esa-sceva/satcom-chunk-collection huggingface dataset:
_Article_  
A Theoretical Model for the Triple Phase Boundary of Solid Oxide Fuel Cell Electrospun Electrodes  
[PERSON] *, [PERSON], [PERSON] and [PERSON] *  
School of Energy and Power, Jiangsu University of Science and Technology, Zhenjiang 212003, Jiangsu, China; [EMAIL_ADDRESS] (M.Z.); [EMAIL_ADDRESS] (Z.H.)  
*Correspondence: [EMAIL_ADDRESS] (W.K.); [EMAIL_ADDRESS] (Q.Z.); Tel.: +86-511-84417398 (W.K.); Fax: +86-511-84404433 (W.K.)  
Received: 31 December 2018; Accepted: 28 January 2019; Published: 31 January 2019  
**Featured Application: Solid oxide fuel cells (SOFC) are one of the power-generated devices that have received much attention over the last decade due to advantages, such as a high efficiency, quietness, multiple fuels, and the inexpensive catalyst nature. To improve the performance of SOFC, the electrospun electrode is introduced to the SOFC, which can be achieved by impregnating nanoscale particles on the backbone surface of fibers. In this paper, a theoretical model was developed for the electrospun electrode. This model captures the key geometric parameters and their interrelationships, which can be used to design the microstructure parameters, such as the particle radius, fiber radius, and impregnation loading.**  
Electrospinning is a new state-of-the-art technology for the preparation of electrodes for solid oxide fuel cells (SOFC). Electrodes fabricated by this method have been proven to have an experimentally superior performance compared with traditional electrodes. However, the lack of a theoretic model for electrospun electrodes limits the understanding of their benefits and the optimization of their design. Based on the microstructure of electrospun electrodes and the percolation threshold, a theoretical model of electrospun electrodes is proposed in this study. Electrospun electrodes are compared to fibers with surfaces that were coated with impregnated particles. This model captures the key geometric parameters and their interrelationship, which are required to derive explicit expressions of the key electrode parameters. Furthermore, the length of the triple phase boundary (TPB) of the electrospun electrode is calculated based on this model. Finally, the effects of particle radius, fiber radius, and impregnation loading are studied. The theory model of the electrospun electrode TPB proposed in this study contributes to the optimization design of SOFC electrospun electrode.  
**Keywords: solid oxide fuel cells; electrospinning; triple phase boundary; electrode**
## 2 Background and Related Work
### Active Queue Management
#### 2.1.4 Flow Queue-Proportional Integral Controller Enhanced (FQ-PIE)  
Similar to FQ-CoDel, FQ-PIE [15] combines fair queuing (FQ) and the proportional-integral (PI) controller, an advanced control theory technique widely used in process control systems, as explained above. In our experiments, we use FQ-PIE deployed in FreeBSD, as mentioned in this paper [16]. The goals are similar to FQ-CoDel, where we use a DDRscheduler to evenly share bandwidth capacity among competing queues, except each queue is managed by an instance of PIE, which utilizes a derivative of the queuing delay to determine congestion and the responses to mitigate it.  
FQ-PIE has been observed to outperform other AQM algorithms, including FQ-CoDel, regarding delay performance and fairness, as shown in this paper [17]. FQ-PIE has also been shown to be effective in wireless networks, where packet loss and delay are significant problems.
# Monte Carlo Study of Imaging Plate Response to Laser-Driven Aluminum Ion Beams
## 1 Introduction
We compare both the linear model and the exponential model with our experimental results for Al ions. In our study, we have used the Monte Carlo simulation code SRIM [30] to calculate the stopping powers of a BAS-TR IP for Al ions because SRIM is known to represent the available experimental data well [31; 32]. We show that the response function calculations using stopping power from SRIM code agree very well with our experimental data for Al ions up to 222 MeV. We find that the IP sensitivity remains nearly constant for all the incident Al ion energy in the exponential model, which suggests that the IP response function can be calculated for the entire energy range of the ion beams using just a few experimental data points.
## 3 Results and Discussion
### Composition Characteristics of Pharmaceuticals in Snow
#### 3.1.4 Other Pharmaceuticals  
Out of the eight screened antipsychotic drugs, only lorazepam and nitrazepam were eventually detected. Lorazepam was detected only sporadically and was found in the range of ND-0.17 ng/L. Nitrazepam was found in the range of 0.21-0.35 ng/L with a high frequency of detection (100%). Nitrazepam was previously detected at a high mean concentration of 441 ng/L in the wastewater from the University Hospital of Santa Maria [51], which can explain its presence.  
