esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Light Photodetectors for Fluorescence Microscopy
## 2 Fluorescence Microscopy
### Fluorescence  
Fluorescence is a photophysical phenomenon of the emission of light through the excitation of a fluorophore from the ground state to an excited electronic state upon the absorption of light, with the light energy equivalent to the energy gap between the two states [10; 12]. Figure 1 shows a jablonski diagram that illustrates the electronic energy levels of a fluorophore and the transitions between them are represented by arrows. S\({}_{0}\), S\({}_{1}\), and S\({}_{2}\) represent the singlet ground, and first and second excited electronic states, respectively. The vibrational ground states and higher vibrational states of each electronic state are illustrated with black and gray lines, respectively. The transition from the ground state to the excited state by the light absorption occurs in less than 10\({}^{-15}\) s. A fluorophore is usually excited to some higher vibrational level of the excited state. The electron usually rapidly relaxes to the lowest vibrational level of S\({}_{1}\). This process is called internal conversion and generally occurs within 10-12 s. Then, the excited state is relaxed to the ground state in a few nanoseconds (10-9 s), which is accompanied by the radiation of the fluorescence emission. Because internal conversion is generally complete prior to emission, the last relaxation step to the ground state accounts for most of the overall process. Thus, the relaxation time is called the fluorescence lifetime. The wavelength of the fluorescence is longer than the excitation wavelength because the energy from the absorbed photon is partially lost via non-radiative decay. This shift in wavelength is called the Stokes shift.  
An electron in the S1 state can also flip its spin thus creating the first triplet state T1, which is termed intersystem crossing. Transition from T1 to S0 is forbidden, thus the rate constants for triplet emission (phosphorescence) are several orders of magnitude smaller than those for fluorescence, which results in a long-lived dark state called blinking. While in the T1 state, the fluorophore may experience photobleaching, which is an irreversible fluorescence switching-off process.  
Table 1 shows characteristics of several fluorophores commonly used in fluorescence microscopy, including dyes, a quantum dot (Qdot) [14; 15; 16], and a fluorescent protein [17; 18]. Photobleaching can be a representative photostability indicator of a fluorophore and the higher resistance to photobleaching allows longer or brighter fluorescence observation.
# Effect of Different Welding Methods on Flip-Chip LED (FC-LED) Filament Properties
###### Abstract  
This paper investigates the effect of two different welding methods, direct welding (DW) and vacuum furnace welding (VFW), on flip-chip light-emitting diode (FC-LED) filament properties. Shearing force, SEM, steady-state voltage, steady-state luminous flux, and change of photoelectric performance with aging time were employed to characterize the differences in filament properties between the two welding methods. The shearing test revealed that the average shearing force of the VFW group was higher than that of the DW group, but the two groups followed the standard. Furthermore, the microstructure of the VFW group fault was more smoother, and the voids were fewer and smaller based on the SEM test results. The steady-state voltage and luminous flux revealed that the VFW group had a more concentrated voltage and a higher luminous flux. The aging data revealed that the steady-state voltage change rate of both groups was not very different, and both luminous flux maintenance rates of the VFW group were higher than those of the DW group, but all were within the standard range. In conclusion, if there is a higher requirement for filament in a practical application, such as the filament is connected in series or in parallel and needs a higher luminous flux, it can be welded using vacuum furnace welding. If the focus is on production efficiency and the high performance of filaments is not required, direct welding can be used.  
