esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis of the Response Surface Parameters
### Effects of Geometric Factors on the Acceleration Response  
The effects of two interactive factors on the acceleration response of point A are displayed in Figure 6. Figure 6a is for interactive buried depth and curve angle, while Figure 6b is for interactive buried depth and curve radius. From Figure 6a, it can be seen that the acceleration response of the small-radius curved tunnel under seismic excitation tends to increase and then decrease as the buried depth increases, with the minimum value of the acceleration response occurring around the minimum point of the buried depth, and the maximum point of the buried depth has a similarly small acceleration response. When the curve angle is 60\({}^{\circ}\), the overall acceleration response is less than the curve angle of 45\({}^{\circ}\). Figure 6b shows that when the curve radius reaches 300 m and the buried depth reaches 10 m, the acceleration response can be obtained at the minimum value; when the curve radius reaches 350 m and the buried depth reaches 20 m, the acceleration response value is also relatively small. When the curve radius reaches 350 m and the buried depth reaches 20 m, the acceleration response is also relatively small. Combined with Table 5, we can obtain that the buried depth and curve radius are the key parameters affecting the acceleration response value, and the effect of curve angle on the acceleration response value is not significant.  
Figure 7 shows the significant effect of the interactive buried depth and curve radius on the acceleration response. The variation of acceleration response of point A with buried depth and curve radius for the small-radius curved tunnel under seismic excitation is given here, showing that the minimum acceleration can be obtained when both buried depth and curve radius are taken as minimum values.  
Figure 5: Variation of the displacement response with buried depth and radius at point B.  
Figure 8 shows the variation of the acceleration response at point B with buried depth and angle for the small-radius curved tunnel under seismic excitation, indicating that the smallest acceleration at point B can be obtained when the buried depth is taken as a minimum and the curve angle is taken as a maximum.  
Figure 8: Variation of the acceleration response with buried depth and angle at point B.  
Figure 6: Interaction effect of buried depth on acceleration: (**a**) angle (deg); (**b**) radius (m).  
Figure 7: Variation of the acceleration response with buried depth and radius at point A.
## 3 Mitigation Scheme for SAR
### Estimation and Subtraction  
An alternative approach to the incoherent notching is the coherent estimation and subtraction. It consists of a coherent estimation and in-phase subtraction of the disturbing sources [42; 43; 44; 45]. The RFI is characterized with a mathematical model like multiple sinusoidal tones [46; 47], and regard the wideband signal-plus-system noise as white noise. Then the model parameters of each RFI source such as amplitude, frequency, phase, etc., are estimated under certain criterions including least square, maximum likelihood estimator, Bayesian framework, etc. Further, the interference can be reconstructed by the estimated parameters and coherently subtracted from the contaminated echoes. Theoretically, this kind of method could achieve superior performance. However, this assumption is no longer valid as current RFI sources have complicated digital modulation schemes and are constantly changing. Precise modeling and accurate parameter estimation remain as key issues that degrades the performance. Moreover, the computation burden for iterative estimation is large with multiple sources.  
Modelling the interference environment would ideally include information such as the statistics about the density of the interference emitters, the type of such emitters, and their effective radiated power, modulation bandwidth, duty factor, and temporal dependence [48]. For some systems with additional channels or well-designed pulse repetition time, the RFI environment could be dynamically monitored by making use of \"sniffer\" pulses or \"listening beforehand\" schemes [10; 25].
## 3 Results and Discussion  
### Nh\({}_{3}\)-SCR Activity Results  
The effects of temperature on the SCR activities of the honeycomb cordierite-based Mn-Ce/Al\({}_{2}\)O\({}_{3}\) catalyst are illustrated in Figure 1. The activities of the catalyst achieved more than 80% NO conversion at 100 \({}^{\circ}\)C, which is maintained at almost 100% over the entire 150-180 \({}^{\circ}\)C range. Furthermore, the catalyst exhibited excellent low-temperatureNH\({}_{3}\)-SCR activity which enlarged the active temperature window of the catalyst. Considering the energy consumption of flue gas reheating and catalyst efficiency in practical applications, the effects of SO\({}_{2}\) and H\({}_{2}\)O were studied at 100 \({}^{\circ}\)C.