No \(\beta\)-adrenergic receptor drugs except salbutamol were detected. Salbutamol was detected in the snow samples at all sites, and the concentrations ranged from 0.10 to 6.85 ng/L. Only one out of the screened hypoglycemic drugs, tolbutamide, was detected. Tolbutamide presented a frequency of detection of 42.11% and was found in the range of ND-0.98 ng/L. The insolubility in water, low boiling point and easy volatilization in air of tolbutamide may be the reasons for its detection in snow.  
According to the available literature, 38 detected target compounds were detected in accumulated snow for the first time. These compounds can be divided into six categories: Antibiotics, hormones, NSAIDs, hypoglycemic drugs, antipsychotic drugs, and \(\beta\)-adrenergic receptor drugs. Figure 3b shows that the majority of PhACs in the snow sample were antibiotics and hormones, accounting for 92.33% and 5.87%, respectively. Antibiotics and hormones are the most commonly used drugs in the medical and livestock breeding industries in China. These proportions are basically in accordance with the characteristics of production and application of PhACs in China.
## 2 Results and Discussion
### Validation
#### 2.2.3 Accuracy and Precision  
Extraction recovery values were used for the assessment of method accuracy at a concentration level of 1000 \(\upmu\)g g\({}^{-1}\) for heptane, a-pinene, toluene, 2-methyl-pyrazine, 3-heptanone, heptanal, 2-octanone, 1-heptanol, benzaldehyde and 1-octanol in a solution containing also 0.5 \(\upmu\)g mg\({}^{-1}\) butylrophenone. The results were between 81.3% and 118.2%, indicating satisfactory method accuracy. For the determination of method precision, means of relative standard deviation (RSD) was employed. For within-day repeatability (n = 5 days), RSD values ranged between 0.4% and 10.7%, while for between-days repeatability (n = 3 \(\times\) 4 days) RSD values ranged between 1.3 and 12.3%. It can be concluded that the reported HS-SPME method is precise. Table 2 shows accuracy and precision results for the studied analytes.
## 3 Structure Design and Optimization
### Modal Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \multirow{3}{*}{**ID**} & \multicolumn{3}{c}{**Total**} & \multirow{3}{*}{\begin{tabular}{c} **Wall Skin** \\ **Thickness** \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} **Type** \\ **Type** \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} **x** \\ **(mm)** \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} **y** \\ **(mm)** \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} **z** \\ **(mm)** \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} **Beam** \\ **(mm)** \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} **E** \\ **(mm)** \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} **Bonding** \\ **(mm)** \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} **Optimized** \\ **Walls** \\ \end{tabular} } & \multirow{3}{*}{\
\begin{tabular}{c} **Fequencies** \\ \end{tabular} } & \multirow{3}{*}{
\begin{tabular}{c} **100 \(\times\) 100 mm\({}^{2}\)** \\ \end{tabular} } \\ \cline{4-14}  & & & & & & & & & & & & & **I** & **II** & **III** & **Weight** & **Weight** \\ \cline{4-14}  & & & & & & & & & & & **[Hz]** & **[Hz]** & **[Hz]** & **[g]** & **\%** \\ \hline
0 & 5 & full & - & - & - & - & - & 74 & no & no & 23 & 24 & 144 & 58.3 & 100.00 \\ \hline
1 & 5 & 0.5 & A & 5 & 5 & 8 & 0.4 & 67 & no & no & 37 & 39 & 221 & 35.14 & 60.27 \\ \hline
2 & 5 & 0.5 & A & 5 & 5 & 8 & 0.4 & 74 & no & no & 39 & 41 & 232 & 35.14 & 60.27 \\ \hline
3 & 21 & 0.5 & B & 10 & 10 & 16.5 & 0.85 & 74 & no & no & 47 & 47 & 212 & 66.93 & **[114.80** \\ \hline
4 & 11 & 0.5 & C & 20 & 10 & 25 & 1 & 67 & yes & no & 34 & 35 & 216 & 41.43 & 71.06 \\ \hline
5 & 11 & 0.5 & C & 20 & 10 & 25 & 1 & 74 & yes & no & 36 & 37 & 227 & 41.43 & 71.06 \\ \hline
6 & 11 & 0.5 & D & 11 & 11 & 16.5 & 0.9 & 67 & yes & no & 38 & 39 & 231 & 46.97 & 80.57 \\ \hline
7 & 11 & 0.5 & D & 11 & 11 & 16.5 & 0.9 & 60 & yes & no & 36 & 37 & 218 & 46.97 & 80.57 \\ \hline
8 & 11 & 1 & D & 11 & 16.5 & 0.9 & 67 & yes & no & 67 & 90 & 283 & 73.47 & 216.02 \\ \hline
9 & 11 & 0.5 & D & 11 & 11 & 16.5 & 0.9 & 67 & yes & yes & 48 & 50 & 254 & 47.6 & 81.65 \\ \hline
10 & 11 & 0.5 & D & 11 & 11 & 16.5 & 0.9 & 60 & yes & yes & 46 & 48 & 240 & 47.6 & 81.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Modal analyses and weights for investigated lattice structures. 11 mm is an interesting configuration (WT11). The cell type C (ID 4 and 5) does not satisfy the requirements. Conversely, cell type D is promising and by using an optimized design it is possible to have a good choice in terms of light structure and dynamic response. This last design will be adopted for fabrication and experimental validation.
