esa-sceva/satcom-chunk-collection huggingface dataset:
# Derating of Electrical Contacts with Varying Surface Roughness
## 1 Introduction
In summary, micro-roughness increases contact resistance by reducing the real contact area between surfaces, and this increased resistance leads to greater heat dissipation. Theoretical models, such as those by [PERSON] and [PERSON], combined with empirical studies, provide strong evidence for this relationship. The resulting heat generation can degrade the contact during extended use, making thermal management an essential aspect of electrical contact design and reliability [3]. In summary, turning, as a mechanical machining method, significantly influences the derating of electrical contacts. This process shapes the surface and geometry of contacts, which in turn determines their electrical, mechanical, and thermal properties. While turning allows for precise manufacturing of components, the resulting micro-roughness, geometric deviations, and material defects can necessitate the reduction of maximum operating parameters of contacts to ensure their reliability and durability in real-world operating conditions. The derating curve illustrates the maximum continuous current that can flow through all possible connections of a component at varying ambient temperatures, up to its specified upper limit [26]. This upper limit temperature is defined by the materials used in the component. The sum of the ambient temperature and the temperature increase due to current flow (resulting from power loss in resistance) must not exceed this limit; otherwise, the component could be damaged or even completely fail [26]. As a result, the current-carrying capacity is not a fixed value--it decreases as the surrounding temperature rises. Additionally, factors like the component's shape, the number of poles, and the type of connected conductors also impact its capacity. To determine this capacity, empirical testing is conducted following the DIN IEC 60512-3 standard [26]. As illustrated in Figure 1, the resulting ambient-temperature profile follows the derating behaviour reported by [PERSON] [27]. On Figure 2, the values are represented on the figure using a linear coordinate system to visualize the relationship between current, ambient temperature, and the temperature rise within the component. The \(y\)-axis displays the loading currents, while the \(x\)-axis represents the ambient temperature of the component, where \(t_{g}\) is the upper limit temperature of component, \(t_{u}\) is the ambient temperature, and \(I_{n}\) is the current. If the derating curve surpasses the current limits in the low ambient temperature range--determined by the current-carrying capacity of the connected conductor cross sections--it should be adjusted to match the lower current value in that range. Figure 1: Resulting ambient temperatures. Figure 2: Resulting component temperatures.\(t_{g}\) = upper limit temperature of component; \(t_{u}\) = ambient temperature; \(I_{n}\) = current, a = base curve; b = reduced base curve (derating curve).
# Cooperative Localization Using Distance Measurements for Mobile Nodes
## 4 Proposed Algorithm Robust to Ambiguity and Noise
\begin{cases}\mathbf{\mathsf{M}}\Big{(}\widehat{\theta }_{R}\Big{(}\mathbf{\mathcal{S}}^{*},\Delta\mathbf{\mathcal{S}}^{\prime},\widehat{\mathbf{ \mathsf{D}}}^{\prime}\Big{)}\Big{)}\mathbf{\mathcal{S}}^{*},&\text{if }\left|\widehat{ \theta}_{R}\Big{(}\mathbf{\mathcal{S}}^{**},\Delta\mathbf{\mathcal{S}}^{\prime\prime}, \widehat{\mathbf{\mathsf{D}}}^{\prime\prime}\Big{)}\right|\leq|l|\\ \mathbf{\mathsf{M}}\Big{(}\widehat{\theta}_{R}\Big{(}\mathbf{\mathsf{F}}\mathbf{\mathcal{S }}^{*},\Delta\mathbf{\mathcal{S}}^{\prime},\widehat{\mathbf{\mathsf{D}}}^{\prime} \Big{)}\Big{)}\mathbf{\mathsf{F}}\mathbf{\mathcal{S}}^{*},&\text{if }\left|\widehat{ \theta}_{R}\Big{(}\mathbf{\mathcal{S}}^{**},\Delta\mathbf{\mathcal{S}}^{\prime\prime}, \widehat{\mathbf{\mathsf{D}}}^{\prime\prime}\Big{)}\right|>|l|\end{cases}  \tag{42}\]  