Keywords:flip-chip LED; welding methods; microstructure; aging; photoelectric performance +
Footnote †: journal: Applied Sciences
## 1 Introduction
Similar to CG, however, inaccurate initial estimates significantly impact the subsequent calculation process, leading to an increase in the number of iterations and high signal delay. In this paper we propose a highly efficient novel detector named CHSOR that combines the advantages of SOR, MSOR, and Chebyshev acceleration methods. Our proposed approach promotes a better balance between BER performance and complexity for multi-user M-MIMO systems through a straightforward two-stage structure that can naturally achieve low complexity within a small number of iterations. The first stage of the detector mixes the SOR and MSOR schemes to produce a rough estimate of parameters such as the compensation vector, iteration matrix, eigenvalue of the iteration matrix, etc., then passes the estimated values of these parameters and the symbol to the second stage as the initial state. In the second stage, the Chebyshev polynomial recurrence relationship characteristics are used to iterate for accelerated convergence and refinement performance. It is worth noting that owing to the iteration matrix of SOR and MSOR being supplemented by each other to double refine and enhance rough output, the combination of SOR and MSOR can generate better initial estimates toward the convergent direction in our study, which is conducive to making the most of the acceleration advantages of the Chebyshev method and improving signal detection efficiency. Simulations show that the proposed two-stage detector has a faster convergence rate and achieves excellent BER performance with moderate computational complexity. Moreover, CHSOR achieves better BER performance than early iterative methods such as Jacobi, GS, etc., as well as the more recently developed Chebyshev-RI and Chebyshev-MAOR methods. In addition, the CHSOR method can be implemented simultaneously in uplink multi-user M-MIMO OFDM and UFMC systems. The simulation scenario covers up to 256 antennas and 1024-QAM. Thus, the proposed CHSOR scheme demonstrates compatibility and practicality in 4G and B5G environments. The remainder of this paper is organized as follows: Section 2 introduces the system model used in this article; Section 3 reviews the iterative methods proposed by some previous researchers and the CHSOR detector proposed in this study; Section 4 provides the simulation results, complexity analysis, and verification of the proposed method; finally, concluding remarks in Section 5 summarize the paper.
# Experimental Evaluation of a Takagi-Sugeno Fuzzy Controller for an EV3 Ballbot System +
## 3 Takagi-Sugeno Fuzzy Modelling
### TSFC Design  
The solution to the controller fuzzy design problem consists of determining feedback gains \(K_{i}\) for the linear controllers fulfilling the following theorem.  
**Theorem 1** ([28]).: _The equilibrium of the continuous fuzzy control systems (14), (15) and (25) is globally asymptotically stable if there exists a common positive definite matrix \(P_{\mathcal{C}}\) such that_  
\[G_{ii}^{T}P_{\mathcal{C}}+P_{\mathcal{C}}G_{ii}< 0, \tag{26}\] \[\left(\frac{G_{ij}+G_{ii}}{2}\right)^{T}P_{\mathcal{C}}+P_{ \mathcal{C}}\bigg{(}\frac{G_{ij}+G_{ji}}{2}\bigg{)}\leq 0, \tag{27}\]  
_for \(i<j\), such that \(h_{i}\cap h_{j}\
eq\emptyset\), with \(G_{ij}=A_{i}-B_{i}K_{j}\)._  
Proof.: See [28].  
The conditions (26) and (27) are not jointly convex in \(K_{i}\) and \(P_{\mathcal{C}}\). So, defining \(X=P_{\mathcal{C}}^{-1}\) and \(M_{i}=K_{i}X\) such that for \(X>0\) then \(K_{i}=M_{i}X^{-1}\), the stable fuzzy controller design problem is established as follows [28]:  
_Stable Fuzzy Controller Design Problem_. Find \(X>0\) and \(M_{i}\) (\(i=1,\ldots,r\)) satisfying  
\[-XA_{i}^{T}-A_{i}X+M_{i}^{T}B_{i}^{T}+B_{i}M_{i}> 0, \tag{28}\] \[-XA_{i}^{T}-A_{i}X-XA_{j}^{T}-A_{j}X+M_{j}^{T}B_{i}^{T}+B_{i}M_{j} +M_{i}^{T}B_{j}^{T}+B_{j}M_{i}\geq 0. \tag{29}\]  
The conditions (28) and (29) are LMIs with respect to variables \(X\) and \(M_{i}\). A positive definite matrix \(X\) and \(M_{i}\) can be found that satisfies such inequalities. From the solutions (28) and (29), a common positive definite matrix \(P_{\mathcal{C}}\) and feedback gains \(K_{i}\) can be obtained as  
\[P_{\mathcal{C}} = X^{-1}, \tag{30}\] \[K_{i} = M_{i}X^{-1}. \tag{31}\]  
Hence, assuming that the pair \((A_{i},B_{i})\) is controllable, one proceeds with the stable fuzzy controller design. Then, one solves the design problem through the use of the LMI Control Toolbox from MATLAB (Matlab2018a)\({}^{\otimes}\). The existence of a common positive definite matrix \(P_{\mathcal{C}}\) has been verified in [30] for which the feedback gain result is  
\[K_{1} = [13.4035\ -0.0515\ \ \ 1.7995\ -0.1413], \tag{32}\] \[K_{2} = [15.0967\ -0.0598\ \ \ 2.0884\ -0.1565]. \tag{33}\]  
A feature of the TSFC design methodology is that the nonlinearities are completely captured by the TSFMs and the nonlinearities are fully embedded into the membership functions in the TSFMs. Here, the membership functions are given, as shown in Figure 3. The TSFC scheme is shown in Figure 4.
