esa-sceva/satcom-chunk-collection huggingface dataset:
# Moving Healthcare AI Support Systems for Visually Detectable Diseases to Constrained Devices
## 2 Related Work
### TinyML
It was found that the local smartphone worked the best in terms of speed and energy efficiency, as it did \begin{table}
\begin{tabular}{c c c} \hline \hline
**Device** & **Memory** & **Speed (GHz)** \\ \hline Raspberry Pi 2 Model B & 1 GB & 0.9 \\ Raspberry Pi 3 Model B & 1 GB & 1.2 \\ Raspberry Pi 4 Model B & Up to 8 GB & 2.5 \\ Xilinx PYNQ-Z1 & 650 MB & 0.5 \\ UDOO BOLT V3 & Up to 32 GB & 2.3 \\ Orange Pi 5 & 8 GB (up to 32 GB) & 2.4 \\ Nvidia Jetson Nano & 4 GB & 1.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Storage capacities and processor speeds of constrained devices. not require network communication. The drawback however was that due to its limited memory, it could only run TensorFlow Lite or other small models. The laptop and 8 GB RPi 4 were able to run more complex models whilst providing fast responses. Although it was not the fastest or most energy-efficient device, the RPi 4 performed at the same standard as a laptop but is evidently a much cheaper and more portable option. There have also been recent developments in tinyML by IBM [21]. IBM's Watson Assistant is a flexible conversational AI platform that can be employed for a variety of use cases, one of them being a healthcare chatbot. It has been used to create an edge computing robot interface for mental health care [22]. Users interacted with the system by responding to specialist questions, which were asked via audio and answered via text. The system was developed on a Xilinx PYNQ-ZI board (see Table 1), which was used as an embedded device that can be placed anywhere within the home. It was programmed in Python 3.9 and operates using a combination of libraries from Google Cloud and IBM Watson. The processing speed of the PYNQ board was evaluated against a PC using an I5 processor, and it was found that, in the worst case, the PYNQ board spent 21 s per question compared to 37 s by the PC. As users interacted with this system, they were able to receive mental health diagnoses after the results were verified by clinical follow-up. More discussion of AI-produced diagnoses and medical verification appears in Section 3.
## 2 Literature Review
### Feature Extraction Network  
Since feature extraction networks form the foundation of image processing algorithms, they are crucial for recognition algorithms. With the advent of ResNet [23], these networks have seen significant improvements in recent years. The primary innovation of ResNet is the introduction of residual connections, which facilitate the training of extremely deep layers, while mitigating the vanishing gradient problem. ResNet has demonstrated impressive performance in tasks such as image classification, object detection, and semantic segmentation. Due to the increased use of embedded systems and mobile devices, there is a growing demand for lightweight 2D feature extraction networks. Traditional deep networks often perform inadequately under resource-constrained conditions, necessitating more efficient solutions. The introduction of the MobileNet [24] series addressed this need. These networks utilize depthwise separable convolutions, which reduce the computational requirements by employing separate depthwise and pointwise convolutions. Consequently, they are well-suited to mobile devices and embedded systems. EfficientNet [25] further advanced this field by proposing automated network structure adjustments. This approach optimizes model performance by balancing the network depth, width, and resolution. Not only does this increase model efficiency, but it also enables automatic adjustments based on the specific requirements of different tasks.  
Research on using the EfficientNet network for behavior recognition is currently underway. [PERSON] et al. [26] applied an improved version of the EfficientNet backbone network to classify behavior data collected by LiDAR, albeit based on a limited dataset. [PERSON] et al. [27] integrated EfficientNet as the feature extraction component in a dual-stream network and enhanced its performance with a multi-head attention mechanism, effectively addressing the challenge of distinguishing similar actions. For detecting car driver behavior, [PERSON] et al. [28] proposed incorporating the EfficientNet-B0 network into a channel attention mechanism. Through the use of comparative analysis with established methodologies, they achieved the highest recognition accuracy with their proposed model. However, it is important to note that channel attention only establishes relationships at the channel level. Incorporating multidimensional attention mechanisms into the backbone network could further improve model performance.