Algorithm 1 summarizes the algorithm to estimate locations of nodes with noisy measurements. ```
Result: Estimated locations of nodes Initialization; /* First step */
```  Collect \(\bar{d}_{i,j}\) for \(i,j=1,\cdots,n\) and estimate \(\widehat{\mathbf{D}}\) via (35);  Calculate \(\mathbf{\mathcal{S}}^{*}\) via MDS algorithm;  end while  begin /* Second step */  Let node 1 move to position \([\Delta x_{1}^{\prime},\Delta y_{1}^{\prime}]^{T}\);  Collect \(d_{i,j}^{\prime}\) for \(i,j=1,\ldots,n\) at the new position and estimate \(\widehat{\mathbf{D}}^{\prime}\);  Calculate \(\widehat{\theta}_{R}\big{(}\mathbf{\mathcal{S}}^{*},\Delta\mathbf{\mathcal{S}}^{\prime },\widehat{\mathbf{D}}^{\prime}\big{)}\) according to (38);  end while  begin /* Third step */  Let node 1 move to position \([\Delta x_{1}^{\prime\prime},\Delta y_{1}^{\prime\prime}]^{T}\);  Collect \(d_{i,j}^{\prime\prime}\) for \(i,j=1,\ldots,n\) at the new position and estimate \(\widehat{\mathbf{D}}^{\prime\prime}\);  Calculate \(\widehat{\theta}_{R}\big{(}\mathbf{\mathcal{S}}^{**},\Delta\mathbf{\mathcal{S}}^{ \prime\prime},\widehat{\mathbf{D}}^{\prime\prime}\big{)}\) according to (38) and (39) using variables \(\mathbf{\mathcal{S}}^{**}\), \(\Delta\mathbf{\mathcal{S}}^{\prime\prime}\) and \(\widehat{\mathbf{D}}^{\prime\prime}\);  Use (42) to estimate the locations, i.e., \(\widehat{\mathbf{\mathcal{S}}}\);  end while if\(\left|\widehat{\theta}_{R}\big{(}\mathbf{\mathcal{S}}^{**},\Delta\mathbf{\mathcal{S}}^{ \prime\prime},\mathbf{D}^{\prime\prime}\big{)}\right|<|l|\)then  Determine locations of nodes via \(\mathbf{F}\) and (21)-(23); else  Determine locations of nodes via (30);  end ```  
**Algorithm 1**Algorithm to estimate locations of mobile nodes.
# Cascaded Regression-Based Segmentation of Cardiac CT under Probabilistic Correspondences
###### Abstract  
The creation of 3D models for cardiac mapping systems is time-consuming, and the models suffer from issues with repeatability among operators. The present study aimed to construct a double-shaped model composed of the left ventricle and left atrium. We developed cascaded-regression-based segmentation software with probabilistic point and appearance correspondence. Group-wise registration of point sets constructs the point correspondence from probabilistic matches, and the proposed method also calculates appearance correspondence from these probabilistic matches. Final point correspondence of group-wise registration constructed independently for three surfaces of the double-shaped model. Stochastic appearance selection of cascaded regression enables the effective construction in the aspect of memory usage and computation time. The two correspondence construction methods of active appearance models were compared in terms of the paired segmentation of the left atrium (LA) and left ventricle (LV). The proposed method segmented 35 cardiac CTs in six-fold cross-validation, and the symmetric surface distance (SSD), Hausdorff distance (HD), and Dice coefficient (DC), were used for evaluation. The proposed method produced 1.88 \(\pm\) 0.37 mm of LV SSD, 2.25 \(\pm\) 0.51 mm* of LA SSD, and 2.06 \(\pm\) 0.34 mm* of the left heart (LH) SSD. Additionally, DC was 80.45% \(\pm\) 4.27%****, where * \(p<\)0.05, ** \(p<\)0.01, and *** \(p<\)0.001. All p values derive from paired \(t\)-tests comparing iterative closest registration with the proposed method. In conclusion, the authors developed a cascaded regression framework for 3D cardiac CT segmentation.  