## 2 Materials and Methods
### Preliminary Tests  
Preliminary tests indicate that the minimum feasible time slice is constrained by the duration of a single raw data reading, which is approximately 330 us for one nine-degrees-of-freedom data sample. Assuming no additional tasks are executed, the maximum achievable sampling rate is 3000 samples per second, given that the scheduler's overhead is approximately 1% (i.e., 3 us per cycle). With a time slice of 400 us, a stable sampling rate of 2500 samples per second can be achieved when no other tasks are running.  
To demonstrate proof of concept, we established a continuous data stream via a Universal Asynchronous Receiver-Transmitter (UART) connection through a Universal Serial Bus (USB) to a personal computer. The UART interface was chosen because the terminal software used for testing supports this type of communication. It is important to emphasize that this connection was used solely for testing purposes--to verify that the data were transmitted correctly--and is not part of the final implementation. The conversion from UART to USB was handled by a dedicated interrupt routine within the Arduino software framework. To prevent interference with real-time tasks, we assigned the routine a lower priority than the scheduler's timer interrupt.  
We successfully maintained a continuous data stream at 2500 sample packages per second. Each sample contains an 8-byte synchronization header, a 4-byte timestamp, a 4-byte sample number, and three 3D sensor vectors (magnetometer, accelerometer, and gyroscope). Each vector component is stored as a 16-bit integer, resulting in a gross sample package size of 34 bytes.
# Authenticated Multicast in Tiny Networks via an Extremely Low-Bandwidth Medium
## 3 Establishing Group Keys  
In this section, we discuss strategies to establish a shared key for a multicast group, given some preloaded cryptographic material. This _group key_ can then be used as a master key to encrypt the multicast traffic in the group and for message authentication, helping to protect against external adversaries. Specifically, for a multicast group \(\mathcal{R}\), we want the nodes in \(\mathcal{R}\) to obtain a group key \(K\) for which it is infeasible to derive \(K\) for nodes outside of \(\mathcal{R}\).  
We study three strategies. The first one is preloading keys for _all_ possible groups in the network.  
This naive solution is a reasonable choice for networks of small size, say, \(n=4\); however, it quickly becomes impractical for larger values of \(n\). Indeed, in this case, the sender has to hold \(2^{n}-1-n\) group keys (there are \(2^{n}\) subsets of \(\mathcal{N}\); we need not consider the empty set and singleton sets, as these correspond to one-to-one communication for which we already assume we have link keys \(K_{ij}\)). A receiver \(i\) has to hold \(2^{n-1}-1\) group keys; for any nonempty \(U\subseteq\{1,\ldots,n\}\setminus\{i\}\), node \(i\) needs a group key for \(U\cup\{i\}\).  
In the following, we propose a tradeoff where the initial storage (preload) requirements are reduced at the cost of requiring communication to establish \(K\). This is motivated by the observation that out of the exponential number of potential multicast groups considered in the above paragraph, only a few will be eventually used in a real-world use case. We discuss two strategies to realize this tradeoff. Both strategies rely on preloading group keys corresponding to some chosen small family of subsets of \(\mathcal{N}\). The first strategy is to takethe (small) pre-existing groups and build new ones by taking their _unions_. Conversely, the second strategy relies on taking _intersections_ of large pre-existing groups.
# Determination of Hydrogen's Thermophysical Properties Using a Statistical Thermodynamic Method
## 3 Results and Discussion  
### Accuracy of Statistical Thermodynamic Method  
#### 3.1.1 Ortho-H\({}_{2}\) Fraction and Conversion Heat  
The ortho-H\({}_{2}\) fraction in equilibrium H\({}_{2}\) is calculated. As shown in Figure 6, the present calculation agrees well with the data from four scholars [4,19,21,29]. When the temperature exceeds 198 K, the composition of equilibrium H\({}_{2}\) is approximately 75% ortho-H\({}_{2}\) and 25% para-H\({}_{2}\). When the temperature approaches the normal boiling point of liquid hydrogen, almost all of the hydrogen molecules are likely to transfer to the lowest energy level (\(j\) = 0) [29]. As para-H\({}_{2}\) molecules are restricted to even rotational energy levels, 99.8% para-H\({}_{2}\) and 0.2% ortho-H\({}_{2}\) comprise equilibrium H\({}_{2}\).  
