esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling of Integrated Nanoneedle-Microfluidic System for Single Cell Temperature Measurement
###### Abstract  
In this research, a finite element study on a nanoneedle-microfluidic system for single cell temperature measurement is presented. The nanoneedle design and electrical and mechanical characterization are analyzed, in which tungsten is used as the sensing material. A rectangular shaped sensor with a gap of 10.8 \(\upmu\)m showed to give the same current density distribution within the nanoneedle, and a 90 nm\({}^{2}\) cross-sectional area showed to cause minimum damage to the cell. Furthermore, the current showed to have a positive temperature coefficient of resistance (TCR) with an increase in the temperature, and the nanoneedle showed to be able to resist ramp force up to 22.5 \(\upmu\)N before failure. Electrical measurement on yeast cell showed that the nanoneedle was independent of the cell conductivity. The nanoneedle proved to be able to measure temperature with a current difference of 50 nA and a resolution of 0.02 \({}^{\circ}\)C in 10 ms. A Y-shaped microchannel was proposed and the microchannel cross-sectional area was optimized to be 63 \(\upmu\)m\({}^{2}\) and a flow rate of 24.6 pL/min allowed successful cell penetration causing minimal damage to the cell.  
microfluidics; nanoneedle; intracellular temperature; finite element Article
## 4 Results and Discussion
### Variable Significance for FS and ASR Expansion  
The effects of the variables on the FS and ASR results were quantified as the importance ratio presented in Figure 14. The curing time is the most significant factor for both FS and ASR expansion. The WGP particle size was the second important variable for FS (26%), thus remarkably affecting the mechanical strength of mortars containing WGP. This was followed by the alkali content (18%) and the WGP replacement ratio (12%). Further, Na\({}_{2}\)SO\({}_{4}\) appeared less important than alkali, which agrees with the experimental findings. For the ASR expansion, the WGP replacement ratio became the most significant factor (16%) except for the curing time. This agreed with the experimental results that ASR expansion significantly depended on the WGP replacement ratio other than the activator's content. Moreover, the alkali content was less important than Na\({}_{2}\)SO\({}_{4}\), which is reasonable since the 1 N NaOH solution was used to immerse the samples. In conclusion, the importance measure provides a quantitative analysis of input variables, indicating that the WGP's particle size and replacement ratio are the most crucial parameters (except curing time) for FS and ASR, respectively.  
Figure 13: Performance evaluation of three models by (**a**) boxplot of FS modelling, (**b**) boxplot of ASR modelling, (**c**) Taylor diagram of FS modelling, and (**d**) Taylor diagram of ASR modelling.
## 3 Results and Discussion  
### Identification and Characterization of the Microbes  
Twenty microorganisms were isolated from PSW and were screened for biofoculant production using a kaolin clay suspension for flocculation activity assessments. Isolate D2 produced biofoculants with the highest activity compared to the flocculants produced by other isolates. Its morphology was circular with cream-white colonies. A gram test showed a rod-shaped, arranged in linear sequences, and tested Gram-positive. The 16S rDNA results predicted that it was a Bacillus subspecies, _Bacillus arrybatai_, or _Bacillus megaterium_ with 100% identity. For a group of Bacillus subspecies, sequencing the universal 16S rRNA region, which is usually expected of all bacteria, does not discriminate between them, hence making it difficult to identify them [20]. However, since the 16S gene did not sufficiently distinguish between the different species of _Bacillus_, for better resolution, RpoD was employed, which was able to distinguish well between the different _Bacillus_ species. The organism was identified as _Bacillus megaterium_ (98.71%), an identity with an accession number CP001983.1 which was deposited into the GenBank. Therefore, the strain and the biofoculants were named _Bacillus_ sp. D2 and D2, respectively.  
