esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of Advanced Medical Telerobots
## 1 Introduction
### Introduction of Robotics in Medicine  
The rise of robotics in medicine has opened up broad perspectives and suggests further progresses, not only related to the precision and comfort of surgeons but also related to the benefits for patients (reduction of invasiveness, recovery time, reduction in pain, and side effects). The medical robot remains a cooperative device for the practitioner, the sole leader on board. Moreover, humans cannot be replaced by robots in the context of medicine (in particular surgery) due to several factors, some of which relate to the need for extremely high cognitive-sensorimotor skills of the clinicians (e.g., surgeons) and imperative medical domain knowledge which cannot be replaced (this is a controversial topic). Medical doctors should be at the heart of the operation because they can integrate complex information from multiple sources and conduct the operation based on their complex medical knowledge related to the physiological and pathological context of the operation. They also possess not only the ability to analyze a rare situation and make critical decisions but also the ability to adapt, even improvise in rare cases. For all these reasons, it matters to keep the practitioners as much as possible in the loop while trying to augment their cognitive and sensorimotor competence using multimodal telerobotic systems.  
In the context of surgery, the environment is usually the organs of the patient on which the surgery is being operated. This is the feedforward information path. As the feedback path, the operator will receive sensory information from the environment about the interaction between the follower robot and the environment, through various sensory channels, e.g., haptics, visual, auditory. The sensory information replicated by the leader robot for the operator allows her/him to conduct the operation based on transparent situational awareness achieved using the received sensory information [2; 20; 21; 22; 23]. Medical applications of various extensions of conventional topology have also been explored in the literature and will be discussed in this paper in Section 2.
# End-to-End Multi-Modal Speaker Change Detection with Pre-Trained Models
## 4 Experiments
### Results
#### 4.6.2 Results of eMD\({}_{att}\) Model  
Tables 5 and 6 show the results of the **eMD\({}_{att}\)** model. It applies a self-attention layer only to the multi-modal and audio-only configuration, as previous experiments showed that adding self-attention to the text-only models did not improve performance. In the freeze (F) mode (Table 5), where feature extraction layers remain unchanged, the audio-only setup outperforms the text-only setup. This suggests that audio inherently provides more informative cues for speaker change detection than text. The multi-modal setup performs best, leveraging both modalities to achieve the highest Hit Rates across all meetings.  
In the non-freeze (NF) mode Table 6, where feature extraction layers are fine-tuned, overall performance improves. The text-only model sees a reduction in FAR from 87-92% to 77-87%, and MDR drops to 9.07% in the best case. The audio-only model continues to outperform the text-only model, with FAR decreasing to 16.80% (IS1009c) and 17.23% (IS1009b), and MDR remaining below 14%. The multi-modal approach remains the most effective, maintaining FAR around 20% and MDR between 9 and 16%. The multi-modal setup achieves the most significant improvements across the most imbalanced meetings (TS3003c, IS1009c, ES2004b). Fine-tuning substantially reduces FAR, dropping from 57.93% to 21.06% in TS3003c, from 76.07% to 14.04% in IS1009c, and from 78.26% to 21.97% in ES2004b. These results suggest that utilizing both text and audio in a fine-tuned model can help reduce the impact of class imbalance, making multi-modal processing a promising approach for speaker change detection in imbalanced scenarios.  
The precision, recall, and F1-score for the **eMD** and **eMD\({}_{att}\)** models are reported in Appendix A.  
Figure 3 compares the performance of freeze (F) and non-freeze (NF) models across three setups of the **eMD\({}_{att}\)** model: text-only, audio-only, and multi-modal. In Figure 3a, the stacked bar chart shows the average FAR and MDR. The hatched bars represent FAR, while the solid bars represent MDR. The light blue bars correspond to freeze mode, and the red bars correspond to non-freeze mode. It is clearly shown that **eMD\({}_{att}\)** uses the attention layer; the fine-tuned model (NF) reduces both FAR and MDR significantly compared to the freeze model. For example, the multi-modal model with freeze mode achieves a high FAR of 69.81% and a low MDR of 10.54; however, the fine-tuned (the red bar) trade-off between these two rates reduces the false alarm from 69.81% to 21.78%. Figure 3b reports the F1-score for these models, and it shows that the highest F1-score 73.39% was obtained by the multi-modal model with NF.  
