esa-sceva/satcom-chunk-collection huggingface dataset:
# Flexible Wearable Heart Rate Monitoring System and Low-Power Design: A Review
## 6 Design of the Flexible Wearable Heart Rate Signals Transmission and Output Module
### Hardware-Algorithm Co-Design for Flexible Wearable Heart Rate Signal Transmission and Output Module
From selectinglow-power components and designing optimized circuit architectures on the hardware side, to configuring multiple operational modes and adopting low-power transmission technologies in the algorithm, these design strategies have effectively reduced system energy consumption and extended device operational duration. The Table 8 lists the innovativeness, practicality, power consumption optimization methods, power consumption indicators of each technology, as well as the comparison of power consumption with previous studies, with specific contents shown in Table 8. Meanwhile, in terms of the hardware design of the heart rate signal transmission and output module, since BLE modules and Wifi modules are more commonly used, references [122; 126] have been selected for list; in the algorithm aspect, given the similarity between the DCT compression algorithm and the DCT-IV compression algorithm, only reference [71] has been selected for list. It should be noted that references [62; 71; 122; 123; 126; 127; 128; 139] in this section are all derived from flexible wearable systems. In conclusion, in the field of heart rate signal transmission and output for flexible wearable systems, low-power design has formed a complete technical system through hardware selection, algorithm optimization, and hardware-algorithm co-design. At the hardware level, compared with wired transmission, wireless transmission modules such as BLE, Wi-Fi, and Zigbee modules have respectively constructed a multi-layered data transmission architecture with their low-power compatibility, long-distance transmission optimization, and self-organizing network capabilities. In terms of algorithms, from [PERSON] et al. 's DCT compression to [PERSON] et al. 's variable-length DCT-IV optimization, system power consumption is reduced through data redundancy elimination and dynamic range suppression. The DF-AM algorithm, relying on the time-varying characteristics of the channel to dynamically adjust modulation parameters, can reduce 22% of invalid transmission energy consumption in deep fading scenarios. Hardware-algorithm co-design breaks through the limitations of single technologies: [PERSON] et al. 's IoT platform reduces 30% of data volume in the preprocessing stage and lowers transmission load through the collaboration of microprocessors and
###### Abstract  
Vessel Traffic Services (VTS) systems are complex systems facilitating decision-making processes and integrating technical infrastructure, aiming to ensure the safety of ship traffic and marine environment protection in indicated water areas. Such services are offered in Poland in selected regions. These systems operate based on guidelines established by the International Maritime Organization (IMO) and European Parliament; therefore, they should be constantly developed and adjusted to current regulations. The aim of this article is to review and assess the adjustment of VTS systems operating in Poland to current selected regulations introduced by the IMO and European Parliament. A comparative analysis and evaluation of three VTS systems operated in Poland was carried out. In addition, the impact of VTS systems on the development of the trans-European transport network was examined. It was stated that the investigated VTS systems' current adjustment to analyzed regulations is different depending on the systems' configuration and possessed infrastructure, parameters of fairways, traffic regulations and other criteria. Based on the achieved research results, recommendations to improve the VTS systems in Poland were proposed. The research outcomes may be interesting for the managers of maritime administrations, ports' authorities, and other decision-makers responsible for safe navigation and traffic management.  
vessel traffic services; decision-making processes; maritime transport; traffic management Article
# Efficient Control of a Non-Linear System Using a Modified Sliding Mode Control
## 2 Control Design
### PD Sliding Mode Control (PDSMC)
#### 2.3.4 Proposed Scheme Characteristics  
Attenuation of Perturbation Signal During the Sliding Phase.  