## 3 Results  
### Compound 4-MSP Inhibits TGF-\(\beta\)1-Induced HSC Activation  
From the previous report, we know that activated HSCs are responsible for liver fibrosis, due to the deposition of excessive ECM and increased collagen accumulation mediated by TGF-\(\beta\)1 [11, 13]. Therefore, we first determined whether 4-MSP suppresses the HSC activation through inhibiting the TGF-\(\beta\)1 signaling pathway. First, we performed the western blot to analyze the expressed levels of \(\alpha\)-SMA and collagen 1A1 (col1A1). Results revealed that TGF-\(\beta\)1-induced \(\alpha\)-SMA and col1A1 upregulation were decreased in 4-MSP-treated LX2 cells, especially under the dosage of 5 \(\upmu\)M (Figure 1b). The QPCR analysis was also conducted to investigate mRNA levels of \(\alpha\)-SMA and col1A2. Results showed that 4-MSP treatment reduced the expression levels of \(\alpha\)-SMA and col1A2 in a dose-dependent manner (Figure 1c). In addition, we also found that cells treated with 4-MSP alongside also inhibited the protein and mRNA expression levels of \(\alpha\)-SMA and collagen 1 (Figure 1d,e). Moreover, we used the rat hepatic stellate cell line, HSC-T6, to further confirm the result found in LX2 cells. Similarly, the 4-MSP treatment inhibited the TGF-\(\beta\)1-induced protein and mRNA expression levels of \(\alpha\)-SMA and collagen 1 in HSC-T6 cells (Figure 1f,g), while 4-MSP alongside treatment had no effect on changing the expression levels of \(\alpha\)-SMA and collagen 1 in HSC-T6 cells (Figure 1f,g). TGF-\(\beta\)1 signaling pathway activation is triggered by TGF-\(\beta\)1 interacting with its receptor. The Smad protein is then phosphorylated, and subsequently triggers a production of collagen [8, 11]. Thence, we further investigated whether 4-MSP treatment inhibits Smad pathway activation in TGF-\(\beta\)1-treated HSCs. In Figure 1h, 4-MSP treatment reduced the phosphorylation of Smad2 (60 kDa), relative to that of the control. We also found that 4-MSP alongside treatment reduced the Smad2 phosphorylation (Figure 1i). In HSC-T6 cells, 4-MSP treatment also inhibited the TGF-\(\beta\)1-induced phosphorylation of Smad2 (Figure 1j). To further elucidate whether the 4-MSP influenced the expression levels of TGF-\(\beta\) receptor (TGF-\(\beta\)R), the western blot experiment was performed. The results showed that treatment of 4-MSP had no effect on changing the protein expression levels of TGF-\(\beta\)R in both LX2 and HSC-T6 cells (Figure 1k,l). We also measured the intracellular levels of reactive oxygen species (ROS) in two HSC's cell lines. The results showed that treatment of either TGF-\(\beta\) or 4-MSP did not affect the levels of ROS in both LX2 and HSC-T6 cells (Figure 1m). These results indicate that compound 4-MSP inhibits TGF-\(\beta\)1-induced activation of HSC through direct inhibition of Smad2/3 phosphorylation and \(\alpha\)-SMA/col1A2 expression.
###### Abstract  
The remote center compliance (RCC) mechanism is of great use for practical designs, especially if a pure rotation about a virtual point is required. The analysis of compliance properties and rotational precision for RCC mechanisms are very important for mechanical design in applications where precision is required. This paper formulates an analytical method for the compliance and rotational precision calculations of a class of RCC mechanisms, combined in parallel with two round beam-based isosceles-trapezoidal flexural pivots. The analytical model of the mechanism is established based on the stiffness matrix method to directly obtain the compliance factors that completely define the elastic response of the mechanism. The rotational precision of the mechanism--That is, the position of rotation center--Is then derived using screw theory and a compliance matrix. The validity of this model is demonstrated using finite element analysis simulation and experimental tests. The results of both simulation and experiment verify that the analytical model has high accuracy and promising practical applications. Moreover, the influences of the geometry parameters on the compliance factors and the center shifts are also graphically evaluated and discussed using the analytical model. The results in this paper provide an effective configuration and analytical method for the design and optimization of RCC mechanisms, and are of great practical significance.  