**Keywords:** cascaded regression; group-wise correspondence construction; cardiac CT; heart segmentation +
Footnote †: journal: Applied Sciences
## 1 Introduction  
_1.1. From Automation to Augmentation: The Rise of Industry 5.0_  
The rise of Industry 5.0 marks a shift from automation-driven systems to a more human-centric industrial paradigm. While Industry 4.0 emphasized digitalization, the Internet of Things (IoT), and cyber-physical systems to boost efficiency, it often resulted in increased cognitive and operational demands on human workers [1]. In contrast, Industry 5.0 emphasizes collaboration between humans and intelligent machines, aiming for enhanced adaptability, resilience, and sustainability [2]. It envisions empowering workers through systems that are not only precise but also empathetic and responsive to human needs [2].  
A compelling example from [PERSON] et al. (2023) illustrates this need: in a smart factory, an operator collaborating with a collaborative robot (cobot) on a pick-and-place task that incorporated a Stroop-style secondary task experienced a marked increase in missed-beep errors due to high task complexity and a cobot speed of 1 m s an overload that NASA Task Load Index (NASA-TLX) scores registered only partially and less sensitively than neural measures [3]. A multimodal approach using electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) data predicted missed-beep events with 77.8% accuracy, enabling the timely detection of cognitive overload and suggesting adaptive support. This highlights the critical demand for dynamic, real-time assessment tools [3], motivating the development of the proposed Multimodal Hidden Markov Model (MHMM). Operators in this evolving context must interpret complex real-time data, interact with cobots, and manage unexpected disruptions, tasks that demand continuous cognitive, physiological, and behavioral engagement [4]. These dynamics call for AI systems that are human-aware, capable of supporting operators in real time, and fully aligned with the adaptive and inclusive vision of Industry 5.0 [5].
# Preparation of Polyacrylate Hollow Microspheres via Facile Spray Drying
## 2 Materials and Methods  
A high-speed centrifugal spray dryer (model LPG-8, Changzhou Lima Drying Engineering Co., Ltd., Changzhou, China) was used. A conventional polyacrylate emulsion was used as the model emulsion for this project to prepare polyacrylate hollow spheres, while other polymer emulsions should work similarly effectively. First, a polyacrylate emulsion with a solid content of 33 wt % and latex particle size of 30-50 nm was prepared by using methyl methacrylate acrylate (MMA)/butyl acrylate (BA)/acrylic acid (AA) (85/15/3 in mass ratio) as commoners (MMA, BA, and AA were all industrial grade and obtained from Guangzhou Guanglin Chemical Co., Ltd., Guangzhou, China) and using ammonium sulfate allyloxy nonylphenoxy poly(ethyleneoxy)(10) ether (DNS-86, industrial grade, Guangzhou Shuangjian Trade Co., Ltd., Guangzhou, China) as emulsifier via semi-continuous emulsion polymerization [21]. The glass transition temperature (T\({}_{\mathrm{g}}\)) of the synthesized polyacrylate was characterized to be 83 \({}^{\circ}\)C by differential scanning calorimetry (DSC) at a heating rate of 10 \({}^{\circ}\)C/min.  
The synthesized polyacrylate emulsion was filtered with a 200 mesh filter and transferred into a centrifugal spray drying tower (Figure 1) using a peristaltic pump (model BT300, Changzhou Purui Fluid Technology Co., Ltd., Changzhou, China). The emulsion was subsequently converted into small droplets by a high-speed atomizer (model DPG-5, Xiangsu Xinglun Electromechanical Equipment Co., Ltd., Taizhou, China) because of the atomization effect. The formed small droplets were in full contact with the hot air in the tower body, and were dried into microspheres after being heat-exchanged along their transportation path, and then separated by a cyclone separator. The microspheres were finally collected in a receiving tank and filtered through a 100-mesh filter and then sealed. The entire process is briefly shown in Figure 1. The resultant microspheres were characterized by scanning electron microscopy (SEM) and laser diffraction. For SEM, the polyacrylate microspheres were coated with a thin gold layer before being imaged using a HITACHI S-3400N scanning electron microscope (HITACHI, Hitachinaka, Japan) operated at a working voltage of 20 kV. For laser diffraction, the microspheres were dispersed in deionized water to prepare a dilute suspension (0.1-1.0 wt %) with the assistance of ultrasonication, and subsequently characterized on a Malvern Laser Particle Sizeer (Mastersizer 3000, Malvern, UK). Three specimens of each sample were characterized, and the average results were recorded.