# A Survey on Wireless Body Area Networks for eHealthcare Systems in Residential Environments
## 1 Introduction
### Taxonomy and Requirements
#### 1.2.1 Low-Power Consumption  
Low-power consumption is considered to be one of the most important and challenging requirements in WBAN systems. Devices in WBAN systems mainly consume energy during sensing vital information, wireless communication and data processing. However, compared to sensing information and data computation, wireless communication consumes a significant amount of energy. Thus, reducing the energy consumption of data transmission during communication can conserve considerable amounts of the energy reserves. In almost all WBAN devices, batteries are the main source of power supply, but they are also the largest component in terms of weight and volume compared to other electronic components. This is important because in many WBAN applications such as pacemakers, wearable devices must be able to operate for very long duration of time without being recharged or replaced. Many techniques have been proposed in the past to lower the power consumption of such devices. As an example, an energy-efficient hybrid system has recently been proposed by [PERSON] _et al._[39] to minimize the required transmission energy consumption of such systems by utilizing energy harvesting techniques and low-power MAC protocols. In order to minimize power consumption, it is also important that the upper layer, the application layer, uses a better strategy of sampling and transmitting data that is more convenient for its application. As an example, the system can reduce the sampling rate of pulse when the user is at rest according to the motion sensor. [PERSON] _et al._[40] and [PERSON] _et al._[41] showed how selective sampling strategies can decrease the power consumption of such systems which results in an increase in the deployment lifetime of wearable technologies. Furthermore, authors in [42] believe that, in order to lower the power consumption, it is also possible to reduce the sampling rate below the Nyquist rate while still achieving an acceptable quality reconstruction.  
In the bigger picture, the topology of network and placement of sensors also play a role in power consumption. The network topology consists of multiple healthcare sensors and relay nodes. Location of these network elements are mostly fixed and are in close proximity of each other. In this setup, the messages can be relayed through the network to communicate various physiological parameters. The optimization of network mesh and positioning is explained in [43]. Authors in [44; 45] show that cooperative transmission with the use of relay nodes can boost the efficiency. In [46], [PERSON] _et al._, refined the network/MAC layer towards an energy efficient routing protocol. A joint optimization of positioning and routing is developed in [47]. Finally, in [48] [PERSON] _et al._ considered the quality of service (QoS) requirements.
## 2 Materials and Methods
### Handle Detection Module
#### 2.1.1 Dataset  
To train the handle detector, a suitable dataset must be collected. This dataset should contain images with labeled handles, specifying their positions as bounding boxes. Unfortunately, no publicly available dataset provides a sufficient number of labeled images with handles. Therefore, a new dataset was created for the training, validation, and testing of the neural networks. The datasets used in this research consist of various publicly available images and images captured in different indoor environments. This diversity enhances the variety and resemblance to the desired robot environment.
## 5 Economic Impact of the Integration of SCP Practices
### Access to Markets  
By adopting and implementing SCP practices, MSMEs were able to access alternative markets requiring sustainable or organic produces, selling to different types of customers and signing supply contracts, which allowed farmers to receive regular income and increase their economic stability. In contrast, non-beneficiary MSMEs that did not participate in the SCP training programs and, therefore, did not implement SCP practices, showing limited access to alternative markets, and fewer had supply contracts. Concretely, 30% (N = 24) of beneficiary MSMEs had supply contracts with processing companies and supermarkets, as compared to 8% (N = 6) of non-beneficiary MSMEs. This confirms a correlation between being a project beneficiary that received SCP training and having a supply contract compared to non-beneficiaries. Having access to training and capacity building programs also increased the access to market information among the beneficiary MSMEs. The analysis showed that 55% of beneficiaries (N = 43) of the GOALAN project, that participated in training and capacity building activities, reported to have access to market information in relation to only 13% (N = 10) of non-beneficiaries.
# Vector Magnetic Anomaly Detection via an Attention Mechanism Deep-Learning Model
## 2 Methods
### Data Preparation
#### 2.2.2 Background Noise  
Geomagnetic noise as background noise data does not provide good robustness of the model. The geomagnetic data that can be collected are recorded by different instruments for a limited period at specific locations. To improve the model's generalization, we obtained the colored background noise by transforming the Gaussian white noise in the frequency domain. We set its power spectral density as \(1/f^{\alpha}\) (\(0<\alpha<2\)), then inverted it to the time domain. The typical background noise with the PSD of \(1/f^{1.5}\) is shown in Figure 3.
# Dynamic Data Filtering of Long-Range Doppler LiDAR Wind Speed Measurements
## 2 Methodology
### Interquartile-Range Filter  
The interquartile filter or box plot filter descripted by [PERSON] et al. [20] is not based on a specific data distribution. For filtering, the interquartile-range (IQR) is calculated and will be subtracted to the first and added to the third quartiles. It is a threshold filter based on statistical dispersion. We used the following common parametrisation for valid measurement points \(u_{r}\):  
\[u_{r,25}-1.5 IQR\leq u_{r}\leq 1.5 IQR+u_{r,75} \tag{3}\]  
where \(u_{r,25}\) is the first quartile, \(u_{r,75}\) is the third quartile and IQR is the interquartile range.
