photographs from the test setup are shown in Figure 6.  
Figure 5: (**a**) Test setup for moving target imaging; (**b**) Photograph of the nonlinear target constructed.
# ADDAEIL: Anomaly Detection with Drift-Aware Ensemble-Based Incremental Learning
## 4 Results
### Datasets  
Three widely adopted time series anomaly detection datasets were applied in the experiment. They cover both synthetic and real-world scenarios, and reflect a range of concept drift characteristics.  
**Yahoo A3**[31]: A synthetic dataset designed to simulate complex anomalies and incremental drift. Frequently used for evaluating drift-aware models.  
**Numerta Anomaly Benchmark (NAB)**[32]: A benchmark for time series anomaly detection that contains 50 real and synthetic time series with labeled anomalies. It includes both sudden and incremental drift.  
**CEMACS**[33]: Real-world ocean surface temperature data recorded every 30 min over two years. It shows mild but long-term drift.  
To ensure consistent evaluation, a fixed window size of \(W=48\) is used across all datasets. This size aligns with the temporal resolution and periodicity of the datasets (e.g., daily or sub-daily cycles), making it suitable for detecting both abrupt and slow-evolving drift.  
The datasets used in our experiments are widely adopted in the literature for evaluating anomaly detection performance under evolving data distributions caused by concept drift [15; 34; 35; 36; 37]. Importantly, these datasets do not exclusively contain highly obvious anomaly points; some anomalies exhibit deviations that are relativity subtle and lie close to the typical variability range observed in normal data. For example, Figure 3 shows a time series from the Yahoo dataset. It illustrates two anomaly points: the first anomaly point exhibits a clear deviation from the normal data trend (a pronounced sudden drop), whereas the second anomaly point shows only a mild deviation, making it more difficult to distinguish it from natural fluctuations. This diversity of anomaly characteristics provides a valuable basis for comprehensively evaluating the capability of anomaly detection models under realistic streaming conditions.