esa-sceva/satcom-chunk-collection huggingface dataset:
# Input Shaping Control of a Flexible Structure for Rest-to-Rest and Non-Rest-to-Rest Maneuvers
## 3 Single Switch and ZVDD Input Shaping
### Single-Switch Reference Signal
From Equation (11) with the substitution \(u_{1}=-u_{2}\), simplified expressions for the switching time intervals can be found:  
\[t_{21} = \frac{(c_{3}^{2}\dot{\theta}_{1,1}+c_{1}\dot{\theta}_{2,1})-(c_{3 }^{2}\dot{\theta}_{1,2}+c_{1}\dot{\theta}_{2,2})}{(b_{4}c_{1}u_{2}+b_{2}c_{3}^ {2}u_{2})}=\frac{(c_{3}^{2}\dot{\theta}_{1,1}+c_{1}\dot{\theta}_{2,1})}{(b_{4} c_{1}u_{2}+b_{2}c_{3}^{2}u_{2})} \tag{12}\] \[+2c_{1}c_{3}^{2}t_{v,p-1}\dot{\theta}_{2,v}-2c_{1}c_{3}^{2}\theta _{2,v}\cos(c_{3}t_{v,p-1})+b_{2}c_{3}^{4}(t_{v,p-1})^{2}u_{v}+b_{4}c_{1}c_{3}^ {2}(t_{v,p-1})^{2}u_{v})/(2c_{3}^{4})\]0  
If \((c_{3}^{2}\dot{\theta}_{1,0}+c_{1}\dot{\theta}_{2,0})=0\), then \(t_{10}\) takes the following form:  
\[+2c_{1}c_{3}^{2}t_{v,p-1}\dot{\theta}_{2,v}-2c_{1}c_{3}^{2}\theta _{2,v}\cos(c_{3}t_{v,p-1})+b_{2}c_{3}^{4}(t_{v,p-1})^{2}u_{v}+b_{4}c_{1}c_{3}^ {2}(t_{v,p-1})^{2}u_{v})/(2c_{3}^{4})\]1  
Note that the denominator of Equation (14) is expressed in terms of \(u_{2}\), where \(u_{1}=-u_{2}\). Thus, for a single-switch control to solve the rest-to-rest problem, the time intervals must be identical: \(t_{21}=t_{10}\), where \(t_{f}=t_{21}+t_{10}\). Similarly, using Equations (6)-(9) and (13), simplified expressions can be found for the final states at point V0. \[+2c_{1}c_{3}^{2}t_{v,p-1}\dot{\theta}_{2,v}-2c_{1}c_{3}^{2}\theta _{2,v}\cos(c_{3}t_{v,p-1})+b_{2}c_{3}^{4}(t_{v,p-1})^{2}u_{v}+b_{4}c_{1}c_{3}^ {2}(t_{v,p-1})^{2}u_{v})/(2c_{3}^{4})\]2 \[+2c_{1}c_{3}^{2}t_{v,p-1}\dot{\theta}_{2,v}-2c_{1}c_{3}^{2}\theta _{2,v}\cos(c_{3}t_{v,p-1})+b_{2}c_{3}^{4}(t_{v,p-1})^{2}u_{v}+b_{4}c_{1}c_{3}^ {2}(t_{v,p-1})^{2}u_{v})/(2c_{3}^{4})\]3 (16) \[+2c_{1}c_{3}^{2}t_{v,p-1}\dot{\theta}_{2,v}-2c_{1}c_{3}^{2}\theta _{2,v}\cos(c_{3}t_{v,p-1})+b_{2}c_{3}^{4}(t_{v,p-1})^{2}u_{v}+b_{4}c_{1}c_{3}^ {2}(t_{v,p-1})^{2}u_{v})/(2c_{3}^{4})\]4 (17) \[+2c_{1}c_{3}^{2}t_{v,p-1}\dot{\theta}_{2,v}-2c_{1}c_{3}^{2}\theta _{2,v}\cos(c_{3}t_{v,p-1})+b_{2}c_{3}^{4}(t_{v,p-1})^{2}u_{v}+b_{4}c_{1}c_{3}^ {2}(t_{v,p-1})^{2}u_{v})/(2c_{3}^{4})\]5  
Equations (15)-(18) represent an over-determined system with no solution; there are no values of \(u_{2}\) and \(t_{21}\) that solve the equation set. Clearly, the single-switch torque profile shown in Figure 2 is not sufficient to perform a rest-to-rest rotation of a flexible body. When this torque profile is applied to the rest-to-rest example problem defined in Section 2, the resulting state trajectories are shown in Figure 3 by solving Equations (6)-(9). Note that the states do not all reach zero at the final time.