It is worth noting that several researchers [5; 21; 22; 23; 24] have studied or characterized biofoculant produced by _Bacillus megaterium_, which was either isolated from various niches or taken from a culture collection. The results or characteristics of the biofiloculants in these studies vary, which can be attributed to the origin of the microorganism, the methods used for flocculants recovery, and other factors. In addition, because biofoculants are produced through microbial fermentation, this process is influenced by several variables, such as the availability and composition of nutrients, physicochemical parameters, and culture conditions, resulting in biofoculants with varying properties [8]. Therefore, intensive research is required to understand their properties better if microbial flocculants are to be implemented on an industrial scale.
## 4 Numerical Examples
### Simply Supported Square Plate Subjected to Uniform Load
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline _h/L_ & **0.001** & **0.01** & **0.10** & **0.15** & **0.20** & **0.25** & **0.30** \\ \hline TiSDTPE (\(4\times 4\)) & 0.1171 & 0.1241 & 0.1459 & 0.1708 & 0.2044 & 0.2463 & 0.2962 \\ TiSDTPE (\(8\times 8\)) & 0.1255 & 0.1266 & 0.1491 & 0.1757 & 0.2112 & 0.2553 & 0.3077 \\ TiSDTPE (\(16\times 16\)) & 0.1265 & 0.1268 & 0.1497 & 0.1766 & 0.2124 & 0.2569 & 0.3097 \\ TiSDTPE (Figure 12) & 0.1266 & 0.1268 & 0.1496 & 0.1764 & 0.2122 & 0.2567 & 0.3095 \\ FfSDTPE (\(16\times 16\)) & 0.1265 & 0.1268 & 0.1491 & 0.1750 & 0.2093 & 0.2516 & 0.3013 \\ FiSDTPE (\(16\times 16\)) & 0.1265 & 0.1268 & 0.1505 & 0.1788 & 0.2173 & 0.2659 & 0.3247 \\ DST-BL (\(16\times 16\)) & 0.1265 & 0.1267 & 0.1488 & 0.1756 & 0.2127 & 0.2601 & 0.3179 \\ RDKTM (\(16\times 16\)) & 0.1265 & 0.1267 & 0.1502 & 0.1784 & 0.2167 & 0.2650 & 0.3236 \\ Ref. [67] & 0.1265 & 0.1265 & 0.1499 & 0.1798 & 0.2167 & 0.2675 & 0.3227 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Normalized deflection for the clamped square plate. Figure 13: Convergence of normalized deflection for the simply supported square plate.
## 1 Introduction
### Background and Motivation
#### 1.1.2 Current State of Eye-Tracking Technology in Educational Environments
To further this matter, we asked teachers experienced with eye-tracking applications to create lessons adapted to their respective subjects for preschool students, who were being monitored with eye tracking during the lesson and interacting with the teachers with real-time gaze estimation being shown to the teacher.
## 2 Methods
### Noisy Labels  
In order to evaluate the effectiveness of our proposed ADL in handling noisy labels, we simulate a scenario with imprecise annotations by introducing artificial noise into a dataset whose initial annotations are considered correct. The process of simulating noisy labels in our dataset is as follows:Starting with a dataset containing perfect annotations, we randomly select a subset of annotations to be altered, and the ratio of noisy labels to the whole dataset is denoted as \(\beta\in[0,1]\).
2. For the selected annotations, we apply image-processing operations such as erosion, dilation, and elastic transformation to generate noisy labels. These operations mimic the types of noise that could be present in real-world clinical scenarios, where annotations might be imperfect due to various factors. Erosion and dilation are fundamental morphological operations. Let \(A\) be the binary annotation mask and \(B\) be a structuring element. The erosion (\(\ominus\)) and dilation (\(\oplus\)) operations can be defined as \[(A\ominus B)(x,y) =\min_{(i,j)\in B}A(x-i,y-j)\] (8) \[(A\oplus B)(x,y) =\max_{(i,j)\in B}A(x+i,y+j)\] (9) where \((x,y)\) represents the pixel coordinates in the image.  