# Correction of Directional Effects in VEGETATION NDVI Time-Series
## 3 Methodology
### NDVI Computation  
In addition to the surface properties, the reflectances are also largely dependent on the specifications of the sensor spectral bands defined by the spectral response functions (SRFs). This means that there is not a true NDVI for a specific surface, but only an NDVI according to sensor-specific SRFs e.g., [36; 37; 38]. Consequently, the VGT1 NDVI is spectrally corrected to VGT2 NDVI (It corrects for the fact that the SRF of VGT1 in the RED band overlaps considerably with the transition zone from leaf absorption to foliage reflectance, which was adapted in the design of VGT2 and PROBA-V), by correcting the BRDF-normalized RED reflectance according to the relationship below:  
\[RED_{VGT2}=\left(\left(a+b\ NDVI_{VGT1}+c\ NDVI_{VGT1}^{2}+d\ NDVI_{VGT1}^{3} \right)\frac{RED_{VGT1}}{100}\right)+RED_{VGT1} \tag{12}\]  
With \(a=0.277052\), \(b=-13.1103\), \(c=33.03465\), \(d=-41.0406\), and NDVI calculated from the uncorrected bands. The methodology used to derive these numerical coefficients is detailed in [20]. The VGTPV NDVI is not spectrally corrected to VGT2 NDVI, because both NDVI data sets have already a good matching that could not be improved by the estimated correction functions. Thus, the final step is the computation of NDVI by Equation (1).
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Article (Total Number of Cases)** & **Intramucosal Nevi** & **Junctional Nevi** & **Compound Nevi** & **DBN** & **CBN** & **ABN** & **EBN** & **Dysplastic Nevi** & **Spitz Nevi** & **Combined** \\ \hline [PERSON] et al. (1991)[13] & 105 & 10 & 12 & 61 & 0 & 0 & 0 & 0 & 03 \\ \hline [PERSON] et al. (1991)[11] & 96 & 05 & 07 & 10 & 0 & 0 & 0 & 0 & 01 \\ \hline [PERSON] et al. (190)[9] & 61 & 03 & 07 & 23 & 02 & 0 & 0 & 02 & 02 \\ \hline [PERSON] & 04 & 01 & 02 & 03 & 0 & 0 & 0 & 0 & 0 \\ \hline [PERSON] et al. (71)[16] & 30 & 03 & 09 & 29 & 0 & 0 & 0 & 0 & 0 \\ \hline [PERSON] et al. (30) & 19 & 0 & 03 & 08 & 0 & 0 & 0 & 0 & 0 \\ present study & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Literature review-based subtypes of OMN from a series of cases published to date. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **Intramucosal Nevi** & **Common Blue Nevi** & **X\({}^{2}\) Test** \\  & **(\(\mathbf{n}\) = 282) (\%)** & **(\(\mathbf{n}\) = 128) (\%)** & **X\({}^{2}\) Test** \\ \hline
**Age in years** & & & & \\ \(<\)20 & 20 (7.1) & 15 (11.7) & \(p\) = 0.007 \\
21–50 & 203 (72) & 72 (56.2) & \\ \(>\)51 & 59 (20.9) & 41 (32.1) & \\ \hline
**Gender** & & & & \\ Male & 82 (31.8) & 47 (44.8) & \multirow{2}{*}{\(p\) = 0.019} \\ Female & 176 (68.2) & 58 (55.2) & \\ \({}^{\star}\) Unknown & 24 & 23 & \\ \hline
**Site** & & & & \\ Hard palate & 88 (31.4) & 85 (68.0) & \multirow{2}{*}{\(p\) = 0.000} \\ Gingiva & 63 (22.5) & 02 (1.6) & \\ Buccal mucosa & 59 (21.1) & 16 (12.8) & \\ Vermillion border/labial mucosa & 70 (25.0) & 22 (17.6) & \\ \multicolumn{2}{c}{02} & 03 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Literature review-based comparison of clinico-pathological characteristics of intramucosal nevi compared with common blue nevi. The literature review also revealed information on the size of the lesions, with the majority of the lesions, including the present series, providing evidence that more than 95% of the OMN, including the DBN, are smaller than 1 cm in their greatest dimension (Table 1) [9; 13; 14]. There is evidence to show that epithelioid, cellular and atypical blue nevi may also rarely occur in the oral cavity [9; 28; 29; 30; 31; 32; 33; 34; 35].