# Open Loop Position Control of Soft Hydraulic Actuators for Minimally Invasive Surgery
## 3 Materials and Methods
### Force Exertion Setup  
The force exertion of a single actuator was tested by blocking it at various levels of strain with the proximal end clamped to a support structure and the distal end coupled to a force sensor (ATI, Nano 17). The actuator was blocked at strains between 2 mm and 16 mm inclusive, at increments of 1 mm, and three repetitions of the test procedure were performed at each strain value. The maximum force in each of the three repetitions was recorded and the mean value recorded as the result. The pressure in the actuator was set at 120 kPa and 130 kPa absolute in two rounds of testing, respectively, with the volume being recorded during tests. Higher pressures could have been used, as other designs manufactured with the same method had burst pressures of over 250 kPa absolute.
# A Low-Cost IoT-Based System to Monitor the Location of a Whole Herd
## 4 Discussion
It could happen that these figures were much lower in a more realistic scenario of dozens of collars together with hundreds of tags, considering that a single collar has been able to read 64% of the tags in the sheep herd and 36% of the tags in the beef cattle herd. Potentially, the proposed solution could be used beyond location monitoring. As it is based on animal proximity to each other, data gathered by the system could be used to study animal affiliations within a herd [54]. This information can be valuable to optimize the collar/tag ratio, e.g., by identifying group leaders and fitting them with the GPS collars. On the other hand, changes in the social structure can be indicators of heat [55], calving [56] and other reproductive and health events of interest for farmers.
# Influence of Aggregate Gradation on the Engineering Properties of Lightweight Aggregate Concrete
## 3 Results and Discussion
### Influence of Aggregates' Gradation on the Properties of Concrete Strength
From the discussion mentioned above, LWAC with lightweight aggregate of small grain size and uniform grade represents comparatively superior strength behavior in the high-strength (low w/c) concrete. The quality concrete possesses low unit weight and holds the high compressive strength at the same time. If the ratio between compressive strength and unit weight of concrete is larger, the economic benefits that the concrete structure provides could be enhanced. Strength efficiency is defined as the compressive strength provided by concrete per unit weight in this research. Figure 7 shows the relationship between the aggregate grain size and strength efficiency of concretes. The strength efficiency of LWAC with high water-cement ratio (w/c = 0.7) is approximately 0.012 to 0.013. Comparing with that of NWC which is about 0.009 to 0.010, the value is probably 30% higher. The average of the strength efficiency of LWAC with a low water-cement ratio (w/c = 0.4) is mostly  
Figure 6: Compressive strength of NWC and LWAC. higher than that of NWC, but the diversity is comparatively unobvious under the larger grain size condition. The influence of the aggregates' grade is considered. It can be seen from the figure that the strength efficiency increased as the grain size of aggregates decrease in substance. For NWC, strength efficiency with uniform grain size aggregates is mostly lower than that of standard gradation aggregates. However, the strength efficiency of LWAC with small uniform grain size aggregates is conspicuously higher than that with standard-grade aggregates. Knowing from the test results mentioned above, both compressive strength and strength efficiency of LWAC with a uniform aggregate grain size less than 11 mm are evidently excellent compared to that of standard-grade aggregates. However, this tendency does not occur in NWC. Therefore, this research recommends that the aggregates gradation requirement of LWAC should be distinct from that of NWC. Following the standard grade suggested by ASTM is optional.