# Research on Optimization of Target Positioning Error Based on Unmanned Aerial Vehicle Platform
###### Abstract  
Achieving precise target localization for UAVs is a complex problem that is often discussed. In order to achieve precise spatial localization of targets by UAVs and to solve the problems of premature convergence and easy to fall into local optimum in the original dung beetle algorithm, an error handling method based on the coordinate transformation of an airborne measurement system and the dung beetle optimization with crisscross and 3 Sigma Rule optimization (CCDBO) is proposed. Firstly, the total standard deviation is calculated by integrating the carrier position, the attitude angle, the pod azimuth, the pitch angle, and the given alignment error of the pod's orientation. Subsequently, the Taylor series expansion method is adopted to linearize the approximated coordinate transformation process and simplify the error propagation model. Finally, in order to further improve the positioning accuracy, a target position correction strategy with the improved dung beetle optimization algorithm is introduced. The simulation and flight experiment results show that this method can significantly reduce the target positioning error of UAVs and improve the positioning accuracy by 20.42% on average compared with that of the original dung beetle algorithm, which provides strong support for the high-precision target observation and identification of UAVs in complex environments.  
target positioning; error analysis; airborne optical electronic pod; Monte Carlo +
Footnote †: journal: Applied Sciences  
1
Footnote 1: institutetext: College of Weaponry Engineering, Naval University of Engineering, 717 Jiefang Road, Qiaokou District, Wuhan 430030, China; [EMAIL_ADDRESS] (Y.L.); [EMAIL_ADDRESS] (S.S.); [EMAIL_ADDRESS] (W.Y.)
# Determination of Optimum Frequency for Electromagnetic-Assisted Nanofluid Core Flooding
## 3 Results and Discussion
### Reservoir Rock Sample Characterization  
A combination of a FESEM micrograph and an EDS elemental analysis of the Berea sandstone sample is presented in Figure 5. The dry sandstone grain and pore distribution are shown in Figure 5a, which indicates that the sandstone sample is dominated with macropores with a pore diameter of the order of 100 \(\upmu\)m. This implies that the magnetite nanoparticles will possess ample mobility during core flooding without causing pore throat blockage, as the particle size is less than 50 nm. Moreover, it has been reported that the frequency dependence of \(\varepsilon^{\prime}\) of saturated sandstone greatly depends on pore space surface area to volume ratio [41].  
The elemental distribution of the pore space is indicated in Figure 5b by the EDS spectrum, which is comprised largely of silicon (31.43 wt%) of sandstone silica and oxygen (50.39 wt%) of connate water and carbon (18.18 wt%). Hence, a combination of the pore structure and elemental composition are important background knowledge, which can help determine the influence of the injected fluid content on EM wave propagation within the medium, as will be shown in subsequent sections. The presence of the injected crude oil and saturating nanofluid is confirmed in Figure 5d, which shows the EDS element distribution analysis within a pore space (Figure 5c) of the sandstone sample. A combination of the high carbon (C) content in the spectrum confirms the presence of the saturating crude oil, which is 61.16 wt% of the pore content. The presence of magnetite nanofluid is likewise confirmed by the Fe peak in the EDS spectrum and is relatively sparse (0.92 wt%) due to the small amount (0.05 wt%) of Fe\({}_{3}\)O\({}_{4}\) nanoparticles dispersed in the NaCl used to prepare the nanofluid. This small concentration of nanoparticles was used to avoid nanoparticle retention within the porous structure, as this has been earlier reported [42].  
Figure 4: **(a)** Field emission scanning electron microscope (FESEM) image showing particle size and the shape of a magnetite nanoparticle and (**b**) hysteresis loop, the inset shows a magnified image of the initial magnetization.