The transfer function between the sliding surface and perturbation for both control schemes for our proposed one (PDSMCSPO) Equation (49) and conventional (SMCSPO) one Equation (50) are shown below.  
\[\frac{s}{\psi}=\frac{k_{d}\cdot p^{3}+p^{2}\big{(}k_{p}+4\cdot k_{d}\cdot \lambda_{d}\big{)}+p\big{(}k_{p}\cdot\lambda_{d}+9\cdot\lambda_{d}^{2}\cdot k _{d}\big{)}}{(p+\lambda_{d})^{4}} \tag{49}\]  
\[\frac{s}{\psi}=\frac{p^{3}+5\cdot\lambda_{d}\cdot p^{2}+10\cdot\lambda_{d}^{2 }\cdot p}{(p+\lambda_{d})^{4}} \tag{50}\]  
In Figure 4, the red line is the Bode plot of SMCSPO and the blue line (dotted)is for PDSMCSPO. It can be seen that our proposed logic attenuated the incoming perturbation signal better as compared to SMCSPO during the sliding phase. The value of lambda is 30 for both schemes.  
Small Output ErrorThis robust control PDSMCSPO had small output error compared with conventional SMCSPO because this scheme attenuated the perturbation signal more than conventional SMCSPO. Figure 3 is the Bode plot between the error and perturbation. It can be seen that PDSMCSPO attenuated  
Figure 4: Bode plot of sliding surface over perturbation.  
Figure 3: Bode plot of estimated perturbation over original perturbation.  
perturbation more than SMCSPO. The transfer function between error and perturbation for both schemes, PDSMCSPO Equation (51), and SMCSPO Equation (52) are shown below.  
\[\frac{e}{\psi}=\frac{k_{d}\cdot p^{3}+p^{2}\big{(}k_{p}+4\cdot k_{d}\cdot\lambda _{d}\big{)}+p\big{(}k_{p}\cdot\lambda_{d}+9\cdot\lambda_{d}^{2}\cdot k_{d} \big{)}}{0.5\cdot(p+\lambda_{d})^{5}} \tag{51}\]  
\[\frac{e}{\psi}=\frac{p^{3}+5\cdot\lambda_{d}\cdot p^{2}+10\cdot\lambda_{d}^{2} \cdot p}{\left(p+\lambda_{d}\right)^{5}} \tag{52}\]  
In Figure 5, the blue line (dotted), PDSMCSPO, has more attenuation capability than the red line, SMCSPO. The value of lambda = 30.  
Faster Convergence.Our proposed scheme had faster convergence to the desired state as compared with conventional SMCSPO. The reaching time can be calculated as:  
\[t_{r}\leq\frac{s_{o}}{\eta} \tag{53}\]  
\(\eta\) is a constant which can be defined as:  
\[K=\eta+\psi \tag{54}\]  
Step input is given to the system.  
_i. SMCSPO_  
From Equation (17).  
\[\hat{s}=\hat{e}+c\cdot\hat{e}\]  
In case of step input \(r=1,y=0,\hat{y}=0.\hat{r}=0\).  
\[\hat{s}_{0}=\dot{r}-\dot{\hat{y}}+c(r-\hat{y}) \tag{55}\]  
\(\dot{r}=1\), \(\dot{\hat{y}}=0\), \(\dot{y}=0\) at time t = 0.  
So Equation (55) becomes:  
\[\hat{s}_{0}=c \tag{56}\]  
\[t_{r}\leq\frac{c}{\eta} \tag{57}\]  
Figure 5: Bode plot of error over perturbation.  
From Equation (36) \[\hat{s}=k_{d}\cdot\hat{e}+k_{p}\cdot\hat{e}\] In case of step \(\dot{r}=1\), \(\hat{y}=0\), \(\hat{y}=0\) at time t = 0. \[\hat{s}=k_{d}(\dot{r}-\hat{y})+k_{p}(r-\hat{y})\] (58) Equation (58) above becomes: \[\hat{s}=k_{p}\] (59) And it is known that \(k_{p}<c\) so that the reaching time is smaller in PDSMCSPO.