# Variational Quantum Circuits for Machine Learning.
## 3 Quantum Approaches for Machine Learning
### Variational Quantum Circuits for Machine Learning  
The quantum mode operation of the set of qubits is very fragile and unstable. In fact, the instability of the set of operating qubits of a quantum computer increases with the number of qubits and is related to the quality of the materials from which they are made. For this reason, the coherence time in which the qubits are stable is limited.  
To efficiently solve problems such as unstructured database searches [44], the use of thousands of qubits with low error rates and high coherence times will be required. These quantum computers are not yet available, but noisy intermediate-scale quantum (NISQ) computers can already be used [45]. However, these computers are composed of numerous noisy qubits that remain stable for very few nanoseconds.  
Variational quantum algorithms (VQAs) [46; 47; 48; 49] use classical optimizers to train parameterized quantum circuits and represent an advancement in quantum computing running on NISQ computers [50].  
Variational algorithms can be the basis for numerous applications, for example, in the design of a quantum classifier. The algorithm is similar in operation to \(a\) classical support-vector machine.  
In machine learning, support-vector machines or SVMs are supervised learning models for solving classification and regression problems. Basically, SVMs build models capable of predicting whether a new point belongs to one category or another. Similarly, the quantum variational circuit performs hyperplane slices like a conventional SVM. On the other hand, kernel functions are used to solve nonlinear classification problems using linear classifiers. Classifiers based on quantum logic have no advantage over classical computers when the kernel function can be computed efficiently on a classical computer. _However_, on many occasions, when the feature space is very large and kernel functions are hard to find, quantum computing can be very useful [38].  
The variational algorithm uses the Hilbert space in which the quantum processor operates to find the optimal hyperplane cut in the same way as a classical support-vector machine. The algorithm is run for training and for classification. The training stage uses an already classified dataset.  
The algorithm is composed of three parts: (i) state preparation or feature map where the input data x are encoded; (ii) model circuit or optimization circuit with input parameters \(\theta\); and (iii) measurement stage z as shown in the following equation. A diagram with the three stages is shown in Figure 4.  
\[f(x,\theta)=z \tag{2}\]
## 4 Discussion
Another approach using different waveform principles such as harmonic reduced pulse-width modulation to maximize the output power of the single element transducer [43]. A whole new electrical design could be introduced to excite HIFU array transducer [44]. Moreover, even if the current temperature monitoring technique using the thermocouple has a real-time response advantage, this minimally invasive method is likely still restricted by clinical applications [45]. Additionally, the accuracy of this approach strongly varies from the relative position of the thermocouple tip and the focal beam of the HIFU transducer [46]. The single-point tracking of temperature is also a drawback since the observation of temperature in surrounding normal tissues during treatment is immensely important for clinical translation [47; 48]. A noninvasive temperature monitoring method, such as using an embedded infrared camera, is a possible solution if the sensor's thermal feedback speed is rapid enough [49]. For effective HIFU treatment, PAI takes the guiding role as a powerful biomedical imaging technique that provides the real-time structural information of the tissues. Currently, the potential of the PA method is extensive in hyperthermia therapy and the proposed PAI system shows the capacity to define the boundaries of the treated areas before and after HIFU treatment. PA ability should be typically utilized for real-time therapeutic observations of the treated area [30]. Furthermore, the temperature of the target sample can be directly measured from the PA amplitude. It is even possible to create a temperature map from the mathematical model [50]. The HIFU system could able to scan the surface area along with vertically deep inside tissues depending upon the penetration capacity of the laser source [30; 51]. Our recent study by [PERSON] et al., using 650-1064 nm laser wavelengths shows the ability of the PAI system to detect tumor phantom at 8-mm depth, producing a 3D PA image of the tumor region [52]. Moreover, the PAI technique shows the potential to examine the tumor microenvironment, which has been increasingly considered as a leading tool for cancer treatment [53; 54; 55]. Lastly, for the translation to clinical applications, besides the HIFU acoustic output power justification, the PAI-guided system needs to be improved to the 3D real-time PAI guidance and the target temperature should also be interpolated from the PAI system.
## 5 Simulation Results
### Ga  
The simulation parameters based on GA are shown in Table 4. Figure 6 shows the system capacity for different population sizes in GA with \(K=18\). The objective function is \(f_{obj\_C}()\) in Equation (13). When \(M=10\), the system capacity performance can reach 15 UEs. However, when \(M\geq 100\), the system capacity reaches saturation with a near optimal solution with 18 UEs. From Figure 6, it is observed that the large population size can reach the optimal solution.  