Elastic transformation is a nonlinear deformation technique that can simulate the local warping of shapes. Given an image \(I(x,y)\) and two displacement fields \(\Delta x(x,y)\) and \(\Delta y(x,y)\), which are generated by Gaussian smoothing of random fields, an elastic transformation can be defined as \[I_{\text{transformed}}(x,y)=I(x+\alpha\Delta x(x,y),y+\alpha\Delta y(x,y))\] (10) where \(\alpha\) is the deformation scale factor.
3. The altered annotations are then used to replace their corresponding original annotations in the dataset, creating a new dataset with a mix of perfect and noisy labels. By simulating noisy labels in this manner, we create a dataset that can challenge experiments, enabling us to assess the noise-robustness of our proposed method and compare its performance against existing high-performing techniques.
# Revolution or Evolution? Technical Requirements and Considerations towards 6G Mobile Communications
## 6 Enablers for 6G and Relevant Technical Preparation and Implementation
### Blockchain  
Blockchain [82; 83; 84; 85] is part of the revolution for future mobile communication technologies. Through the different features of the blockchain (e.g., auditability, decentralization, immutability, and transparency) that are essential for 6G communication, better security features allow secure access to critical data in different network entities and an untamable distributed ledger containing all data is shared among all relevant entities [86]. Besides that, blockchain also offers a number of benefits in resource orchestration and future 6G  
Figure 7: Cell-free massive MIMO.  
network access. Apart from cryptocurrency, blockchain is useful for financial and social services, risk management, and healthcare facilities. The role of blockchain in different application domains has been explored in several studies [87]. There are many significant advantages of blockchain-enabled 6G; for example, 6G will be able to monitor and control resource use and sharing more effectively utilizing blockchain [88]. Also, new business prospects, value generation, and competitive advantages can be created on the blockchain in 6G to create sustainable and scalable company models [89].
## 1 Introduction
Compared to these earlier studies, this article introduces the following innovative elements:  
* **The use of a full 6-DOF AUV motion model** for training and validation (in most prior works, the model is typically simplified to 3-4 DOFs);
* **Experiments based on real ENC map data** (converted into a simple binary grid derived from electronic navigational charts of the port of Gdynia), significantly bridging the gap between the training conditions and real-world applications;
* **A comparison of three advanced off-policy algorithms (SAC, TD3, and TQC) with classical methods** (A*, APF, GA, PSO, and RRT*), while also incorporating Hindsight Experience Replay (HER) and curriculum learning;
* **An extensive multiparametric analysis** (accuracy, distance, speed, and mission time) confirming that the TQC-based approach offers both high success rates (near 99%) and short execution times. The remainder of this article is organized as follows: Section 2 describes the mathematical model of the AUV used both to train the RL-based algorithms and verify the feasibility of the trajectories obtained using the classical methods. This section also outlines the RL-based and classical methods, along with the procedure for path smoothing and trajectory generation. Section 3 details the agent's training environment, including the observation and action spaces, as well as the reward function. Section 4 presents the optimized hyperparameters for the RL algorithms and discusses their training process. In Section 5, the simulation results for a path-planning task in the nonlinear AUV model are analyzed. Section 6 provides a discussion of the key aspects of trajectory planning based on the results obtained. Finally, Section 7 concludes this paper with a summary of its main findings and future work.
