esa-sceva/satcom-chunk-collection huggingface dataset:
# Dead-Beat Control Cooperating with State Observer for Single-Phase Electric Springs
## 3 Dead-Beat Control for Single-Phase ESs  
### System Modeling of Single-Phase ES  
As explained in Reference [11], by neglecting the dynamics of the ES DC bus, the equations of an ES are linear and time-invariant as explicated hereafter to simplify the ES modeling, both CL and NCL are taken of resistive type.  
Applying Kirchhoff's Current Law (KCL) to the circuit in Figure 1 yields  
\[i_{3}=C\frac{dv_{ES}}{dt}-i_{L} \tag{1}\]  
\[\frac{i_{3}R_{3}+v_{ES}}{R_{2}}+i_{3}=i_{1} \tag{2}\]  
Applying Kirchhoff's Voltage Law (KVL) yields  
\[L\frac{di_{L}}{dt}+v_{ES}=v_{i} \tag{3}\]  
\[L_{1}\frac{di_{1}}{dt}+i_{1}R_{1}+v_{ES}+i_{3}R_{3}=v_{G} \tag{4}\]  
Solving Equations (1)-(4) yields  
\[\left\{\begin{array}{l}\frac{di_{1}}{dt}=-\frac{v_{ES}}{L}+\frac{v_{i}}{L}\\ \frac{dv_{ES}}{dt}=\frac{i_{L}}{C}-\frac{v_{ES}}{C(R_{2}+R_{3})}+\frac{R_{3}i_ {1}}{C(R_{2}+R_{3})}\\ \frac{di_{1}}{dt}=-\frac{R_{2}v_{ES}}{L_{1}(R_{2}+R_{3})}-\frac{(R_{1}R_{2}+R _{3}R_{3}+R_{2}R_{1})i_{1}}{L_{1}(R_{2}+R_{3})}+\frac{v_{G}}{L_{1}}\end{array}\right. \tag{5}\]  
Applying again KVL to the circuit of Figure 1 also yields  
\[v_{S}=v_{ES}+i_{3}R_{3} \tag{6}\]  
\[v_{S}=v_{G}-i_{1}R_{1}-L_{1}\frac{di_{1}}{dt} \tag{7}\]  
Solving Equations (5)-(7) yields  
\[v_{S}=\frac{R_{2}}{R_{2}+R_{3}}v_{ES}+\frac{R_{2}R_{3}}{R_{2}+R_{3}}i_{1} \tag{8}\]  
Based on the equations above, the state-space model of the ES can be written as follows:  
\[\left\{\begin{array}{l}\dot{x}=Ax+Bu\\ y=Cx\end{array}\right. \tag{9}\]  
where,  
\[x=\left[\begin{array}{c}i_{L}\\ v_{ES}\\ i_{1}\end{array}\right]\text{, }u=\left[\begin{array}{c}v_{G}\\ v_{i}\end{array}\right]\text{, }y=v_{S}\text{,}\]  
\[A=\left[\begin{array}{cc}0&-\frac{1}{C}&0\\ \frac{1}{C}&-\frac{1}{C(R_{2}+R_{3})}&\frac{R_{2}}{C(R_{2}+R_{3})}\\ 0&-\frac{R_{2}}{L_{1}(R_{2}+R_{3})}&-\frac{R_{1}R_{2}+R_{3}R_{3}}{L(R_{2}+R_{ 3})}+\frac{R_{1}}{C(R_{2}+R_{3})}\end{array}\right]\text{, }B=\left[\begin{array}{cc}0& \frac{1}{L}\\ 0&0\\ \frac{1}{L_{1}}&0\end{array}\right]\text{, }C=\left[\begin{array}{cc}0&\frac{R_{2}}{R_{2}+R_{3}}& \frac{R_{2}R_{3}}{R_{2}+R_{3}}\\ \end{array}\right]\text{.}\]
###### Abstract  
This study aimed to develop and assess the effectiveness of a web-based nutrition education program for self-managing IBS symptoms using the IMB model. This study used single-arm, pre-post study design to test the effectiveness of the nutrition education program after its development. Participants were adults in their twenties and thirties in South Korea with IBS according to the ROME IV diagnostic criteria (n = 49). For statistical analysis, normality was verified using the Shapiro-Wilk test, and variables that met the assumption of normality were analyzed using the paired _t_-test, and variables that did not meet the assumption of normality were analyzed using the Wilcoxon signed-rank test. \(p\) < 0.05 was considered statistically significant. IBS patients (n = 49) experienced a statistically significant increase in nutrition knowledge after receiving nutrition education (_p_ = 0.015). IBS patients (n = 35) with no prior experience in nutrition education or diet modification also demonstrated a statistically significant increase in self-efficacy (_p_ = 0.044) and nutrition knowledge (_p_ = 0.016). The web-based nutrition education program based on the IMB model developed in this study was found to be effective. These results will contribute to diversifying symptom prevention strategies for patients with IBS.  