remote center compliance; stiffness matrix; screw theory; rotational precision; finite element analysis +
Footnote †: journal: Applied Sciences
## 3 Methodology
### Piece-Wise Linear RUL and HI  
The performance degradation of equipment may result in changes in sensor measurements, which are rewarding for the application of prognostics techniques. As shown in Figure 1, sensor variation tends either to rise or fall over time in a sequence. In practice, during the entire operation cycle of equipment, there are two phases: the first phase is normal performance, illustrating a relatively flat trend; the second phase is degradation performance, illustrating an approximately exponential dropping trend.  
Consequently, the RUL is difficult to predict in the preliminary state. We presume that the RUL is a constant \(R_{th}\) until it straddles the critical point \(R_{th}\) in the first phase. While in the second phase, we define that RUL is represented by a linear function. Hence, the entire RUL curve is identified as a piece-wise linear function, as shown in Figure 2.  
Furthermore, to characterize equipment health condition, there are two methods: physical parameters [45] and stall margins [44]. Physical parameters are usually too complex to obtain. Effectively, stall margins can satisfy the target of better showing the equipment failure state by transforming the sensor measurement data to a one-dimensional HI.  
In general, the health indicator \(\delta\in[0,1]\), with 0 and 1 corresponding to equipment failure and an intact state, respectively. According to the regularities of RUL distribution regarding the dataset collected, the RUL threshold is ascertained from the predefined critical point \(R_{th}\). Then, the health indicator \(\delta\) of the \(j\)th sequence is formulated as  
\[\delta_{j}^{i}(t)=\frac{(T_{j}-y_{(j)}^{i})}{R_{th}},\;1\leq y_{(j)}^{i}\leq T _{j}. \tag{6}\]  
The sensor data and HI of the dataset are processed by polynomial regression. HI estimation \(\hat{\delta}_{HI}\) at each time-step of the entire dataset is obtained by  
\[\hat{\delta}_{HI}=\alpha+\beta\mathbf{x}^{T}=\alpha+\sum_{i=1}^{m}\beta_{i}x_ {i}, \tag{7}\]  
Figure 1: Sensor measurement data from a piece of equipment.  
Figure 2: Piece-wise linear remaining useful life (RUL).  
where \([\alpha,\beta]=[\alpha,\beta_{1},\beta_{2},\cdots,\beta_{m}]\) represents \(m+1\) dimensional parameters regarding biases and weights. HI trajectories of the dataset are denoted as  
\[HI=\{\hat{\delta}_{j}^{1},\hat{\delta}_{j}^{2},\cdots,\hat{\delta}_{j}^{m}\}, \tag{8}\]  
where \(\hat{\delta}_{j}^{i}\) is the HI estimation at the \(i\)th time-step of the \(j\)th sequence.
# Collision Avoidance Resource Allocation for LoRaWAN
## 6 Coexistence of LoRaWAN with CARA  
A key aspect to consider is the coexistence between CARA and traditional LoRaWAN devices. In order to assess the performance of a LoRaWAN network with a mixture of devices, a new campaign of simulations has been carried out. We employed three set of simulations with different distribution between traditional LoRaWAN and CARA devices. The first set included 25% of LoRaWAN devices and the remaining 75% were CARA devices. In the other sets, the distributions were 50-50% and 75-25%, respectively. These  
Figure 11: Throughput and PER for 1000 devices in the case of non groups of SF with different area dimensions. (**a**) Mean throughput of the system for ADR and CARA cases. (**b**) Packet Error Rate of the system in ADR and CARA cases.  
simulations deployed 1000 devices assuming ideal radio link conditions (i.e., PER matches the collision probability).  