# An Analog Circuit Fault Diagnosis Method Based on Circle Model and Extreme Learning Machine
## 2 Feature Engineering Based on Circle Model
### Circle Model-Based Feature Engineering
#### 2.2.1 Circle Model-Based Feature Extraction
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Fault Source**} & \multirow{2}{*}{**Sampled Value**} & \multirow{2}{*}{**Output Voltage**} & \multicolumn{2}{c}{**Model Parameter**} \\ \cline{3-5}  & & & **Circle Center (a, b)** & **Radius r** \\ \hline \multirow{2}{*}{R11} & \(1\ \Omega\) & \(4.038,6.224\) & \multirow{2}{*}{(19.153, \(-\)3.8939)} & \multirow{2}{*}{18.189} \\ \cline{2-2} \cline{5-5}  & \(100\ \mathrm{M}\Omega\) & \(1.298,-4.253\times 10^{-1}\) & & \\ \hline \multirow{2}{*}{R12} & \(1\ \Omega\) & \(-0.6481,3.870\) & \multirow{2}{*}{(5.1950, 3.6332)} & \multirow{2}{*}{2.838} \\ \cline{2-2} \cline{5-5}  & \(100\ \mathrm{M}\Omega\) & \(4.863,8.145\times 10^{-1}\) & & \\ \hline \multirow{2}{*}{R13} & \(1\ \Omega\) & \(-1.602\times 10^{-4},5.512\times 10^{-4}\) & & \\ \cline{2-2} \cline{5-5}  & \(100\ \mathrm{M}\Omega\) & \(4.861,8.568\times 10^{-1}\) & & \\ \hline \multirow{2}{*}{C1} & \(1\ \mathrm{pF}\) & \(1.210,3.466\) & \multirow{2}{*}{(1.3481, 1.4736)} & \multirow{2}{*}{1.997} \\ \cline{2-2} \cline{5-5}  & \(100\ \mathrm{mF}\) & \(8.835\times 10^{-4},-8.085\times 10^{-4}\) & & \\ \hline \multirow{2}{*}{C2} & \(1\ \mathrm{pF}\) & \(-1.993\times 10^{-5},1.502\times 10^{-4}\) & & \\ \cline{2-2} \cline{5-5}  & \(100\ \mathrm{mF}\) & \(3.585,-2.252\) & & \\ \hline \multirow{2}{*}{C3} & \(1\ \mathrm{pF}\) & \(2.262,7.341\times 10^{-1}\) & & \\ \cline{2-2} \cline{5-5}  & \(100\ \mathrm{mF}\) & \(4.864,8.150\times 10^{-1}\) & & \\ \hline \multirow{2}{*}{C4} & \(1\ \mathrm{pF}\) & \(3.452,3.081\) & \multirow{2}{*}{(3.0696, 4.7786)} & \multirow{2}{*}{1.740} \\ \cline{2-2} \cline{5-5}  & \(100\ \mathrm{mF}\) & \(4.037,6.225\) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
Figure 3: The circle model of Leapfrog circuit. In the actual test, each element has a certain tolerance, and thus faces a deviation in the actual trajectory of the output voltage. To minimize the deviation, a classifier was introduced to diagnose and position the fault more accurately, in association with the simulated voltage features of the circle model in the complex domain.
## 5 Ablation Study and Discussions
### Practical Application as Image Pre-Processing for Textile Design  
The proposed method serves as a general-purpose framework for repeated pattern detection, with the flexibility to detect a wide range of repeated patterns from images across various domains. In this paper, its practical value is demonstrated through an application to textile design. The workflow of textile design begins by processing a large input image containing multiple repeated patterns using the proposed method. This step detects and isolates the repeated patterns, trimming them into individual repeating units. These isolated patterns are then fed into an external design element extraction and vectorization method [45], which outputs vectorized design elements. This automated workflow streamlines the pre-processing stage, significantly reducing manual effort, simplifying input data, and ensuring consistency in isolating design elements for downstream tasks.
## 2 Design, Materials, and Methods
### Characterization  
The morphology and microstructures were characterized by a SEM instrument (FEI, Quanta 200). The sheet resistance was determined from \(Rs\) = \(Rw/l\), where the resistance \(R\) was measured using a digital multimeter (UT61E, Uni-Trend Technology (China) Co., Ltd., China), and \(w\) and \(l\) are the electrode width and length, respectively. The thickness of the silver membrane was measured from the cross-sectional views using an optical microscope (Leica DM6000 M, Leica Microsystems GmbH, Germany). For RF performance testing, SubMiniature version A (SMA, pin diameter: 1.24  mm, medium diameter: 4.10 mm) connectors were used to mount the MS line samples to a cable. Conductive silver paste was used to enable solid contact between the silver conductors and SMA connectors. The RF performance of the microstrip lines was examined using a vector network analyzer (E5071C, Keysight Technologies, United States) from 0.3 GHz to 18 GHz. The port impedance of the vector network analyzer was kept as the standard impedance of 50 \(\Omega\).