## 3 Results and Discussion  
### Molecular Structure of DIT-SAL  
The optimized molecular geometry of DIT-SAL is displayed in Figure 5. The optimized bond lengths were determined using the DFT/B3 LYP method with the 6-311++G(d,p) basis set. Naturally, for this new compound, there are no existing data on its structure. We have therefore been restricted to comparing it with dithranol and salicylic acid, which are structurally similar to the two parts of DIT-SAL joined by an ester bond and for which there are data available both from experiments [55] as well as from quantum chemistry computation [56; 57; 58; 59; 60; 61; 62] (Figures 6 and 7); for example, the calculated length of the two hydrogen bonds in dithranol is in very good agreement with the experimental value (1.69 A versus experimental 1.60 A). Also, the experimental length of the C=O bond (1.26 A) is almost identical to the theoretical value (1.25 A). Good agreement between theoretical and experimental values has also been obtained for salicylic acid, and it can therefore be expected that the theoretical values of the structural parameters for DIT-SAL are also reliable and would be useful for further research and the synthesis of this proposed antisporiatic drug.  
Figure 5: Molecular structure of DIT-SAL optimized with B3 LYP/6-311++G(d,p).  
The values of some of the calculated properties of SAL-DIT's structure (dipole moment value, entropy, SCF energy, etc.) are shown in Table 1. The orientation of the dipole moment is shown in Figure 8.  
\begin{table}
\begin{tabular}{l} \hline Dipole Moment = (3.2541, 3.1276, \(-\)1.6948) 4.8211 D \\ Entropy = 148.643 cal/molK \\ Heat Capacity = 79.744 cal/molK \\ Molecular Mass = 346.08412 \\ SCF Energy = \(-\)743680.78 kcal/mol \\ Thermodynamic Energy = 202.013 kcal/mol \\ Zero-Point Energy = 189.608733 kcal/mol \\ \hline \end{tabular}
\end{table}
Table 1: Properties of SAL-DIT’s structure obtained using the DFT method.  
Figure 6: Molecular structure of dithranol optimized with B3 LYP/6-311++G(d,p).  
Figure 7: Molecular structure of salicylic acid optimized with B3 LYP/6-311++G(d,p).  
All the proposed optimized geometries were confirmed to correspond to equilibrium structures through a vibrational analysis, without imaginary normal modes. These frequency calculations also provided us with the predicted IR vibrational spectra (Figure 9).  
Figure 8: Orientation of total dipole moment of SAL-DIT’s optimized structure.  
Figure 9: _Cont._
# Detecting Small Size Mass Loading Using Transversely Coupled SAW Resonator
## 2 Materials and Methods
### PDE Modeling of Transversely Coupled SAW Resonator  
The 2D-COM equations can be numerically solved by the PDE module of COMSOL Multiphysics [21]. Firstly, the geometric model of TC-SAWR was established, as shown in Figure 4. The substrate material and structural parameters of the model are listed in Table 3.  
Here, IDT1 and IDT2 are the input and output ports, respectively.  