Keywords:iritable bowel syndrome; nutrition therapy; telemedicine; program development; program evaluation +
Footnote †: journal: Applied
# A Statistical Approach to Detect Jamming Attacks in Wireless Sensor Networks
###### Abstract  
Wireless Sensor Networks (WSNs), in recent times, have become one of the most promising network solutions with a wide variety of applications in the areas of agriculture, environment, healthcare and the military. Notwithstanding these promising applications, sensor nodes in WSNs are vulnerable to different security attacks due to their deployment in hostile and unattended areas and their resource constraints. One of such attacks is the DoS jamming attack that interferes and disrupts the normal functions of sensor nodes in a WSN by emitting radio frequency signals to jam legitimate signals to cause a denial of service. In this work we propose a step-wise approach using a statistical process control technique to detect these attacks. We deploy an exponentially weighted moving average (EWMA) to detect anomalous changes in the intensity of a jamming attack event by using the packet inter-arrival feature of the received packets from the sensor nodes. Results obtained from a trace-driven simulation show that the proposed solution can efficiently and accurately detect jamming attacks in WSNs with little or no overhead.  
wireless sensor networks; jamming attack; exponentially weighted moving average; inter-arrival time +
Footnote †: journal: Sensors
# Small-Satellite Synthetic Aperture Radar for Continuous Global Biospheric Monitoring: A Review
## 3 Synthetic Aperture Radar Satellite Missions
### Microsatellites  
Sub-100 kg microsatellites are currently the lightest small-satellite class in which SAR has been implemented. Many of these SAR satellites are being developed by start-up companies for commercial purposes and feature rapid design iterations between generations.
## Materials and Methods
### Tested Satellite Receivers
The horizontal accuracy specified by the manufacturer is 7 m 2 drms (2 distance rms, i.e., 95% of measurements) in static mode, 0.7 m 2 drms by using post-processed differential correction data, 2-3 m 2 drms by using real-time differential correction data from EGNOS or land-based systems, e.g., a private broadcast via RTCM (Type 1 or 9) standard. The low-cost satellite receiver assembled at the Department of Agricultural, Food and Forest Sciences (SAAF) of the University of Palermo, Quanum GPS Logger V2 (named QLV2), with built-in GPS U-Blox NEO-6M receiver, is an autonomous device receiving energy from a reserved channel so that it needs no additional battery. This receiver has 50 channels, a position update rate of 1 Hz (1 position s\({}^{-1}\)), a size of \(24\times 77\times 18\) mm and a weight (without battery) of 43 g, and is powered by an external battery and is able to log travelled distance, starting and ending position of a route, date, UTC (Universal Time Coordinated), route and speed (also the maximum one). The data can be shown on the built-in backlighted LCD display. The differential correction data in NMEA (National Marine Electronics Association) standard can be easily downloaded to a PC so that the positions can be displayed in mapping websites (e.g., Google Earth). This instrument is able to receive only L1 signal (C/A code) from GPS and SBAS satellites (via NMEA 0183 V 4.10 standard). The accuracy specified by the manufacturer is 2.5 m by using GPS satellites, 2 m by using SBAS satellites. The tested external antennas were the following:  
* Dual-frequency (L1/L2) Stonex geodetic antenna with built-in ground plane, able to receive signals from GPS, GLONASS, BeiDou and SBAS satellites and used together with the receivers Stonex S7-G and S5 for removing the errors due to multipath effect and, therefore, achieving better accuracy;
* Low-cost A-Info Sci. & Tech. GPS L1 survey antenna JXATGPSL1_S, that was used only together with the receiver Thales MobileMapper Pro. In fact, if the ground plane of the choke ring survey antenna (with the height in accordance with the whole or half or quarter of the wavelength of the L1 signal) is used, the interference multipath signals are rejected by the antenna ring. Even if the achievable positioning accuracy improves with the increasing cost of the external antenna, it is also possible to obtain optimum results by using a low-cost antenna. In fact, under ideal conditions, i.e., with no or little multipath effect, low-cost antennas do not differ considerably from survey-grade antennas [11].