Figure 5: Comparison of temperature profiles in VDMLI/VCS-VDMLI [22].  
Figure 6: Equilibrium ortho-H\({}_{2}\) fraction versus temperature [4,19,21,29].  
Figure 7 shows the calculated para-ortho H\({}_{2}\) conversion heat. The present calculation is in accordance with the data reported by three scholars [4; 19; 29]. At low temperatures, para-H\({}_{2}\) molecules tend to be at the most stable energy level (\(j=0\)), so almost all ortho-H\({}_{2}\) molecules are converted to the lowest energy level and extensive energy is released. The para-ortho H\({}_{2}\) conversion heat is maintained above 690 kJ\(\cdot\)kg\({}^{-1}\) if the temperature is less than 73 K. As the temperature rises, some para-H\({}_{2}\) molecules jump to higher energy levels. Therefore, not all ortho-H\({}_{2}\) molecules need to be transferred to para-H\({}_{2}\) molecules, and the energy released in para-ortho H\({}_{2}\) conversion drops [29]. At elevated temperatures (\(T>300\) K), all rotational energy levels of hydrogen molecules are populated, and the rotational energy of para-H\({}_{2}\) approaches that of ortho-H\({}_{2}\), so para-ortho H\({}_{2}\) conversion heat tends toward zero.
## 2 Materials and Methods
### Analytical Methods Used as Reponses in Experimental Design of Extraction Optimization (EO)
#### 2.2.2 Determination of Antioxidant Activity by Using ABTS Assay  
The ABTS assay was conducted according to the method described by [PERSON] et al. [48] with some modifications. In order to prepare ABTS working solution, 4.9 mM of potassium persulfate solution was mixed with 14.9 mM of ABTS radical solution in ratio 1:1 (\(v/v\)) and maintained in the dark at ambient temperature for 12 h. The ABTS working solution was then diluted with a PBS buffer solution of pH 7.4 so as to obtain its absorbance of 0.7 \(\pm\) 0.02 units at wavelength of 734 nm. CS extracts (40 \(\upmu\)L) were allowed to react with 4 mL of the diluted ABTS solution for 6 min in a dark condition. Then, the absorbance of tested mixture was measured at 734 nm. The standard curve was linear between 0.4 and 1.5 mM of Trolox solutions. The ABTS results were expressed as \(\upmu\)mol of Trolox per gram of coffee silverskin dry matter (\(\upmu\)mol Trolox/g CS d.m.).
###### Abstract  
Under the high dynamic conditions, Global Navigation Satellite System (GNSS) signals produce great Doppler frequency shifts, which hinders the fast acquisition of signals. Inertial Navigation System (INS)-aided acquisition can improve the acquisition performance, whereas the accuracy of Doppler shift and code phase estimation are mainly determined by the INS precision. The relation between the INS accuracy and Doppler shift estimation error has been derived, while the relation between the INS accuracy and code phase estimation error has not been deduced. In this paper, in order to theoretically analyze the effects of INS errors on the performance of Doppler shift and code phase estimations, the connections between them are re-deduced. Moreover, the curves of the corresponding relations are given for the first time. Then, in order to have a better verification of the INS-aided acquisition, a high dynamic scenario is designed. Furthermore, by using the deduced mathematical relation, the effects of different grade INS on the GNSS (including Global Positioning System (GPS) and BeiDou Navigation Satellite System (BDS)) signal acquisition are analyzed. Experimental results demonstrate that the INS-aided acquisition can reduce the search range of local frequency and code phase, and achieve fast acquisition. According to the experimental results, a suitable INS can be chosen for the deeply coupled integration.  
INS-aided acquisition; Doppler shift estimation error; code phase estimation error; high dynamic; GNSS signal +
Footnote †: journal: Sensors
# 3D-CNN-Based Fused Feature Maps with LSTM Applied to Action Recognition
## 3 The Proposed Approach
### Extraction of Spatio-Temporal Fused Features
#### 3.1.3 Training of Motion Map Network (MMN)  
Since the network can only handle video frames, the videos need to be processed as video frames. Some methods directly split the videos, ignoring different frame rates, so dynamic information may be inconsistent in time. Therefore, we extract a constant number video frames per second, which improves the generalization performance of the network. For the network to better capture the changes in the action, we extract two frames per second. As for some short videos, we loop the extracted frames, and fill up 16 frames per video.  