Figure 2: Comparison of freeze (F) and non-freeze (NF) models across text-only, audio-only, and multi-modal configurations of **eMD** model: (**a**) Average FAR and MDR (%) Comparison: F vs. NF; (**b**) Average F1-score (%) Comparison: F vs. NF.
## 3 Simulations and Experiments  
### Numerical Simulations  
Three sets of comparative numerical simulations are carried out to illustrate the correctness of PS method. The wavelength \(\lambda\) is 635 nm in visible range, and the physical side length of object plane \(Lo\) is 5.3 mm. The horizontal and vertical flare angles of WRP to the center of SCGH \(\theta\) and \(\varphi\) are used to represent the curvature of SCGH segment as the physical side length of object plane is fixed, and \(\theta=\varphi\) for simplicity. The original object images used in numerical simulations and optical experiments are shown in Figure 3. The peak signal-to-noise ratio (PSNR) and the structural similarity index (SSIM) are used as quality measurements during simulations [22]. Considering the huge amount of time consumed by the PS method, the maximum resolution of the object images used in simulations is \(256\times 256\).
###### Abstract  
In this paper, continuous phase modulation-generalized frequency division multiplexing (CPM-GFDM) is proposed. The performance of CPM-GFDM is evaluated over Gaussian and frequency selective fading channels. In the proposed technique, the mapper in the transmitter and the de-mapper in the receiver of traditional GFDM are replaced by a CPM mapper and de-mapper, respectively. Using Monte-Carlo simulations, the bit error rate performance is evaluated for several rational values of the modulation index. We establish the superiority of CPM-GFDM over traditional GFDM using error performance plots through extensive simulations. We demonstrate that there are several values of the modulation index that give a performance superior to the conventional GFDM, with \(h=\frac{1}{2},\frac{3}{10},\frac{5}{16},\frac{7}{16}\) giving the best performance for additive white Gaussian noise (AWGN) channels, while for the frequency-selective channels the best performance is observed when \(h=\frac{3}{10},\frac{5}{16},\frac{7}{16}\).  
CPM; continuous phase modulation; GFDM; generalized frequency division multiplexing; bit error rate +
Footnote †: journal: 2023, 13, 854. [[https://doi.org/10.3390/app13020854](https://doi.org/10.3390/app13020854)]([https://doi.org/10.3390/app13020854](https://doi.org/10.3390/app13020854))
###### Abstract  
This study systematically investigates 50 \(\upmu\)m-diameter 631 stainless steel fine wires subjected to both sequential and simultaneous electrothermomechanical loading to simulate probe spring conditions in microelectronic test environments. Under cyclic current loading (\(\sim\)10\({}^{4}\) A/cm\({}^{2}\)), the 50 \(\upmu\)m 631 SS wire maintained electrical integrity up to 0.30 A for 15,000 cycles. Above 0.35 A, rapid oxide growth and abnormal grain coarsening resulted in surface embrittlement and mechanical degradation. Current-assisted tensile testing revealed a transition from recovery-dominated behavior at \(\leq\)0.20 A to significant thermal softening and ductility loss at \(\geq\)0.25 A, corresponding to a threshold temperature of approximately 200 \({}^{\circ}\)C. These results establish the endurance limit of 631 stainless steel wire under coupled thermal-mechanical-electrical stress and clarify the roles of Joule heating, oxidation, and microstructural evolution in electrical fatigue resistance. A degradation map is proposed to inform design margins and operational constraints for fatigue-tolerant, electrically stable interconnects in high-reliability probe spring applications.  
631 (17-7 PH) stainless steel; fine wire; electrical fatigue; electrothermal effect; probe spring Article
# Mapping Based Quality Metrics for Mesh Deformation Algorithms Using Radial Basis Functions
## 4 Results and Discussions
### Test Case 2: Large Rotation of NACA0012 Unstructured Mesh  
This test case considers the mesh movement due to a severe rotation of airfoil NACA0012. The airfoil is initially located in the center of a circle domain with a ra  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \(f_{\text{skew}}\)**(Average)** & \(f_{\text{skew}}\)**(min)** & \(f_{\text{size}}\)**(Average)** & \(f_{\text{size}}\)**(min)** \\ \hline Mapping-based metrics & 0.97603 & 0.74225 & 0.88579 & 0.57495 \\ Mesh-based metrics & 0.97583 & 0.74057 & 0.88567 & 0.57412 \\ Error & 0.02\% & 0.22\% & 0.01\% & 0.14\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison between the mapping-based metric and mesh-based metrics.  