## 5 Conclusions  
Reliable transmission has become the main challenge of maritime wireless sensor networks due to infinite multipath effects in the ocean scenarios. In this paper, we propose a highly reliable macrodiversity reception scheme based on multiple hard-decision receivers for maritime wireless sensor networks. The scheme indicates that multiple antennas are divided into several clusters placed at different locations in the sink node, and the multiple packages are combined using the cascaded combing scheme in the sink node, which can achieve lower complexity and higher reliability. Furthermore, we construct a maritime wireless sensor networks experiment platform and conduct experiments to verify the performance of the proposed macrodiversity reception scheme in different scenarios. The experimental results show that the proposed macrodiversity reception scheme has a transmission gain of 3-4 dB compared with the single antenna reception scheme at PER = \(10^{-2}\) and has higher transmission gain than the single cluster using same number of antennas reception scheme in the ocean scenarios.  
[PERSON] proposed the idea and revised the manuscript. [PERSON] constructed experimental platform and wrote the paper. [PERSON] and [PERSON] participated in designing the scheme. [PERSON] and [PERSON] participated in experiment. All authors have read and agreed to the published version of the manuscript.  
Figure 14: Performance comparison of diversity scheme with different antenna configuration schemes. (**a**) Comparison of PER between macrodiversity reception scheme with two clusters using four antennas and single cluster using eight antennas reception scheme. (**b**) Comparison of FER between macrodiversity reception scheme with two clusters using four antennas and single cluster using eight antennas reception scheme.  
Funding: This work was supported in part by the Director's Funding from Pilot National Laboratory for Marine Science and Technology (Qingdao) under the grant QNLM201712.  
The authors declare no conflict of interest.
## 2 Review of Related Works and Technological Trends
### Review of Literature
#### 2.3.2 Studies on Platooning Using a Driving Simulator  
[PERSON] et al. [18] used a driving simulator to evaluate the HMI system of truck platooning. Among the three types of HMI (numerical, graphical, and hybrid), the numerical and graphical types were preferred by 60% of the participants because of their ease of understanding platooning situations. The study also determined that a 10 m intervehicle distance significantly increased drivers' psychological stress. The essential information displayed by the HMI included vehicle control status (graphical), speed, and current and target intervehicle distances, whereas acceleration and fuel efficiency information were regarded as less important. The participants emphasized the need to clearly display the driving speed and distance to the leading vehicle.  
[PERSON] et al. [19] analyzed the psychological stress caused by short intervehicle distances during platooning. Participants engaged in experiments in manual and automated driving environments with intervehicle distances of 20-30 m for manual driving and 4-12 m for automated driving. The results showed that short intervehicle distances significantly increased driver stress, and abrupt deceleration by the leading vehicle was the main cause of increased stress in the following drivers.  
[PERSON] et al. [20] analyzed the stress changes associated with different types of tasks assigned to drivers. Three scenarios were considered: (1) No task, where drivers simply monitored the road; (2) Voluntary task, where drivers performed tasks of their choice; and (3) Detection task, where drivers were required to detect vehicle colors as instructed by experimenters. The results showed that the Detection task had a high detection rate (95%) but also resulted in higher stress levels. In contrast, the Voluntary task resulted in relatively lower stress. Additionally, the average heart rate tended to decrease with increasing scenario repetitions due to the time-on-task effect.  
[PERSON] et al. [21] evaluated a platooning partner-matching system using an agent-based simulation model. They applied two algorithms, the first-viable match and best match, considering real-time matching during vehicle stops and opportunistic matching during highway driving. An experiment, conducted at the Port of Rotterdam and nearby highways, confirmed that the main factors determining the economic viability of platooning were wage reduction and feasibility of platooning between trucks from different manufacturers.
## 3 Background  
### Autoencoders  
Autoencoders are commonly used for dimensionality reduction of multidimensional data as a powerful non-linear alternative to PCA or matrix factorization [32; 33; 34]. If a linear activation function is used, the autoencoder becomes virtually identical to a simple linear regression model or PCA/matrix factorization model. When a nonlinear activation function is used, such as rectified linear unit (ReLU) or a sigmoid function, the autoencoder goes beyond the PCA, capturing multi-modal aspects of the input distribution [12; 35]. It is shown that carefully designed autoencoders with tuned hyperparameters outperform PCA or K-Means methods in dimension reduction and characterizing data distribution [36; 37]. They are also more efficient in detecting subtle anomalies than linear PCAs and in computation cost than kernel PCAs [32; 38].  