## 1 Introduction
### Deployment Strategies  
Sensors can be put either deterministically or randomly in a region of concern [16]. Deployment system selection is extremely dependent on the type of sensors, the implementation and the environment in which the sensors will function. Controlled node deployment is feasible and often necessary when sensors are costly or when their position affects their operation considerably. Such situations involve populating an area with extremely accurate seismic nodes, WSN apps underwater and video and image sensors. On the other hand, random node allocation is the only viable alternative in some apps.  
* Controlled node deployment: Controlled deployment is usually pursued in indoor applications of WSNs [17].
* Random sensor deployment: Randomized positioning of sensors is often the only choice. For instance, in WSN apps in battle recognition tasks, disaster recovery and forest fire detection, sensor deterministic deployment is highly dangerous and/or unfeasible. It is commonly anticipated that helicopters, grenade launchers or clustered bombs will drop sensors. Such deployment implies random sensor spread although to some extent, node density can be monitored. While substantial progress has been made in investigating node positioning optimization in WSNs, many challenges remain [10].  
**Sleep Scheduling Mechanism:** Sleep scheduling implies the proportion of the wake-up moment in a predefined period to the complete duration of the period [18]. For instance, if one node remains active for 0.1s and sleeps for 0.9s for a period of 1s, then the ratio is 0.1. Sleep planning processes for WSNs can usually be categorized into three classifications.  
* Synchronous Schemes: In synchronous systems such as S-MAC and T-MAC, sleeping nodes wake up at the same moment to interact with each other regularly, which implies that the network must maintain global synchronization.
* Semi-Synchronous Schemes: Sensor nodes are usually grouped into clusters concerning semi-synchronous systems. Sensor nodes wake up or go to sleep simultaneously in the same cluster, but clusters behave asynchronously with others.
* Asynchronous Schemes: Each sensor node has its wake-up and sleep timetable in terms of asynchronous systems.  
The purpose of a WSN sleep schedule is to save the WSN's energy consumption. Here, we list some possible instructions [19].  
* Mobility for relay and mobility WSNs: The nodes consume energy which further leads to an energy hole problem. The mitigation for this problem is possible with the help of a mobile relay and sink. Similar to a mobile robot, the mobile relay or mobile sink can move around to collect data, providing a nice trade-off between energy consumption, latency and delay in shipment.
* Clustering process for WSNs: For semi-synchronous sleep scheduling schemes, the network is generally divided into several clusters, and cluster heads are responsible for interacting with other clusters. The cluster heads may need to consume more energy compared to normal sensor nodes [20].