After the geometric model is meshed properly, the 2D-COM equations would be solved by the finite element method. Figure 5 demonstrates the calculated transverse modes of order 1\(\sim\)4, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline
**Region** & \(\gamma_{1}\) & \(\gamma_{2}\) \\ \hline Grating & \(-1.3799\) & \(-0.7284\) \\ Busbar & \(-1.3604\) & \(-0.6634\) \\ Free space & \(-1.3928\) & \(-0.5000\) \\ \hline \end{tabular}
\end{table}
Table 1: Anisotropic parameters \(\gamma_{1}\) and \(\gamma_{2}\) of each region.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline SAW velocity & \(3148\) (m/s) \\ Normalized reflection coefficient & \(0.019\) \\ Normalized excitation coefficient & \(2.48\times 10^{-5}\) \\ Static capacitance & \(3.99\times 10^{-11}\) (F) \\ \hline \end{tabular}
\end{table}
Table 2: COM parameters.  
Figure 4: Geometric model of TC-SAWR constructed in COMSOL.  
The port current should be calculated by the following integral.  
\[I=\iint\limits_{Region}-2j\alpha^{*}u_{1}-2j\alpha u_{2-}+j\omega CV \tag{2}\]  
The description of variables in Equation (2) is written above (line 86 to 90).  
Once the port current is determined, the insertion loss (S21) of TC-SAWR is obtained.  
The parameter S21 of the fabricated TC-SAWR device was measured by a VNA (Agilent E5071B Network analyzer). The frequency response of S21 is shown in Figure 6. The measured and calculated center frequencies were 201.13 MHz and 201.11 MHz, respectively. The measured result was in good agreement with the simulation.  
Figure 5: Transverse modes of order 1–4.  
Figure 6: Insertion loss (S21) of TC-SAWR calculated by using 2D-COM (black solid curve) and measured (red dotted curve).
# TrafficWave: Generative Deep Learning Architecture for Vehicular Traffic Flow Prediction
## 2 Methods  
### Mathematical Properties of Traffic Flow  
Traffic flow deals with interactions between travelers (including pedestrians, cyclists, drivers and their vehicles) and infrastructures (including motorways, signage and traffic control devices), in order to understand and develop a transport network with efficient circulation and minimum traffic congestion problems [6; 7]. It is important to underline that spatial and temporal constraints must be considered to properly model the flow [8; 9].  
Let \(X_{i}^{t}\) denote the observed traffic flow for the \(t\)-th time interval at the \(i\)-th sensing location and given a sequence \(\{X_{i}^{t}\}\) of observed traffic flow data points with \(i=1\),..., m, and \(t=1\),..., \(T\), the traffic flow problem aims to forecast the flow for the next (\(t+\Delta\)) time interval under a prediction window \(\Delta\)[10]. The traffic flow can also be defined, such as:  
\[T=\left\{X_{i}^{t}\right\}\,\forall\,i,t\,\in\,\aleph\,i,t\geq 0. \tag{1}\]  
If we consider highways, the traffic flow is generally limited along a one-dimensional path (for example a travel lane).  
Three main variables for displaying a traffic flow: speed (v), density (k), and flow (q). In general-purpose systems, the speed of each vehicle cannot be tracked; therefore, the average speed is measured by sampling the vehicles in a given area for a time period. However, in many other cases, due to the adoption of speeding violations tools, average speed on a segment of the highway and instantaneous speed, can also be monitored (e.g., Safety tutor system on Italian highways).  
The density (\(k\)) is defined as the number of vehicles per length unit. Spacing (\(s\)) is the distance from center to center between two vehicles. The relation between density and spacing is the following:  
\[k=\frac{1}{s}. \tag{2}\]  
Even in this case, density can be estimated in general terms or an extended evaluation of it can be available depending on the specific devices available on the highway. The flow (\(q\)) is the number of vehicles that exceed a reference point per unit of time, its unit of measure is vehicles per hour:  
\[q=kv. \tag{3}\]  
The inverse of the flow is progress (\(h\)), which is the time between the first vehicle passing a reference point in space and its successive vehicle:  
\[q=\frac{1}{h}. \tag{4}\]  
In this work, the main metric is Flow (\(q\)). This value is aggregated with respect to the datasets adopted for experiments by using timestamps of when each car was detected which implies also its speed (\(v\)) and the overall density (\(k\)) over a predefined time window.