Figure 5: Geometrical configuration of the mesh element.  
Figure 6: Mesh-based metrics of test case 1.  
dius of 20C, with \(C\) being the chord length of the airfoil. The trailing edge of the airfoil is initially at point \((C,0)\) and the leading edge at point \((0,0)\). The initial mesh is triangular and given in Figure 7. Two hundred nodes on the airfoil surface and 20 nodes on the filed boundary are selected as control points to calculate RBFs' interpolation. Control points on the airfoil surface and field boundary are presented in Figure 8.  
Figure 9 shows the deformed mesh around the airfoil and Figure 10 presents the mapping-based metrics calculation results. Elements near the airfoil have a high-size metric value larger than 0.94, indicating that the area change of these elements does not exceed 6%. Elements with a low-size metric value are in the region above and below the airfoil, especially in the region above the airfoil, as shown in the blue area in Figure 10b. This is mainly because the displacement above the airfoil is the largest over the domain, resulting in large compression of the elements. The value of the skewness metric of elements near the airfoil is larger than 0.99, which indicate that these elements are almost not distorted. Both the size metric and skewness metric are larger than 0.82 and the mesh deformation method is applicable for 2-D mesh deforming of an airfoil large angle rotation. The mapping-based metrics can describe the quality of the mesh deformation method very well.  
Figure 8: Control points in test case 2.  
Figure 7: Initial mesh of test case 2.  
In order to check the accuracy of mapping-based metrics of mesh deformation, a specific element with the minimal metrics value in the blue area is selected as Figure 10(a) shows with a red line. Same as described in Section 4.1, with the geometrical method, the angles of deformed mesh and the area ratio from initial mesh to deformed mesh can be calculated directly based on geometrical relationships. The geometric configuration of the specific element is presented in Figure 10(b). \(\theta_{k}\) is the angle between the sides joined at the \(k\)th node in the element.  
Figure 11: Specific element with minimal skewness metrics.  
Figure 9: Deformed mesh around the airfoil.
## 2 Synchronization and Reliability Provided by Power Supply System Design
### Traction Power Substation  
Because of its high-frequency service, the MCS electrical load has unique power consumption characteristics and is regarded as a first-class priority electrical load. Therefore, stricter regulations are needed to deliver power to the subway. The traction power supply system for the MCS generally has the following technical features [12; 13; 14; 15; 16; 17; 18; 19; 21; 22; 23; 24; 25; 26; 27; 28]:  
The DC system traction power substation (TPS) consists of AC/DC converters including a rectifier transformer (RTT) and uncontrolled rectifier (SR). Transformer-rectifier groups (TSR) are in line with EN 50328, EN60146-1-1, NEMA RI9, and IEC 62590 connection schemes [12; 13; 14; 15; 16; 17; 18; 19; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49], for example, star, delta/delta, star--three-phase bridge; delta, star/star, delta--parallel bridge or series bridge; delta, star/double star--six-phase half wave with an interphase transformer (reactor); etc.  
The number of group rectifiers will depend on the type of capacity traction substation, since there can be 2 or 3 groups of rectifiers, and normally they will include a spare set for emergency situations. The transformer-rectifier groups are duty class VI according to the above standards.  
A traction power substation includes a 22 KV ac insulated power cable and accessories, 750 V DC switchgear, negative panels, a traction power feeding substation (TPFS), a traction return bond, low-voltage AC switchgear assemblies, auxiliary step-down transformers (AC system), 110 V batteries and chargers for control power supply, a local annoucator panel and 110 V DC distribution panel boards, supervisory control and data acquisition (SCADA) interface provisions (including Interfacing Panel), a low-voltage AC insulated power cable and accessories, a 750 V DC insulated power cable and accessories, outgoing DC disconnect switches as may be required for isolation purposes, control cables and accessories, installation materials, an earthling system, bonding, corrosion control, ventilation, and regenerative energy storage systems [13; 14; 15; 16; 17; 18; 19; 24; 26; 27; 28].  
Every piece of electrical equipment is linked to the operation control center's specialized SCADA system, which controls the power supply by turning it on and off and keeping an eye on the equipment's charged state and status.