## 2 System Model
### System Setup
Accordingly, the signal-to-interference-plus-noise ratio (SINR) \(\gamma^{i}\) of the \(i\)-th user can be expressed as follows:  
\[\gamma_{t,b,f}^{i}=\frac{P_{t,b,f}^{i}\cdot G_{b}^{i}\cdot PL_{b,f}^{i}}{\sum \limits_{b^{\prime}\
eq b}P_{t,b^{\prime},f}^{i}\cdot G_{b^{\prime}}^{i}\cdot PL _{b^{\prime},f}^{i}+N_{i}} \tag{3}\]  
where \(N_{i}\) denotes the thermal noise power associated with the sub-band allocated to the \(i\)-th user, which is determined by the sub-band bandwidth and receiver characteristics. The achievable data rate \(R_{t,b,f}^{i}\) for the \(i\)-th user can be calculated based on the Shannon capacity formula:  
\[R_{t,b,f}^{i}=B_{t,b,f}^{i}\text{log}_{2}(1+\gamma^{i}) \tag{4}\]  
where \(B_{t,b,f}^{i}\) is the bandwidth of the sub-band assigned to the \(i\)-th user via the \(b\)-th beam.
## 4 Simulation Verification and Result Analysis
### Accuracy of Saddle Point Approximation  
In Figures 1 and 2, doubly correlated Rayleigh fading and Rice fading channel models were considered, respectively. We compared the average mutual information by the Monte Carlo method and saddle point approximation method by (12). Different input types (BPSK, QPSK, QAM, 8 PSK, and 16 QAM) were assigned to transmitting antennas to ensure generality. Obviously, with the increase in SNR, mutual information presented an upward trend. When the SNR was greater than 15 dB, mutual information tended to be stable. In these cases, (12) offered a very good approximation to the average mutual information for known channel state information.  
Figure 3 compares normalized MI calculated by Monte Carlo and saddle point approximation according to upper and lower bounds of \(\alpha_{m,k}\) by (14) over doubly correlated Rayleigh fading channels. At low SNR, the normalized average mutual information of different modulation signals had little difference. With the increase in SNR, the normalized average mutual information of different input types tended to 1, and the growth rate of BPSK was the fastest. All simulation values were very close to the approximation values.  
Figure 1: Comparison on MI calculated by Monte Carlo and saddle point approximation under different input types and correction parameters (\(\rho_{\rm T}=\rho_{R}=\rho\)) over doubly correlated Rayleigh fading channel model.  
Figure 2: Comparison on MI calculated by Monte Carlo and saddle point approximation under different input types and correction parameters (\(\rho_{\rm T}=\rho_{R}=\rho\)) over doubly correlated Rice fading channel model.  
Comparison of MI calculated by Monte Carlo and the lower bound of saddle point approximation methods by (15) over doubly correlated Rayleigh fading channel are shown in Figure 4. With the increase in SNR, the mutual information increased, and the accuracy of saddle point approximation became higher. It was also clear that the lower bound of saddle point approximation achieved considerable accuracy for different doubly correlated MIMO fading channel scenarios.
## 4 Results and Discussion
### Discussion  
The results show two use cases using Carbonic from two different perspectives: in the first one, an existing dataset is visually explored, while in the second one, a graph is created from scratch using the visualization and interaction capabilities provided by Carbonic.  
The first example shows a drastic reduction in complexity by visualizing the aggregated data at a higher level of abstraction (Figure 13). Figure 13b presents a decluttered image where the connectivity and proportion of airports on each continent are easily visualized. The visual separation between the internal routes to the continent and the external routes to the rest of the continents facilitates the perception of connectivity and the identification of the most strongly connected continents (_Europe_ and _Asia_) and those with fewer air routes between them (_Africa_ and _South America_). The selective uncollapsing of sectors allows cross-scale exploration and the identification of the countries with which a continent is mainly connected or the regions most connected to a given country. In this way, it is easily detected that _Brazil_ is the only country that directly interconnects _Africa_. This information would have been very difficult to discern in a classical representation of air routes on a geographical map.  
Figure 22: (**a**) Relevance filtering. Only the most relevant concepts are kept, while the least relevant ones are filtered out and depicted in grey color. (**b**) Outdegree resizing. Concepts re-sized according to their outdegree. _Assembler Basics_ was filtered out according to its relevance but it turns out to have a high number of post-dependencies.  