###### Abstract
[28] were the first to indicate the importance of detecting individual events and discussing the time detection accuracy to discriminate between false and true positive detections. They reported a mean error of approx. 5 s in individual events detection, however, they admitted the need for further investigation in the field due to restrictions related to small sample size (69 patients) and the use of additional sensing elements [28]. [PERSON] et al. [29] also studied pattern recognition techniques to detect individual events rather than specific sound features for patient classification. However their study--employing only 50 patients--does not report on the time detection error of the episodes but only on the AHI reporting accuracy of -88% [29]. Therefore, the field remains open in more approaches that could enrich our understanding of the sound features that are able to identify apneic or hypopneic events during sleep. In this context and based on the new requirements for deeper study of the characteristics of diagnosed SAS, as described above, we developed a simple algorithm for apnea/hypopnea events detection in patients diagnosed with severe or moderate SAS. The proposed algorithm is based on breathing sound recordings solely, by making use of the annotated events according to the interpretation of polysomnographic data. More specifically, we employ breathing sound recordings from tracheal and ambient microphones during the entire night and we investigate the hypothesis of using standard voice activity detection (VAD) algorithms for identifying breathing sound during sleep. Breathing sound detection, performed by VAD algorithms can provide information on the time detection of apnea and hypopnea events [30]. The proposed algorithm profits from (a) a validation process relying on a multitudinous dataset of patients diagnosed with \"severe\" or \"moderate\" apnea syndrome, (b) increased sensitivity in the detection of apneaic/hypopneic events, and (c) adequate accuracy in the detection of the MAD of a patient with severe or moderate SAS. Thus, although it cannot be used as a standalone process for SAS diagnosis, we envision the use of it for (a) further examination of the patients with SAS at home to improve the treatment they receive, as well as, (b) for deeper understanding of the breathing sound and its potential employment in apnea related applications, and (c) as part of automated systems for apnea scoring in a hospital, which take into consideration the sound characteristics of audio recordings from tracheal or ambient microphones.
## 2 Materials and Methods
### Multi-Head Soft Attention Module  
During the encoding process, we employ the multi-head soft attention module, as illustrated in Figure 4. Each attention head within this module functions as a soft attention. Inspired by [30,31], it encompasses various convolutional operations culminating in a final attention score. This design empowers the model to dynamically allocate attention across features, adapting to the diverse inputs. Unlike conventional attention mechanisms, multi-head soft attention modules can process input representations with spatial dimensions, such as images, feature maps, or other numerical data types. The \(x_{1}\) input from the skip-connection and the \(x_{2}\) input generated by the previous layer are fed into the \(1\times 1\) convolution, turning them into the same number of channels, because \(x_{2}\) comes from the next layer of \(x_{1}\), and the size is \(x_{1}=1/2\), so \(x_{1}\) is downsampled. After that, they are accumulated and passed through ReLU via another \(1\times 1\) convolution and sigmoid activation function. The process involves assigning an importance score, ranging from 0 to 1, to every segment of the feature map. Subsequently, this attention channel map is multiplied by the input of the skip connection, leading to the generation of the final output for the attention block. Following this, the outputs of identical 4 or 8 soft attention modules are concatenated, followed by subsequent average pooling across different attention heads. This ensemble ultimately produces the final output.  
The semantic concatenation layer serves the purpose of connecting the outputs generated by the multi-head soft attention modules. This structure aligns with the framework of the encoder. Utilizing these five initial effective feature channels, the output from the  
Figure 4: The structure of MSAM.  