# Improvement of Recycled Aggregates Properties by Means of CO\({}_{2}\) [PERSON]
## 4 Results and Discussions
### Parametric Study of the Accelerated Carbonation Test on RCAs
#### 4.1.6 Correlation between the Optimum Water Content and the Water Absorption Coefficient of the RCA  
The physicochemical properties of RCAs, such as water absorption, are responsible for a mode of saturation which is specific to each material. The relationship between the water content and the water absorption coefficient is indeed known. The pore size distribution of the material or its chemistry can also affect the optimum water content in other applications, as is described in [50] for soot aerosols and their water uptake properties.  
However, several interests require determination of the relationship between the optimum water content and the WAC of an RCA.  
Figure 11: Effect of the water content on the CO\({}_{2}\) uptake (at 20 \({}^{\circ}\)C and 40 \({}^{\circ}\)C) for OC 12-20 (one accelerated carbonation test for these RCAs at these water contents).  
Thus, based on the values obtained for the optimum water content, which differed for each RCA, Figure 12 shows the relationship between the optimum water content and the water absorption coefficient of an RCA.  
The relationship between the optimum water contents and water absorption coefficients could be approximated with a linear relation. Based on the equation of the linear regression, the two factors differed by a factor of 0.83, with a standard deviation of 12%.  
The optimum water content is related to the maximum CO2 uptake, as was demonstrated in Section 4.1. Thus, this relationship between the optimum water content and the WAC of an RCA gives information on the maximum CO2 uptake. In the literature on concretes, the optimum water content is described globally to be between 65 and 80% in relative humidity. This relation is important data for research and could be used in several applications because of the important number of RCAs from different origins, types and sizes tested to obtain these values.  
The value of this relationship lies in its potential use in an industrial context. For reasons of efficiency and time, the optimum water content cannot be determined by accelerated carbonation tests. However, if the water absorption coefficient is known, the above relationship can be used to determine the optimum water content to be applied to an RCA in an industrial environment and if it is possible to correct the water content of the RCA before treatment.
## 5 Analysis of Prominent Clustering Routing Protocols in WSNs
### Cluster-Construction Based Clustering Routing Protocols
#### 5.1.1 Leach
Sensor nodes, with lower initial energy, that act as CHs for the same number of rounds as other sensor nodes, with higher initial energy, will die prematurely. This could bring about energy holes and coverage problems; (3) Since CH election is performed in terms of probabilities, it is hard for the predetermined CHs to be uniformly distributed throughout the network. Thereby there exist the elected CHs that are concentrated in one part of the network and some nodes that have not any CHs in their vicinity; (4) The idea of dynamic clustering brings extra overhead. For instance, CH changes and advertisements may diminish the gain in energy consumption.
## 2 Materials and Methods
### Analytical Procedures  
The algal suspension was used to measure algal dry weight (DW) and algal cell carbon and nitrogen content. The filtrate of algal suspension was used to measure pH, electrical conductivity, nutrients (nitrate, nitrite, and phosphorus), dissolved inorganic carbon (DIC), and dissolved organic carbon (DOC). The gas sample was used to measure headspace CO\({}_{2}\), N\({}_{2}\), and O\({}_{2}\) concentration. The amount of CO\({}_{2}\) absorbed into the medium was calculated from the difference of DIC concentration before and after the 24-h absorption period.  
Algal DW was measured every day using glass fiber filters. Firstly, algal suspensions were filtered through pre-weighed glass fiber filters with a pore size of 0.7 \(\upmu\)m (GF/F, Whatman, USA) and washed with distilled water three times. Then, filters were dried in an oven at 60 \({}^{\circ}\)C for over 24 hours and subsequently cooled to room temperature in a desiccator before weighing. Filters were weighed with an ultra-micro balance (XP6U Ultra Micro Comparator, Mettler Toledo, Columbus, OH, USA). Algal cell carbon and nitrogen contents were measured by measuring particulate organic carbon and nitrogen (POC and PON) of filters samples using an elemental analyzer (Flash 2000 CHN, Thermo, Waltham, MA, USA). To measure DIC and DOC, TOC analyzer (TOC-V\({}_{\mathrm{CSH}}\), Shimadzu, Kyoto, Japan) was used. Nutrients (nitrate, nitrite, and phosphate) were analyzed with an automated nutrient analyzer (SWAAN, BL-TEC, Tokyo, Japan).  