# Model Based Open-Loop Wind Farm Control Using Active Power for Power Increase and Load Reduction
## 4 Discussion
### Strengths and Weaknesses of the Current Study  
The strengths of the current study can be summarized as the following two points. First, the current study considered the actual application of the wind farm control algorithm to the real wind farm, and proposed a simple but effective open loop wind farm control which can be fully applied to a wind farm controller (hardware) and used without affecting the current wind turbine control algorithms. The wind farm controller receives the measured generator speed, blade pitch angle, and the power output from the wind turbine controller and sends the optimal power command to the wind turbine controller. In addition, the proposed wind farm control algorithm is validated in a relatively high-fidelity wind farm simulation tool [20], which has wind turbine dynamics and control including the basic torque and pitch controller slightly modified to include the demanded power point tracking (DPPT) control [37]. Therefore, the current wind farm simulation is almost the same as the real wind farm in terms of control. In addition, the wind farm control frequency applied was at least 10 times slower than the wind variation in the simulation to get more realistic simulation results. Parametric studies including turbulence intensities, turbine spacing and wind farm control frequencies were made to see the effects of active induction control on the total power increase in some more detail. This study also has some weaknesses and they are as follows. The wake model used in this study is [PERSON]'s eddy viscosity model which uses the thin boundary layer approximation on the RANS (Reynolds Averaged Navier-Stokes) equation with the empirical boundary condition obtained from the wind tunnel test. Although the model or its modified version is commonly used in commercial software for calculating the power outputs of a single or multiple wind turbines including the wake effect, the accuracy of the model needs to be experimentally validated with various wind turbine operating conditions. In addition, in this study, the wake meandering is not considered. If the meandering is considered, the power decrease in the wake region might be changed and, therefore, the performance of the proposed wind farm control algorithm will be affected. Therefore, in the future, experimental validation of the simulation results needs to be performed.
## 3 Results
### Regression Models with All Parameters  
#### 3.3.1 UAV Regression Models  
For the UAV regression models, the first modelling step used all 28 variables including: the five MicaSense band reflectances, 14 VIs, plant height, soil moisture, LAI, topographic slope, aspect, profile curvature, plan curvature, TWI #1, and TWI #2. Single-date and combinations of multi-date datasets were tested to evaluate the temporal effect on models. A proportion of 70% of each dataset was used to calibrate the RF and SVR models, before the remaining 30% was used for validation. From the calibrated models (Table 5), RF had better performance in comparison to SVR; specifically, it had higher R\({}^{2}\) and lower RMSE values overall. The top model performance was that of RF with the combination of 12, 20, and 27 May resulting in an R\({}^{2}\) of 0.96 and an RMSE of 1.07 g/m\({}^{2}\). Although the 20/27 May and 4 June model had a slightly higher R\({}^{2}\) of 0.97, the RMSE was considerably greater at 1.76 g/m\({}^{2}\). Of the RF and SVR models, that of 20 May had the lowest performance. Of the SVR models, 12, 20, and 27 May model had the best performance with an R\({}^{2}\) of 0.87 and an RMSE of 2.07 g/m\({}^{2}\). Figure 4: Variation of canopy nitrogen weight (g/m\({}^{2}\)) as a function of the date of field measurements during the 2020 growing season. An outlier is represented by a dot on the graph. The UAV models were then applied to the validation datasets (Table 6). All single date models for RF and SVR had low performance. This was likely due to the small number of sample point data from single date sets resulting in the calibration model not encompassing normal field data variation. The top performing model was RF with the 12, 20, and 27 May data, resulting in an R\({}^{2}\) of 0.74 and an RMSE of 2.76 g/m\({}^{2}\). Overall, RF had better performance than SVR and multi-date models had better performance than single-date models. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Date** & **Model** & **R\({}^{2}\)** & _p-Value \({}^{1}\)_ & **RMSE (g/m\({}^{2}\))** & _(n)_ \\ \hline \multirow{2}{*}{5 May} & RF & 0.18 & NS & 0.71 & 10 \\  & SVR & 0.06 & NS & 0.71 & 10 \\ \hline \multirow{2}{*}{12 May} & RF & 0.07 & NS & 0.95 & 15 \\  & SVR & 0.14 & 0.1 & 0.79 & 15 \\ \hline \multirow{2}{*}{20 May} & RF & 0.05 & NS & 2.28 & 15 \\  & SVR & 0.18 & 0.1 & 2.14 & 15 \\ \hline \multirow{2}{*}{27 May} & RF & 0.25 & 0.1 & 4.35 & 10 \\  & SVR & 0.03 & NS & 5.05 & 10 \\ \hline \multirow{2}{*}{4 June} & RF & 0.20 & 0.1 & 4.35 & 15 \\  & SVR & 0.06 & NS & 5.66 & 15 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Statistics for the validation of the UAV canopy nitrogen models with 28 variables as a function of the date and modelling approach (RF or SVR).