## 4 Experiment Setup and Performance Analysis
### Experiment Results and Analysis in the Park Scenarios
#### 4.2.1 Performance Analysis of Macrodiversity Reception Scheme Using Different Number of Antennas  
The channel fading in the park environment is relatively stable because the sensor node and the sink node are stationary. However, the obstacles between sensor node and sink node are different when the location of the sensor node changes, which causes that the channel fading is different. The first experiment mainly analyzes the relationship between the transmission performance of the macrodiversity reception scheme using different number of antennas and the transmission distance in the park scenarios, and a total of seven locations were tested. All antennas in the sink node are in non-line-of-sight (NLOS) transmission. When the transmission distance is less than 1100 m, there are some trees and small buildings between the sensor node and sink node. When the transmission distance is greater than 1100 m, there are some tall buildings near the sensor node. The experiment analyzes PER and FER of macrodiversity reception scheme with different number of antennas varying with the transmission distance in the park scenarios and the experimental results are plotted in Figure 9.  
It can be observed from Figure 9 that the FER of single antenna reception scheme is about \(2\times 10^{-3}\) when the transmission distance is 800 m. Both the single cluster reception scheme using four antennas and the macrodiversity reception scheme can achieve error-free transmission. As the transmission distance increases, the performance of the single cluster reception scheme using four antennas scheme begins to decline. When the transmission distance is 1000 m, the FER of the two local convergence nodes are \(3\times 10^{-3}\) and \(4\times 10^{-3}\) respectively. The macrodiversity reception scheme can still obtain better transmission performance, and its FER is \(4\times 10^{-4}\). When the transmission distance is more than 1100 m, the performance of the three reception schemes decrease rapidly, and the gain of macrodiversity reception scheme decreases.
# Minimally-Invasive Neural Interface for Distributed Wireless Electrocorticogram Recording Systems
## 2 System Architecture  
### Concept of Distributed Wireless ECoG Recording  
Figure 1 shows the schematic diagram of the system implementation. Instead of high-risk craniotomy, a burr hole is opened and a flexible microelectrode array is inserted into the hole. A bolt-shaped implantable interface module is located inside the hole and covered by the skin. The proposed implementation can significantly reduce invasiveness by placing the electrode array over the dura matter, making an operation much simpler and less risky. The recorded ECoG signals are transmitted through the skin, instead of using the conventional wireless communication techniques, such as near or far field communication (RF or inductive coupling). Each bolt-shaped module sends the recorded signals through a low-power wireless ISCOM link to the way station that also resides on the body and relays the data to the external host. An external wireless link for the data exchange between the way station and the host (personal computer or workstation) can employ a standard wireless technology, e.g., WLAN, with a high data rate. The power consumption in the way station is not restricted because it does not need to be implanted inside the body; thus, a relatively large power source can be equipped in the way station. In addition, a digital signal processing (DSP) unit is implemented in the way station where we can cost-effectively perform feature extraction and classification with a modest power budget. One of the advantages in this scheme is that the algorithm for decoding neural signals in the way station can be easily upgraded and customized for a personalized solution. Each interface in the system has multiple components inside, consisting of a low-power, low noise IC for neural signal processing (recording, filtering, and digitization), a rechargeable battery, a wireless power source for the rechargeable battery, and the flexible ECoG and ISCOM electrodes at the bottom. All the components are hermetically sealed inside the enclosure.
# Threat Analysis and Risk Assessment of a Driver Monitoring System
## 4 DMS Architecture and Functional Overview
### Sensors and Hardware Modules  
The system architecture relies on an array of sensors to monitor the driver's status with high accuracy and reliability. Among these, the seat pressure sensor is crucial in detecting whether the driver's seat is occupied. This sensor is a reliable method to confirm the presence of a driver, as it is unlikely that any other object would exert similar pressure on the seat during vehicle operation. Additionally, the seat belt sensor, another widely used component in modern vehicles, ensures that the driver is adhering to safety protocols by wearing the seat belt. The pressure sensor sends a HIGH signal above a specified pressure on the driver's seat; otherwise, it is LOW. The same is valid for the seat belt sensor, which sends a HIGH signal when the seat belt lock is closed and a LOW signal when the seat belt lock is open. However, the most critical element of the sensing module is the infrared (IR) camera mounted above the dashboard and aimed directly at the driver's face. This camera is the primary input for the system's advanced image-based monitoring. It features a resolution of 720P and a horizontal viewing angle of approximately 70 degrees, ensuring comprehensive coverage of the driver's facial area. In addition, the camera is equipped with an IR fill light, allowing it to function effectively in various lighting conditions, including low-light environments.  