Figure 12 shows the aggregate packet error rate for the different distributions of devices. It is also included the cases with 100% of LoRaWAN devices and 100% of CARA devices. As shown, the collision probability decreases gradually as the percentage of CARA devices increases. This demonstrate that both device types can coexist in the same network.
# Digital Shearing Speckle Pattern Interferometry Based on Rochon Prism and Its Application
## 2 Principle of DSSPI  
After subtraction, the fringe patterns are visible and can be described as [11]:  
\[I_{d}=I(t)-I(0)=I_{0}\Bigg{[}\gamma sin\Bigg{(}\varphi_{0}+\frac{\Delta\varphi}{ 2}\Bigg{)}sin\Bigg{(}\frac{\Delta\varphi}{2}\Bigg{)}\Bigg{]}, \tag{1}\]  
where \(I_{0}\) is the light intensity of background, \(\gamma\) is the amplitude of modulation, \(\varphi_{0}\) is the random initial phase, and \(\Delta\varphi\) is the phase variable quantity that depends on the small deformation of the measured object by the loading. Equation (1) means that a high-frequency carrier \(sin\Big{(}\varphi_{0}+\frac{\Delta\varphi}{2}\Big{)}\) is modulated by a low-frequency factor \(sin\Big{(}\frac{\Delta\varphi}{2}\Big{)}\).  
The deformation derivative can be expressed by [11]:  
\[\frac{\partial w}{\partial x}=\frac{\lambda\Delta\varphi}{4\pi\Delta x}, \tag{2}\]  
where \(\frac{\partial w}{\partial x}\) is the out-of-plane displacement derivative, \(\lambda\) is the wavelength of the illuminant, and \(\Delta x\) is the shearing distance. This means the out-of-plane displacement derivative can be obtained if we acquire the distribution of the phase difference.  
In our previous research, we introduced the TWT to determine the wrapped phase [10]. The continuous wavelet coefficient can be expressed as:  
\[W_{f}(a,b)=|a|^{-1/2}\int\ I(x,y,t)\psi^{*}\Bigg{(}\frac{t-b}{a}\Bigg{)}dt\, \tag{3}\]  
where \(a\) is the scale parameter, \(b\) is the shift parameter, and \(\psi^{*}\) is the mother wavelet. The phase and the amplitude are given by:  
\[A(a,b)=\sqrt{\big{[}Im[W_{f}(a,b)]\big{]}^{2}+\big{\{}Re[W_{f}(a,b)]\big{]}^{ 2}}}, \tag{4}\]  
\[\varphi(a,b)=\arctan\frac{Im[W_{f}(a,b)]}{Re[W_{f}(a,b)]}, \tag{5}\]  
where \(Im\Big{(}W_{f}(a,b)\Big{)}\) is the imaginary part of \(W_{f}(a,b)\) and \(Re\Big{(}W_{f}(a,b)\Big{)}\) is the real part. After unwrapping the phase, \(\frac{\partial w}{\partial x}\) can be solved using Equation (2).
## 4 Results and Discussion
### Simulation Results  
In the block diagram shown in Figure 11, ANN2 is a neural network that describes the controlled object. It was trained and solidified to generate a Simulink module and added to the block diagram. The inside of the BPNN-PID structure was the packed neural network ANN1 and a PID controller. The online learning neural network ANN1 was realized by using the S-function module. There were two kinds of input signals as the setting force: a step signal with an amplitude of 20 N and a sin signal with an amplitude of 20 N and frequency of 0.2 Hz. The simulation data of the three controllers were exported through the simout module.