Table 5 shows the performance comparisons of RPSO, GA and Rand methods with \(M=100\) and \(G=200\). From the results in Table 5, it is observed that when \(M=100\), the proposed GA outperforms RPSO by 1.4 UEs for \(K=24\).
# Customer Churn Prediction Approach Based on LLM Embeddings and Logistic Regression
## 3 Preliminaries
### Embeddings
#### 3.1.5 Sent2 vec  
Sent2 vec is a static embedding method designed to create fixed-size vector embeddings for words, sentences, or entire text passages. It is based on the idea of using the composition of word embeddings within a sentence to generate a meaningful representation of the entire sentence. First, individual words in the sentence are transformed into fixed-size vector representations known as word embeddings, utilizing popular methods such as Word2 Vec [36], GloVe [37], and FastText [37]. These embeddings encode the semantic meaning of each word based on its context in a large text corpus. Subsequently, various aggregation methods (e.g., averaging, concatenation) are used to merge the word embeddings into a singular representation for the entire sentence. Following aggregation, the resultant vector representation is frequently normalized to ensure uniform scale and distributions. Finally, this resulting vector can be used in various natural language processing tasks such as sentiment analysis, text classification, sentence similarity, or clustering.
# Uncertainty Analysis of 3D Line Reconstruction in a New Minimal Spatial Line Representation
## 3 Results
### Experiments on Simulation Data
#### 3.1.3 Experiment on Simulation Data to Validate Uncertainty Decrease by Multiple Observations  
To illustrate the fusion process of uncertainty, a randomly generated 3D line is projected to multiple images. Similar to the last synthetic experiment, noise with known standard deviation is added to all input variables. Every following observation is used to triangulate with the first observation, the estimated line parameters and uncertainty are incrementally updated. As illustrated in Figure 9a, every red ellipse denotes the estimated 95% confidence region of \((\theta,\phi)\) by one triangulation; the green ellipse denotes the final 95% confidence region of \((\theta,\phi)\) by incremental fusion. Figure 9b shows the case for \((m_{l},\alpha)\) in the proposed compact representation. The blue \"*\" represents the ground truth and the green \"*\" represents the finally estimated \((\theta,\phi)\) or \((m_{l},\alpha)\) by incremental fusion. Figure 9c shows the decrease of 95% confidence interval of every component in the proposed compact representation by the incremental update. A more accurate estimation with smaller uncertainty can be obtained by  
Figure 8: Monte Carlo test to validate the uncertainty estimation of three-view line reconstruction. (**a**) The red ellipse denotes the estimated 95% confidence region of \((\theta,\phi)\). Red “+” denotes the ground truth. Each green “+” represents a noisy estimation of \((\theta,\phi)\). (**b**) The red ellipse denotes the estimated 95% confidence region of \((m_{l},\alpha)\). Red “+” denotes the ground truth. Each green “+” represents a noisy estimation of \((m_{l},\alpha)\).  
importing more observations. When the confidence interval of each parameter is smaller than the respective thresholds, the line is thought to be well-reconstructed.
## 6 VLC-IoT: Existing and Proposed Approaches
### Handover Mechanisms in VLC-IoT
#### 6.3.1 Existing Approaches  
It is important that the unique characteristics of the VLC systems are taken into consideration when implementing a framework for a handover mechanism in VLC-IoT systems. Based on the literature, we have highlighted the different existing handover processes deployed for VLC systems especially in scenarios where massive-IoT connectivity is a requirement.  
1. Vertical Handover  
Similar to a typical vertical handover mechanism in RF systems, vertical handover in VLC-IoT systems occur between different access layers. However, in VLC-IoT systems, this type of handover is usually triggered when there is a line-of-sight (LOS) blockage. For example, when a VLC-IoT device experiences a LOS blockage to a VLC AP it can connect to an RF AP to maintain seamless connection and guarantee QoS requirements of VLC-IoT devices [56].  
2. Horizontal Handover  
This handover mechanism is commonly executed in mobile VLC-IoT devices to maintain seamless connectivity and to prevent inter-cell interference. In this respect, a moving VLC-IoT device switches its connection from one VLC AP to another VLC AP seamlessly to maintain connectivity [56]. Some information, e.g., speed, direction, and acceleration of the mobile VLC-IoT device are significant when estimating what time a handover will be required. In addition, this type of information is vital to improving the performance of the handover mechanism.