Figure 3: (**a**) Illustration of CDRCM; (**b**) Illustration of the bottleneck module; (**c**) Illustration of the basic block module.  
preceding layer is concatenated, followed by feature fusion. The method of feature fusion involves upsampling and stacking the feature layers.  
\[x=\text{merge}(x_{1},x_{2}) \tag{1}\]  
In Formula (1), \(x_{1}\) is the corresponding positions in the encoding process, and \(x_{2}\) is the feature from the upper layer convolution output. We upsample the output of the fifth convolutional pooling block to obtain a feature layer of [16; 16; 512], and then change the channel through \(1\times 1\) convolution and connect the output \(x_{1}\) to the fifth convolution pool through jump \(x_{2}\) after block upsampling is used as the two inputs of the multi-head soft attention module, and then the output is concated with \(x_{1}\) to obtain a feature layer of [32; 32; 256], and so on, and finally through the bilinear interpolation method restores the feature layer shape back to the input image size, uses a \(1\times 1\) convolution to adjust the channels, and adjusts the dimension of channels of the final feature layer to num_classes (cloud and non-cloud pixels).
# A Review of Research on Signal Modulation Recognition Based on Deep Learning
## 4 Modulation Recognition Based on Deep Learning in Small Sample Environment
### The Small Sample AMR Algorithm Based on Signal Data Enhancement  
Data enhancement is the most intuitive method to solve the low recognition rate in a small sample environment. The current signal data enhancement methods can be divided into two types. One is to use a confrontational generative network (GAN) to generate virtual data, and the other is to use a semi-supervised learning method to jointly train labeled and unlabeled data to ultimately improve the recognition rate. We will introduce two AMR methods based on signal data augmentation.
# Review of Monitoring and Control Systems Based on Internet of Things
## 3 Monitoring and Control Systems Based on IoT
### IoT-Based Monitoring and Control Systems for Healthcare
Patient data are stored on a server and published to enable medical staff to monitor it remotely. The experiments confirmed the system's effectiveness in monitoring and diagnosing patients' health conditions. Research conclusions suggest that the IoT in healthcare systems can significantly improve the availability and quality of medical services, especially in rural and hard-to-reach regions. By monitoring patients remotely, doctors can keep up to date with the health of their patients, which can lead to faster responses to changes and better health care. [PERSON] et al. in [54] present a comprehensive health monitoring system designed to help diagnose and monitor patients with COVID-19. The system consists of various hardware components, including a microcontroller with a built-in ADC converter, a blood pressure sensor, a non-contact temperature sensor, and an oximeter. The sensors are connected to a Raspberry Pi microcontroller responsible for collecting and transmitting data to the cloud for storage and further analysis. This allows it to automate various systems and cooperate with sensors and external devices. Additionally, the system is scalable, which means it can monitor multiple patients simultaneously, a significant advantage in a medical setting. The system operation process can be divided into three main stages: data collection, data processing, and storage and display of patient parameters. In the first phase, data from the sensors are collected and transferred to the Raspberry Pi microcontroller, where they are processed. The processed data are recorded in the next stage using the I2C interface or other appropriate communication protocols. Finally, the data are stored in the cloud and displayed on a monitor, allowing doctors to quickly and easily monitor patients' condition. While testing the system on various patients, the effectiveness of monitoring health parameters such as heart rate, body temperature, blood pressure, and oxygen level was examined. Comparison test results with commercial sensors showed promising results, with minimal relative errors. The system was also tested for scalability, confirming its ability to monitor multiple patients simultaneously. The research conclusions indicate that the developed health monitoring system based on the IoT can effectively aid in diagnosing and monitoring patients with COVID-19.