A traditional autoencoder is a feed-forward multi-layer neural network which is trained to copy its input into the output. To prevent identity mapping, deep autoencoders are built with low dimensional hidden layers by creating non-linear representation of input data [32]. An autoencoder is trained to encode the input \(x\) into some latent representation \(z\) so that the input can be reconstructed from that lower dimensional representation. An autoencoder is trained by unsupervised learning to learn how to build its original input with a minimum reconstruction error. Figure 1 depicts a typical autoencoder network structure with one hidden layer, being composed of two parts: an encoder and decoder. Deep autoencoders learn a non-linear mapping from the input to the output through multiple encoding and decoding steps. An autoencoder takes an input vector \(x\in R^{d}\), and first maps it to a latent representation \(z\in R^{d^{\prime}}\) through the mapping:  
\[z=f_{\theta}(x)=Wx+b \tag{1}\]  
where the function \(f_{\theta}\) represents the encoding steps and parameterized by \(\theta=\{W,b\}\). \(W\) is a \(d^{\prime}\times d\) weight matrix and \(b\) is a bias vector. The lower dimensional latent representation of the input is then mapped back to a reconstructed vector \(x^{\prime}\in R^{d}\) in the input space  
\[x^{\prime}=g_{\theta^{\prime}}(z)=W^{\prime}z+b^{\prime} \tag{2}\]  
where the function \(g_{\theta^{\prime}}\) represents the decoding steps and parameterized by \(\theta^{\prime}=\{W^{\prime},b^{\prime}\}\). The autoencoders training procedure consists of finding a set of parameters \(\{W,\ b,\ W^{\prime},b^{\prime}\}\) that make the reconstructed vector \(x^{\prime}\) as close as possible to the original input \(x\). The parameters of the autoencoder are optimized by minimizing a loss function that measures the quality of the reconstructions. The loss function of an autoencoder is sum-of-squared differences between the input and the output. Therefore, an autoencoder tries to minimize the following loss function during the training:  
\[L(x,x^{\prime})=\sum\
olimits_{x\in\varphi}\sum\
olimits_{i=1}^{d}{\left\|x^ {i}-x^{\prime i}\right\|}^{2}, \tag{3}\]  
where \(\varphi\) is the training dataset.
## 1 Introduction
To address the complex problems of a high dimensionality, non-linearity, and difficulty in establishing the historical decline process of aero-engine monitoring parameters, this paper proposes an MLP aero-engine remaining useful life prediction method based on random forest and Bayesian updating. The random forest algorithm was introduced to analyze and evaluate the degree of influence of each monitoring feature on the remaining life cycle of the engine and calculate the corresponding weights. This was carried out to effectively solve problems such as the large number of engine monitoring parameter features and the difficulty in extracting them, and to eliminate the features with the worst weight score values. Extracted parameters were exponentially smoothed to reduce noise interference. An RUL training model for MLP engines based on Bayesian parameter updating was then constructed, and the remaining service life probability density distribution of the aeroengine was derived. The expectation and standard deviation of the RUL prediction were obtained. The method proposed in this paper was validated using NASA's C-MAPPS dataset, and the RUL prediction results were evaluated using the RMSE and \(R^{2}\). The results show that the accuracy of the engine RUL prediction can be effectively improved compared with other methods. Our methodology has been shown to significantly enhance the accuracy of RUL prediction for engines.
# A Single Wearable Sensor for Gait Analysis in Parkinson's Disease: A Preliminary Study
## 2 Design of the Proposed System
### Extraction of Walking Segments  
To better focus on walking segments and so to remove activity windows that did not include walks, the periodicity of the signal was analyzed. Rapid movements of the wearable sensors, sitting and standing up and other kinds of activities were excluded from further process. On the contrary, activities such as walks, walks while turning, walks while ascending or descending stairs were considered.  