We introduce an iterative method to train the Motion Map Network (MMN). We use video \(V\) with \(N\) frames and video labels \(L\) to train our MMN. \(S\) is defined as maximum training iteration length. We train MMN using training length \(s\) from 2 to \(S\). The training length \(s\) is the round of iteration. We cut the training video \(V_{i}\) into \(s\)-length clips \(C_{j}^{i}\) (\(j\in 1\ldots N_{i}/s\)) with overlap 0.7 and assign the labels \(L_{i}\) to clip \(C_{j}^{i}\). We define the MMN as a function \(Z_{\theta_{s}}(L_{a},I_{b})\), where \(\theta_{s}\) denotes the parameters of MMN after the iteration of training length \(s\). The initial parameters of the network are defined as \(\theta_{1}\). For each \(s\), we generate the motion map \(F_{1\sim s-1}^{c_{j}^{i}}\) using \(Z_{\theta_{s-1}}\), and train the MMN using the motion map \(F_{s-1}^{c_{j}^{i}}\), video frame \(f_{s}^{c_{j}^{i}}\) and video clip label \(L_{i}\). Finally, we can get \(\theta_{s}\) which is the parameter of our trained motion map network. The detail of the training steps is summarized in Algorithm 1.  
```
Input:\(V\) is Video dataset; Frame number of video dataset, \(N\); Video labels, \(L\); Maximum training iteration length, \(S\); Parameters of our model, \(\theta_{1}\); Output: Final parameters of Network, \(\theta_{s}\);
1: Initialize the parameter \(\theta_{1}\) for our model;
2:for each \(s\in\)2,3, \(\ldots\) Sdo
3: cut \(V_{i}\) into \(s\)-length clips \(C_{i}^{i}(j\in 1\ldots N_{i}/s)\) with overlap 0.7;
4: Extract the video frames from \(C_{i}^{i}\) as \(F_{i}^{C_{i}^{i}}\);
5:for each \(j\in\)1, 2 \(\ldots\)\(N/s\)do
6:for\(k\in\)1, 2 \(\ldots\)\(s-1\)do
7: Generate the motion map \(F_{k}^{C_{i}^{i}}\) using \(Z_{\theta_{s-1}}(F_{k}^{C_{i}^{i}}-1,f_{k}^{C_{i}^{i}})\)end for
8: Train the MMN using \(F_{s-1}^{C_{i}^{i}},F_{s}^{C_{i}^{i}}\) and \(L_{i}\) endfor
9: Get the MMN parameters \(\theta_{s}\); endfor
```  
**Algorithm 1** Training of Our Motion Map Network
## 2 Model Establishment
### Geometric Model and Material Parameters  
#### 2.5.1 Geometric Model of the Slope  
The geometric model of the slope is shown in Figure 1. The soil is considered as an ideal elastoplastic material, which following the Mohr-Coulomb strength criterion. An 8-node three-dimensional strain pore pressure element (C3D8P) is used to discretize the model. The expansive soil slope has a height of 14 m at the crest and 6 m at the toe, with a slope ratio of 2:3. The width of the slope at the crest and toe is 5 m. The slope soil is divided into three layers: the upper layer is the weathered expansive soil layer with a thickness of 1.5 m, the middle layer is the un-weathered expansive soil layer with a thickness of 1.5 m at the toe and 9.5 m at the crest, and the lower layer is the stiff clay layer with a thickness of 3 m. The total thickness of the three-dimensional slope model is taken as 7 m. The temperature equivalent coefficient of soil expansion is taken as \(1\times 10^{-4}\) based on reference [4]. The material parameters are provided in Table 1. The slope layer A is the weathered soil layer with developed cracks, and its permeability coefficient is 1000 times that of layer B (un-weathered layer) [24].  
Figure 1: Geometric model of the slope (unit: m).
# An Online Reactive Power-Optimization Strategy Based on Load-Curve Prediction and Segmentation
## 4 Discussion  
Through offline and online case studies over a modified IEEE 34-bus test distribution system, the experimental results gave evidence that,  
1. Reactive power optimization based on load curve prediction and segmentation is able to reduce reactive power compensation device adjustment times effectively, reduce network losses during a duty cycle to some extent, and at the same time improve computational efficiency;
2. Compared with using individual load curve for segmentation, using integrated load curve is adequate to produce better optimization performance w.r.t. device adjustment times and to reduce computational burden, at the sacrifice of total network losses to some extent;
3. The optimal adjustment times of tap changers and capacitors have positive correlation with the number of load curve segments;
4. When doing load curve segmentation, filtering method 2 is superior to filtering method 1 in terms of optimization performance.