The dataflows of the sensors mentioned above are presented below:
## 4 Doppler Shift Estimation Error and Code Phase Estimation Error  
As shown in Figure 4, assuming the position of the satellite n is \(\left( \begin{array}{ccc}x_{\rm s}^{n}&y_{\rm s}^{n}&z_{\rm s}^{n}\end{array} \right)\), the receiver's real position is \(\left( \begin{array}{ccc}x&y&z\end{array} \right)\) and the position of INS is \(\left( \begin{array}{ccc}x_{\rm I}&y_{\rm I}&z_{\rm I}\end{array} \right)\). So the pseudo-range \(\rho_{\rm G}^{n}\) from the receiver to the satellite and the pseudo-range \(\rho_{\rm I}^{n}\) from the INS to the satellite can be expressed as follows:  
\[\rho_{\rm G}^{n}=c(t_{\rm R}-t_{\rm SV})=r^{n}+\hat{\alpha}t+n_{\rho} \tag{15}\]  
\[\rho_{\rm I}^{n}=\sqrt{\left(x_{\rm I}-x_{\rm s}^{n}\right)^{2}+\left(y_{\rm I }-y_{\rm s}^{n}\right)^{2}+\left(z_{\rm I}-z_{\rm s}^{n}\right)^{2}} \tag{16}\]  
where \(\hat{\alpha}t\) is the clock error, \(r^{n}=\sqrt{\left(x-x_{\rm s}^{n}\right)^{2}+\left(y-y_{\rm s}^{n}\right)^{2} +\left(z-z_{\rm s}^{n}\right)^{2}}\) is the geometric distance from the satellite to the receiver, \(n_{\rho}\) is the error of noise. Equation (16) is executed in Taylor expansion, and the following expression is obtained:  
\[\rho_{\rm I}^{n}\approx\sqrt{\left(x-x_{\rm s}^{n}\right)^{2}+\left(y-y_{\rm s }^{n}\right)^{2}+\left(z-z_{\rm s}^{n}\right)^{2}}\ +\ \frac{\partial\rho_{\rm I}^{n}}{\partial x}(x_{\rm I}-x)\ +\ \frac{ \partial\rho_{\rm I}^{n}}{\partial y}(y_{\rm I}-y)\ +\ \frac{\partial\rho_{\rm I}^{n}}{ \partial z}(z_{\rm I}-z) \tag{17}\]  
Assuming \(\hat{\alpha}x\ =\ x_{\rm I}\ -\ x\), \(\delta y\ =\ y_{\rm I}\ -\ y\), \(\delta z\ =\ z_{\rm I}-\ z\), Equation (17) can be expressed as:  
\[\rho_{\rm I}^{n}=\ r^{n}\ +\ \frac{\partial\rho_{\rm I}^{n}}{\partial x}\delta x \ +\ \frac{\partial\rho_{\rm I}^{n}}{\partial y}\delta y\ +\ \frac{\partial\rho_{\rm I}^{n}}{ \partial z}\delta z \tag{18}\]  
\[\left\{ \begin{array}{ll}\frac{\partial\rho_{\rm I}^{n}}{\partial x_{\rm I}} =\frac{x-x_{\rm s}^{n}}{\sqrt{\left(x-x_{\rm s}^{n}\right)^{2}+\left(y-y_{ \rm s}^{n}\right)^{2}+\left(z-z_{\rm s}^{n}\right)^{2}}}=\frac{x-x_{\rm s}^{n} }{r^{n}}=e_{1}\\ \frac{\partial\rho_{\rm I}^{n}}{\partial y_{\rm I}}=\frac{y-y_{\rm s}^{n}}{ \sqrt{\left(x-x_{\rm s}^{n}\right)^{2}+\left(y-y_{\rm s}^{n}\right)^{2}+\left( z-z_{\rm s}^{n}\right)^{2}}}=\frac{y-y_{\rm s}^{n}}{r^{n}}=e_{2}\\ \frac{\partial\rho_{\rm I}^{n}}{\partial z_{\rm I}}=\frac{z-z_{\rm s}^{n}}{ \sqrt{\left(x-x_{\rm s}^{n}\right)^{2}+\left(y-y_{\rm s}^{n}\right)^{2}+\left( z-z_{\rm s}^{n}\right)^{2}}}=\frac{z-z_{\rm s}^{n}}{r^{n}}=e_{3}\end{array} \right. \tag{19}\]  
where \(E=\left[ \begin{array}{ccc}e_{1}&e_{2}&e_{3}\end{array} \right]^{T}\) is the unit vector of the LOS. The pseudo-range \(\rho_{\rm I}^{n}\) from the INS to the satellite is:  
\[\rho_{\rm I}^{n}=r^{n}\ +\ e_{1}\delta x\ +\ e_{2}\delta y\ +\ e_{3}\delta z \tag{20}\]  
Figure 4: Satellite pseudo-range scheme.