# Analyzing the Impact of Variability and Uncertainty on Power System Flexibility
## 2 Flexibility Index: Ramping Capability Shortage Probability
### Ramping Capability Shortage Probability  
If \(RCR_{t}\) is larger than \(SRC_{t}\), then load shedding is enforced; this situation is denoted as RC shortage. The probability that an RC shortage will happen at time \(t\) is defined as the RC shortage probability (\(RSP_{t}\)), is expressed by  
\[RSP_{t}=\ \sum\limits_{c\in E_{t}}Prob(e)\left[\sum\limits_{c\in C_{t-\Delta t}} Prob_{c}\left[FNL_{t}+NLFE_{t}>\sum\limits_{i\in I}A_{i,t-\Delta t}O_{i,t- \Delta t}\{P_{i,t-\Delta t}+\min(P_{max,i}-P_{i,t-\Delta t},\tau r_{i}\Delta t )\}\right]\right] \tag{5}\]  
The \(RSP_{t}\) is applied to the worst case, i.e., when calculating the \(RSP_{t}\) of the targeted time, uncertainty cases are considered that occur just before the targeted time and thus cannot be resolved by post-recovery actions. This consideration is helpful in reducing the computational burden in large power systems. The variability relates to \(FNL_{t}\), \(O_{t-\Delta t}\), and \(P_{t-\Delta t}\). Any change in \(FNL_{t}\) itself indicates an increase/decrease in variability, and also changes \(O_{t-\Delta t}\) and \(P_{i,\ t-\Delta t}\). Uncertainty is associated with \(C_{t-\Delta t}\), \(E_{t}\), \(A_{t-\Delta t}\), and \(NLFE_{t}\), and \(C_{t-\Delta t}\) and \(E_{t}\) represent the set of possible cases for \(A_{i,t-\Delta t}\) and \(NLFE_{t}\), respectively.  
An example for \(C_{t-\Delta t}\) is as follows: two online generating units are considered for 2 h. Each unit has two states, i.e., 0 (failure) or 1 (online). Considering the possible cases of two units, \(C_{1}\) is a set of four elements; i.e., ((0,0), (0,1), (1,0), (1,1)). The probability of each element can be obtained based on a Markov-chain-based capacity state model; for further information, please refer to [30]. An example for \(E_{t}\) is as follows: the forecast net loads (i.e., \(FNL_{t}\)) are assumed to be 10 MW. If \(NLFE_{t}\) follows a triangular distribution as shown in Figure 1, \(E_{t}\) becomes ((\(-0.5\) MW), (0 MW), (0.5 MW)) by multiplying 10 MW and 5%, and the elements have a probability of 0.25, 0.5, and 0.25, respectively. Therefore, any changes in the parameters relating to the variability and uncertainty can be reflected in the \(RSP_{t}\) calculation.  
Complementarity is considered an influential factor with respect to the variability of the system [31]. Its effect is reflected in \(NLFE_{t}\), \(VGFE_{t}\), \(O_{it\cdot\Delta t}\), and \(P_{it\cdot\Delta t}\). If the aggregated forecast output of renewable energy resources change with the effects of complementarity, the value of \(FVG_{t}\) varies, and accordingly the value of \(VGFE_{t}\), leading to changes in \(O_{it\cdot\Delta t}\), and \(P_{it\cdot\Delta t}\).