The second use case shows the usefulness of Carbonic as a support for planning the contents of a course. Carbonic's editing capabilities allow creating the hierarchical structure of the concepts and establishing the dependencies between them, simultaneously visualizing the hierarchical structure of the contents and the relationships between them. The explicit visualization of this information can be of great help when rescheduling, deleting content, or modifying the temporal sequencing in which contents are addressed. In the example presented, the relevance attribute was associated with each concept taught, thus qualifying the contents according to the teacher's criteria. The combination of filtering operations to discard less relevant concepts with the ability to scale the sectors according to a numerical attribute (in this case the degree of output connectivity) highlights the inconsistencies between the perceived relevance of certain concepts and their importance due to further dependencies with subsequent concepts.
# Fiducial Lower Confidence Limit of Reliability for a Power Distribution System
## 1 Introduction
Based on records data, the authors of [27] calculated the interval estimation of quantiles and reliability in a two-parameter exponential distribution. In practice, some electronic components, such as the key components of a PDS, have a long lifetime and high reliability [28; 29]. The sample size of these lifetime data is usually very small. To overcome the shortcoming of a large sample, some references adopted the Bayesian method to estimate the LCL of reliability. For example, the authors of [30] and [31] used the Bayesian method to study the reliability for binomial systems, while those in [32] used Bayesian networks to present developments for traditional series and parallel systems. The authors in [33] and [34] applied the Bayesian approach to analyze the reliability of the degradation data. Although the Bayesian approach is suitable for a small sample of lifetime data, the selection of the prior distribution is difficult. To avoid the difficulty of choosing a proper prior distribution, the fiducial method is adopted to estimate the LCL of reliability for a PDS in this paper. The fiducial method considers the parameter as a random variable whose distribution is decided by the observation instead of the prior distribution. The authors of [35] developed the fiducial method by defining a functional model, and those in [36] proved that the LCL derived by the fiducial method is the same as the LCL obtained by the traditional method under some conditions. Recently, some optimal inferences [37] and generalizations of the fiducial method [38] have been developed based on [38; 39; 40], giving the generalized fiducial inference for generalized exponential and Lomax distribution. It can be seen that the generalized fiducial method is an effective method to estimate the reliability of a long-life product. Based on the high reliability of the PDS, this paper estimates the LCLs of the reliability of the PDS by using the generalized fiducial method. For a typical satellite intelligent PDS, the lifetime distribution of the key components in the satellite intelligent PDS are assumed to follow an exponential distribution and Weibull distribution according to the experience of the engineers. In this situation, two algorithm procedures are presented to estimate the LCL of the reliability for an exponential-typecomponent and a Weibull-type component. Then, the LCLs of reliability for the satellite intelligent PDS are established by the relationship between the key components and the system. Compared with the Bayesian method, the generalized fiducial method of this paper is more flexible to avoid the selection of the prior distribution.
# A New Methodology for Estimating Surface Albedo in Heterogeneous Areas from Satellite Imagery
## 3 Materials
### Landsat 8  
Landsat is a space mission for Earth monitoring, led by NASA (Washington, DC, USA) and the United States Geological Survey (USGS) (Reston, Virginia). The last satellite launched within this program and in operation is Landsat 9 [45], which has the same technical characteristics as the previous Landsat 8, and together they present an orbit with a time lag of 8 days. Thus, the methodologies are tested and designed in this paper using Landsat 8 [37], but the work could be replicated for Landsat 9 imagery. Landsat 8 is equipped with two instruments: Operational Land Imager (OLI) (Boulder, Colorado) and Thermal InfraRed Sensor (TIRS) (Washington, DC, USA).  