Gas composition (CO\({}_{2}\), N\({}_{2}\), and O\({}_{2}\)) was measured using a gas chromatograph (Shimadzu, GC-2014, Japan) equipped with a packed column (Shincarbon ST, 6.0 m long, 3 mm I.D., Shimadzu, Japan) and a thermal conductivity detector. The temperature of the injector and the detector were maintained at 120 \({}^{\circ}\)C and 260 \({}^{\circ}\)C, respectively. The column temperature was gradually increased from 40 \({}^{\circ}\)C to 250 \({}^{\circ}\)C. Helium was used as the carrier gas with a flow rate of 40 mL min\({}^{-1}\).
## 1 Introduction
aimed to establish the pre-relationship and post-relationship of time series in NN and a way to update weights, and a prediction method of a rotating machine's residual useful lifetime was proposed based on a quantum gene chain coding bidirectional neural network [17]. For some key components of mechanical equipment, the accuracy loss can be forecasted and the lifetime can be predicted using the above methods. However, the above studies mainly focus on the accuracy loss and lifetime prediction of a single structure or part. Most measurement systems consist of several components. Moreover, the accuracy loss laws and the accuracy lifetimes of a system and its components are often different, which lead to the inefficient use of instruments and to the wasting of effective resources and costs. Therefore, further investigation is still required to equalize the accuracy loss law of each component and to achieve uniformity of systems' accuracy lifetimes. Equalization designs have attracted great attention in the fields of electronics and networks [18; 19; 20; 21]. In addition to the equalization designs of the circuit, voltage and energy at any given moment, there are some equalization strategies for the whole group's lifetime [22; 23; 24; 25]. Due to inconsistency among the battery cells of electric vehicles, [PERSON] et al. proposed a hybrid optimal control scheme, in which the voltage balancing mode was enabled after completing capacity equalization [22]. To achieve a better battery pack cycle life, [PERSON] et al. proposed a lifetime equalization strategy aiming to optimize the worst cell's working range [24]. However, for mechanical structures or systems, few studies consider the accuracy loss relationship between the whole system and its components. To solve this problem, [PERSON] et al. proposed a novel uniform design theory based on the \"white\" error model of the total system [26]. Based on this theory, [PERSON] et al. obtained the weight functions of the accuracy loss of each main structural component using the grey correlation analysis method [27]. The main cause of the accuracy loss of the whole system was found. [PERSON] et al. established a uniform loss theoretical model to direct the uniform loss design according to a principle of equality life design [28]. Nevertheless, identifying how to establish a universal design model based on the uniform accuracy life principle is still an unsolved problem. A novel optimal design model for measurement systems based on the uniform accuracy life principle is established and solved in this paper. The optimal models and the solution method can be used to provide some practical guidance to the uniform design of an actual measurement system and save resources and costs. This paper is organized as follows: Section 2 outlines the principle of uniform accuracy lifetime design,and the optimal design is described in detail in Section 3. In Section 4, a design example is given to verify the effectiveness of the optimal design method, and conclusions are drawn in Section 5.
## 3 Results and Discussion
### PTR-ToF-MS Results
Comparison between PTR-ToF-MS and GC/MS to Monitor the Formation of Compounds throughout Beer Fermentation  
PTR-ToF-MS can measure more time points compared to GC/MS (four times a day vs. once a day for each micro-fermentation in our case). This is important, especially at the beginning of the fermentation, when many changes in VOCs are occurring, as evident in the initial 24 h for \(m/z\) 157.158 (citronellol), \(m/z\) 199.169 (citronellyl acetate) and \(m/z\) 135.109 (geranyl acetate). An initial rapid increase in the concentration of citronellol was observed after 6 h. The concentration of citronellyl acetate started to increase, and finally, after 18 h, the concentration of geranyl acetate increased. A summary of this formation is shown in Figure 7. The delay of acetylation is likely due to the repression of ATF gene expression when oxygen (dissolved in the word) is present [51]. The concentration of oxygen in word gradually decreases during the first hours of fermentation. By 210 min, complete oxygen depletion is typically observed [52]. Figure 7: Proposed formation of citronellol, citronelly acetate and geranyl acetate during fermentation. Enzymes (OYE and ATF) are based on literature [14; 50; 51].