# Novel Yellow Aromatic Imine Derivative Incorporating Oxazolone Moiety for Color Resist Applications
## 2 Materials and Methods
### Synthesis of 2,2'-(1,4-Phenylene)bis [4-[(4-Methoxyphenyl)methylylene]-5(4H)-oxazoline (PBMBO)  
1,4-Dibromobenzene (0.3 g, 1.27 mmol), (E)-(4-(4-methoxybenzylidene)-5-oxo-4,5-dihydrooxazol-2-yl) boronic acid (0.72 g, 2.92 mmol), palladium(II) acetate (Pd(OAc)\({}_{2}\)) (0.026 g, 0.09 mmol), and 2-dicyclohexylphosphino-2',4',6'-triisopropylbiphenyl (XPhos) (0.07 g, 0.15 mmol) were added in a 30 mL solution of anhydrous toluene. Upon reaching a reaction temperature of 50 \({}^{\circ}\)C, tetraethylammonium hydroxide (Et\({}_{4}\)(NOH)) (20 wt%) (3 mL) was poured into the reaction mixture. The mixture was then heated to 100 \({}^{\circ}\)C under a nitrogen atmosphere for 5 h. Following the completion of the reaction, the mixture was extracted with dichloromethane and water. The organic layer was subsequently dried over anhydrous magnesium sulfate (MgSO\({}_{4}\)), filtered, and the solvent evaporated. The resulting residue was dissolved in dichloromethane, and ethanol was added. The resulting precipitate was filtered and washed with methanol. The precipitate was further purified with column chromatography using a dichloromethane:n-hexane (1:9) eluent, resulting in solid isolation. After column purification, the solid was reprecipitated using a mixture of dichloromethane and methanol, yielding a yellow solid (0.133 g, 22% yield). 1H NMR (400 MHz, THF-\(d_{8}\)) 8.31 (s, 2H), 8.28 (dd, J = 8.7, 3.3 Hz, 4H), 8.26-8.13 (m, 2H), 7.26 (d, J = 9.6 Hz, 2H), 7.05-7.00 (m, 4H), 3.85 (d, J = 4.0 Hz, 6H).
# Numerical Investigation on the Droplet Dispersion inside a Bus and the Infection Risk Prediction
## 5 Conclusions  
In this paper, the CFD with the Lagrangian approach was used to study the dispersion of droplets generated by the cough of infected persons, and gain the information about the dispersion path and the concentration distribution of droplets. Then, the virus dose inhaled by the passengers in the cabin was obtained for a 4 h bus ride. The infection risk of each passenger was predicted combining with Wells-Riley equations.  
The air supply strategy of \"multi-row air supply and centralized air return\" intensifies the droplet dispersion along the longitudinal, while the asymmetric arrangement of the air supply diffusers causes the droplet to spread laterally in the cabin. Therefore, the droplet distribution in the cabin is fairly uneven, and its distribution is closely related to the location of the infected person and the vent. The direction of the longitudinal airflow in the cabin is determined by the location of the vent, which can affect the distributions of airborne infection risk.  
The time for droplets escaping the cabin is affected by the air supply rate. The larger the air supply rate, the greater the speed of droplet escape, the smaller the infection risk of passengers. When the air supply velocity increased from 2 m/s to 4 m/s, the average infection risk of passengers is decreased by nearly 50%, and the maximum infection risk is decreased from 65% to 30%.  
Figure 12: Infection risk of the passengers after a 4-h journey (**a**) Case 5 (**b**) Case 6 (**c**) Case 7.  
In general, compared to the passengers located on the same side of the infected person, passengers located at the different sides have a lower infection risk, the average infection risk of the different-side's passenger is 50-80% lower than the same side.  
Admittedly, some factors were not taken into account. For instance, the effect of racial and personal on emitted droplets during the cough was ignored, which might consequently influence the infection risk of passengers. We only investigated the dispersion of 5 \(\upmu\)m droplets in this study. As we know, the diameter of coughed droplets range 0.4-200 \(\upmu\)m. It indicates that the infection risk of passengers may be underestimated. What's more. We assume that droplet was delete from the computation domain once it collides with the wall. Which might overestimate the removal efficiency.  
Conceptualization, Y.W.; methodology, X.L.; supervision, C.S.; writing--original draft, Y.Y.; visualization, X.Y.; writing--review and editing, Z.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 51805387.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.