The computation of surface albedo requires the seven corrected reflectance bands of the OLI sensor (Table 1). These are the reflectance bands in the short-wave infrared spectrum. The other bands are discarded from the computation due to their characteristics of high absorption and low reflection of the solar radiation [32]. Depending on the surface albedo calculation methodology, coastal aerosol band 1 can be substituted for an equation of atmospheric transmittance where these aerosols are considered. Reflectance values are acquired from TOA radiance values (Landsat Collection 2 Level 1), which can be downloaded via the EarthExplorer service. Finally, these TOA radiances are transformed into BOAreflectances, depending on the methodologies that require it. BOA reflectances (Landsat Collection 2 Level 2) may also currently be downloaded via the EarthExplorer service.  
The selection of images from the Landsat 8 satellite for the computation of surface albedo has been motivated by their spatial resolution (30 m), which improves the resolution of existing surface albedo values from satellite platforms, such as the ABOVE product (500 m). Landsat 8 observations over the North of the USA were collected, in clear sky conditions (less than 10% cloud cover), during 2013, 2014, 2015, and 2016. The selection of these dates is motivated by the disposition of the ABOVE product described in the following section for the sake of comparison.
## 5 Non-Uniform In-Depth Residual Stress Field of the Orthotropic Bridge Deck
### Finite Element Modeling Results
#### 5.2.1 Top Side Deck Plate  
A total of 11 strain gauge rosettes are investigated on the top side of the deck plate. The measured residual stresses for each of these strain gauge rosettes at 20 incremental depths of 0.05 mm are applied to the 3D model on the boundaries not containing the hole and the hole-drilling procedure is simulated. The incremental strains obtained with the 3D model are used to calculate the residual stresses. For each strain gauge rosette, a representative residual stress value at the final hole depth of 1 mm is used to make a residual stress distribution in function of distance to the weld toe.  
Figure 12: General residual stress distribution of the orthotropic steel deck from experimental measurements (\(\mathrm{f_{y}}=\mathrm{yield\ strength}\)) for the longitudinal direction (**a**) and the transverse direction (**b**).  
The experimentally-determined residual stress distribution is compared with the measured residual stress distribution from the finite element model for both the transverse and longitudinal directions. This comparison is shown in Figure 13. The horizontal axis gives the distance to the weld toe, the origin corresponds to the weld toe while positive distances are at the right-hand side of the weld toe and negative distances at the left-hand side.  
The residual stress values obtained with elastic-plastic analysis result in higher tensile and lower compressive residual stresses compared to the expected ones from the experimental measurements according the ASTM standard. However, the general trend of the stresses in relation to the distance to the weld toe remains the same.
###### Abstract  
The present study evaluates the antimicrobial potential of non-equilibrium plasma against microorganisms isolated from diatomaceous earth, which is a waste product from the beer filtration process. For evaluation, waste diatomaceous earth from an industrial brewery was treated with non-equilibrium plasma using a glidearc reactor. The temperature of the treated samples was monitored. The effect of plasma on the morphology of the treated material was investigated microscopically. Plasma can affect the treated material in various ways and change its physicochemical properties. Consequently, the scope of potential plasma applications is constantly expanding from material technologies to decontamination applications in the food industry, environmental protection and medicine and stimulates activities in, for example, agriculture and medicine. At the same time, microbiological analyzes were carried out to determine the presence of selected groups of microorganisms on diatomaceous earth before and after plasma treatment. The study revealed that the porous structure of diatomaceous earth is not an obstacle to the effective removal of microorganisms from it using plasma. A significant decrease in the amount of both bacteria and yeast relative to the control (non-plasma samples) was observed with the increasing contact time of the diatomaceous earth with the plasma. The numbers of bacteria and yeast decreased by a maximum of 2.2 log\({}_{10}\) CFU-g\({}^{-1}\) and 1.72 log\({}_{10}\) CFU-g\({}^{-1}\) (30 min of plasma contact), respectively. The obtained results are extremely promising and encourage further, in-depth research to optimize the plasma process and its effect on microorganisms.  
microorganisms; non-equilibrium plasma; diatomaceous earth; glidearc reactor; electrotechnology for waste processing