## 4 Solutions to the Influence Line Equations of the Main Beam
### Solution to the Beam Equation on Elastic Foundation Under the Concentrated Load
#### 4.3.1 Vertical Deflection Curve of the Main Cable in the Main Span  
The main cable in middle span is divided into three sections, corresponding to the hybrid section, pure suspension section, and cable-stayed section, as shown in Figure 5a. The no-hanger main cable only bears its own uniform dead load \(q_{2}\); therefore, it has \(y_{2}^{\prime\prime}=-\frac{q_{2}}{H_{q}^{\prime}},y_{2}^{\prime\prime}+w^{ \prime\prime}=-\frac{q_{2}}{H_{q}+H_{p}}\), where \(y_{2}\) is the left no-hanger main cable's initial shape. By subtracting these two equations, the vertical deflection of the right end of no-hanger main cable caused by the load is \(w^{\prime\prime}=-\frac{H_{p}y_{2}^{\prime\prime}}{H_{q}+H_{p}}\approx\frac{ H_{p}y_{2}^{\prime\prime}}{H_{q}}\). For the right no-hanger main cable, \(y_{2}^{\prime\prime}=y_{4}^{\prime\prime}\), where \(y_{4}\) is the right no-hanger main cable's shape function under the dead load. Similarly, the vertical deflection of the left end of the no-hanger main cable caused by the load is \(w^{\prime\prime}=-\frac{H_{p}y_{4}^{\prime\prime}}{H_{q}+H_{p}}\approx\frac{ H_{p}y_{4}^{\prime\prime}}{H_{q}}=\frac{H_{p}y_{2}^{\prime\prime}}{H_{q}}\).  
Therefore, the deflection curve of the main cable \(w(x,\xi)\) is as follows:  
\[w(x,\xi)=\left\{\begin{aligned} &\frac{H_{p}y_{2}^{\prime\prime}}{2H_{q}}( \frac{L}{2}+x)(L_{xl}-\frac{L}{2}-x)+\frac{v(L_{xl}-\frac{L}{2},\xi)}{L_{xl}}( \frac{L}{2}+x)&-\frac{L}{2}\leq x<L_{xl}-\frac{L}{2}\\ & v(x,\xi)& L_{xl}-\frac{L}{2}\leq x\leq\frac{L}{2}-L_{xl}\\ &\frac{H_{p}y_{2}^{\prime\prime}}{2H_{q}}(x-\frac{L}{2}+L_{xl})( \frac{L}{2}-x)+\frac{v(\frac{L}{2}-L_{xl},\xi)}{L_{xl}}(\frac{L}{2}-x)& \frac{L}{2}-L_{xl}<x\leq\frac{L}{2}\end{aligned}\right. \tag{35}\]
# Hyperspectral Imaging to Evaluate the Effect of Irrigation Water Salinity in Lettuce
## 3 Results and Discussion
### Models and Data Processing
#### 3.3.3 Comparison between Spectral Indexes
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Index** & **F-Fisher** & \(p\)**-Value** & & **Mean** & **Standard Error** \\ \hline \multirow{4}{*}{LSI} & \multirow{4}{*}{3440.2} & \multirow{4}{*}{0} & Ct & 0.01765a & 0.00045 \\  & & & S1 & 0.03130b & 0.00045 \\  & & & S2 & 0.05175c & 0.00045 \\  & & & S3 & 0.07829d & 0.00045 \\ \hline \multirow{4}{*}{NDVI} & \multirow{4}{*}{137.3} & \multirow{4}{*}{2.45 \(\times\) 10\({}^{-88}\)} & Ct & 0.82469a & 0.00080 \\  & & & S1 & 0.80969b & 0.00080 \\  & & & S2 & 0.80345c & 0.00080 \\  & & & S3 & 0.80714d & 0.00080 \\ \hline \multirow{4}{*}{NDVI\({}_{\text{705}}\)} & \multirow{4}{*}{874.2} & \multirow{4}{*}{0} & Ct & 0.45693a & 0.00076 \\  & & & S1 & 0.46466b & 0.00076 \\  & & & S2 & 0.47856c & 0.00076 \\  & & & S3 & 0.50774d & 0.00076 \\ \hline \multirow{4}{*}{RPI} & \multirow{4}{*}{686.4} & \multirow{4}{*}{0} & Ct & 3.48135a & 0.00864 \\  & & & S1 & 3.54853b & 0.00864 \\  & & & S2 & 3.64758c & 0.00864 \\  & & & S3 & 3.99075d & 0.00864 \\ \hline \multirow{4}{*}{Chl\({}_{\text{green}}\)} & \multirow{4}{*}{737.2} & \multirow{4}{*}{0} & Ct & 2.26632a & 0.00731 \\  & & & S1 & 2.28408a & 0.00731 \\  & & & S2 & 2.39855b & 0.00731 \\  & & & S3 & 2.69576c & 0.00731 \\ \hline \multirow{4}{*}{Chl\({}_{\text{red edge}}\)} & \multirow{4}{*}{1024.1} & \multirow{4}{*}{0} & Ct & 1.78446a & 0.00583 \\  & & & S1 & 1.84552b & 0.00583 \\  & & & S2 & 1.94359c & 0.00583 \\  & & & S3 & 2.20687d & 0.00583 \\ \hline \multirow{4}{*}{PSRI} & \multirow{4}{*}{119.8} & \multirow{4}{*}{4.38 \(\times\) 10\({}^{-77}\)} & Ct & \(-\)0.00536a & 0.00013 \\  & & & S1 & \(-\)0.00706b & 0.00013 \\  & & & S2 & \(-\)0.00552a & 0.00013 \\  & & & S3 & \(-\)0.00366c & 0.00013 \\ \hline \multirow{4}{*}{WI} & \multirow{4}{*}{53.4} & \multirow{4}{*}{2.03 \(\times\) 10\({}^{-34}\)} & Ct & 1.28100a & 0.00036 \\  & & & S1 & 1.27616b & 0.00036 \\  & & & S2 & 1.27872c & 0.00036 \\  & & & S3 & 1.28220d & 0.00036 \\ \hline \multirow{4}{*}{NDVI\({}_{\text{green}}\)} & \multirow{4}{*}{1160.7} & \multirow{4}{*}{0} & Ct & 0.53842a & 0.00108 \\  & & & S1 & 0.50228b & 0.00108 \\  & & & S2 & 0.47366c & 0.00108 \\ \cline{1-1}  & & & S3 & 0.45415d & 0.00108 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Analysis of variance (F-Fisher and \(p\)-value) comparing the values of indexes for the four salinity classes (\(n\) = 7000 spectra per class belonging to the calibration set of leaves). Mean values and standard error of the different indexes per salinity class. Means followed by the same letter are not significantly different by Tukey–Kramer test; different letters implies there is significant difference between means. Although seven of the eleven indexes reviewed from literature showed good performance for salinity effect detection, the LSI index, specifically defined for images of lettuces acquired under laboratory conditions, exhibited the best behavior, with the highest _F_-value (Table 3).
# CFD Simulation of Dry Pressure Drop in a Cross-Flow Rotating Packed Bed
## 4 Results and Discussion
### Cavity Zone A
#### 4.2.2 Effect of the Gas Flow Rate  
The effect of the gas flow rate on the pressure drop in the cavity zone A is shown in Figure 6. As the gas flow rate increases at 200 rpm, the pressure drop increases rapidly from 6.5 Pa to 97 Pa due to the formation of more swirls at high gas flow velocities (Figure 6a), while the static pressure decreases from 2.5 Pa to \(-\)4 Pa due to the conversion of static energy to kinetic and inner energy. However, note that as the gas flow rate increases from 200 m\({}^{3}\cdot\)h\({}^{-1}\) to 1000 m\({}^{3}\cdot\)h\({}^{-1}\) at the rotating speed of 200 rpm, the dynamic pressure drop increases markedly from 4 Pa to 101 Pa (Figure 6c). This is because more kinetic energy is transformed to static energy, and more energy is consumed by swirls formed at high gas flow rates. In the cavity zone A, the dynamic pressure drop accounts for a major portion of pressure drop at rotating speeds below 800 rpm, while the static pressure plays an important role in pressure drop at a gas flow rate of 200 m\({}^{3}\cdot\)h\({}^{-1}\).  
Figure 4: The pressure drop in cavity zone A for three turbulence models at different rotating speeds. (**a**) Gas flow rate of 200 m\({}^{3}\cdot\)h\({}^{-1}\); (**b**) gas flow rate of 800 m\({}^{3}\cdot\)h\({}^{-1}\).