The activity segments computed in the previous step were divided into consecutive five-seconds windows and were analyzed in the frequency domain. The Power Spectral Density (PSD) derived from anterior posterior acceleration reflects the variability and regularity of the gait pattern. The extraction of walking segments were based on the characteristics of the dominant peak of the main frequency in the PSD of the 0.5 to 3 Hz band [29]. In detail, for each five-seconds sliding window, the local maxima were found. If three or more of these maxima exceeded a threshold set at 75% of the absolute maxima, the window was not considered for the walking extraction. If more than 60% of number of sliding windows, in which the original activity segment was divided, were not recognized as walking bouts, the entire activity segment was discarded. Moreover, the Attitude and Heading Reference System (AHRS) was adopted to provide a unique measure of body segment orientation on which the wearable sensor was positioned [31]. Therefore, the quaternions and the raw data measured by the inertial wearable sensor were used  
Figure 3: The superposition of the \(x\)-axis (red), \(y\)-axis (blue), and \(z\)-axis (green) components of acceleration and the extracted activity segments (yellow) as a function of time.  
to compute the acceleration rotated providing acceleration components in the Earth's reference system [32].
## 3 Results  
### Baseline Data of Participants  
A total of 51 patients were included in the study. This is three more participants than expected in the sample size calculation to ensure the representativeness of the target population. Of the 51 patients included, 35 (68.63%) individuals remained with the device for 4 or 7 days without the need for invasive ventilatory support, whereas 16 (31.37%) patients discontinued its use because they needed tracheal intubation, with a significant difference between these two groups (\(p<0.05\)). Considering the 35 patients who remained with the device, completing the study follow-up period, 18 (51.43%) used it for four days and 17 used it (48.57%) for seven days. The need for invasive ventilatory support we considered as one of the possible outcomes of patients' progress to more severe stages of the disease.  
Regarding the demographic data of the 51 study patients, 28 were male, whereas 23 were female. Of the 23 women who were included in the study, 12 (52.18%) did not need mechanical support for ventilation, whereas 11 women (47.82%) did (\(p>0.05\)) (Table 1). However, in the case of males, 23 men (82.14%) were not intubated after inclusion in the study, whereas five men (17.86%) required invasive ventilation (\(p<0.05\)) (Table 1). The average age of the two groups was similar: 65 years old. However, the age group was different, where patients who remained with the device for 4 or 7 days were between 41 and 97 years old, and the intubated group of patients was between 26 and 86 years (Table 1). The non-rebreathing mask and the high-flow nasal catheter were the oxygenation devices used by the patients in the study. The high-flow nasal catheter was the primary oxygenation device used by patients who did not require mechanical ventilation (\(n=25\) or 86.21%), whereas the non-rebreathing mask was the most used device among patients who were intubated (\(n=12\) or 54.55%) (Table 1).  
were intubated the frequency of hypertension was 47.6% (\(n=17\)) and the frequency of diabetes was 33.3% (\(n=7\)). Obesity and cardiovascular disease were also clinical conditions reported by study patients, with a relative frequency between 3 and 10%.  
Figure 3: Comorbidity of study patients: (**a**) patients who remained with the device for 4 or 7 days, and (**b**) patients who required invasive ventilation (intubation).  
Figure 2: Symptomatology reported by study patients before hospitalization: (**a**) patients who remained with the device for 4 or 7 days and (**b**) patients who required invasive ventilation (intubation).
## 2 Materials and Methods
### Data Analysis
#### 2.2.3 Validation  
In feature selection, the subsets of the 25 joint kinematic features were carefully selected to avoid any overfitting of the proposed classification model. Preprocessing of the statistical data helped us to avoid overfitting the classification model, using non-overlapping features with multiple input features. Additionally, we applied 10-fold cross-validation [21] to improve the validity of the proposed model. In the cross-validation, 24 subjects were allocated to ten subfolders; one of these ten was used to test the model. The remaining subfolders were used to train the model. The procedure was intended to develop a robust model for lookout activity classification that could describe an unseen navigator's lookout behavior. Finally, the validated model for lookout activity classification was validated using directly observed record of lookout activity. The non-scenario navigation experiments of the three additional participants were directly observed by trained observers and recorded with a video camera for cross-validation. Every five seconds, the direct observer recorded a representative lookout activity  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **Description** & **Abbreviation Example** \\ \hline \multirow{3}{*}{Joint motion magnitude} & Vector magnitude of each joint & \multirow{3}{*}{JMM-5 = JMM head} \\  & obtained by using root mean squares & \\  & of the \(x\)-, \(y\)-, and \(z\)-axis values & \\ \multirow{3}{*}{Joint motion variation} & Standard deviations for each joint for & \multirow{3}{*}{JMV-10 = JMI right elbow} \\  & the \(x\)-, \(y\)-, and \(z\)-axes & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Navigator movement feature properties.  
that took place in that interval; the record of direct observation was also double-checked with a comparison of a video recording to check for observation error.