# Sino-InSpace: A Digital Simulation Platform for Virtual Space Environments
## 3 Key Technologies
### Environment Data Organization
#### 3.2.2 Discrete Space Environment Data Index  
The space environment contains rich elements and covers a vast area; for example, taking the earth space environment as an example, elements such as the middle and upper atmosphere, the geomagnetic field, and the ionosphere are in the range of tens of kilometers to tens of thousands of kilometers. For space environment elements with a wide distribution and large density under a static state, whether it is the clipping in the visual process or the nearest neighbor in the query operation, the conventional traversal method does not meet the requirements. The spheroid degenerated-octree grid (SDOG) uses a volumetric discretization of the sphere that can provide a reference [61; 62]. The octree spatial subdivision method was adopted to construct the index for the original version of Sino-InSpace (see Figure 4).  
Figure 3: Geographic data segmentation and index method.  
An octree is a tree data structure involving spatial level recursive subdivision. The subdivision of the space environment data was based on the simple cylindrical projection plane and height direction. The cube represented by the root node included all the data to be processed. The root node was subdivided into eight sub nodes recursively, and the nonempty node continued to be divided. The maximum recursive depth of an octree depended on the resolution of various environmental data, display requirements, and query accuracy.
# Reflection Characteristics Measurements of Indoor Wireless Link in D-Band
## 6 Conclusions  
For five kinds of materials, we present a series of indoor reflection measurements at six incident angles (i.e., 20\({}^{\circ}\), 30\({}^{\circ}\), 40\({}^{\circ}\), 50\({}^{\circ}\), 60\({}^{\circ}\), and 70\({}^{\circ}\)) in the full D-band range. We also establish a modified theoretical reflection model with two parameters (i.e., relative permittivity and surface roughness) fitting by the MMSE criterion. With this model, it is unnecessary to measure these two parameters by complex devices because they can be estimated by this model. The estimated relative permittivity from 110 GHz to 170 GHz is 1.75-1.92, 2.56-2.69, 2.51-2.69, 5.94-6.26, 3.49-3.76, respectively and the estimated surface roughness is 76.4-80.0 \(\upmu\)m, 8.2-14.5 \(\upmu\)m, 96.4-99.2 \(\upmu\)m, 264.3-269.0 \(\upmu\)m and 321.3-325 \(\upmu\)m respectively, corresponding to wood, plexiglass, drywall (plaster), concrete board and red brick. These estimated parameters of materials are very close to the data in previous references and  
Figure 9: The relationship between estimated surface roughness of five materials and frequencies in D-band.  
the measured reflection coefficients are in good agreement with the theoretical values calculated by the model. Through the analysis of the relationships between properties and frequency in the D-band, it is concluded that both the reflection coefficients and the relative permittivity demonstrate a slow downward trend, whereas the surface roughness increases slightly with increasing frequency. They all demonstrate a weak frequency dependence. The large reflection loss suggests that the parallel polarized wave may be not suitable for indoor wireless communication in D-band. Interestingly, at a fixed angle and frequency, the concrete board with the highest roughness have the lowest reflection loss, indicating that the reflection characteristics of indoor building materials are determined not only by surface roughness, but also by many other factors, such as relative permittivity, frequency, and incident angle. Although our results are obtained in the full D-band range, we believe that the general conclusions remain applicable even for higher frequency in the THz range. Our work suggests many optimistic possibilities for an indoor wireless pico-cellular communication system. In the future, we want to study the indoor channel transmission characteristics in a larger space, including multiple reflections and multipath delay, and explore the transmission performance of data flow in THz indoor channels.  
Conceptualization, M.W. and Y.W.; methodology, M.W. and J.D.; software, M.W. and C.B.; validation, M.W., W.L., and Z.Z.; formal analysis, M.W. and C.L.; investigation, M.W. and X.W.; writing--original draft, M.W.; writing--review and editing, M.W., J.Y., and C.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the NNSF of China (61935005, 61720106015, 61835002, and 62127802).  
Not applicable.  
The authors declare no conflict of interest.