esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Validations
Four specimens were manufactured for the TL measurement, which are shown in Figure 6. The materials of the specimens are aluminum and steel, respectively, and the geometric parameters of the ABH circular plates are as shown in Table 3. The thickness and diameter of the uniform circular plate are the same as the thickness of the uniform portion and the outer diameter of the ABH circular plate. The internal diameter of the standing wave tube is 36 mm, and the distance between sensors 1 and 2 and the distance between 3 and 4 are both 35 mm. The distances of sensors 2 and 3 from the surface of the specimen are \(X_{2}=X_{3}=148\) mm. The transmission loss of the ABH circular plate calculated by FEM simulation and measured by experiment are compared in Figure 7. It can be seen from Figure 7 that the simulation result agrees well with the measured transmission loss of the ABH circular plate, which confirms the accuracy of the FEM modeling procedure and analysis in Section 3. The transmission loss values of the ABH circular plate and uniform circular plate manufactured by different materials measured by the experiment are shown in Figure 8. As can be seen from Figure 8a, the sound insulation performance of the ABH circular plate is better than that of a uniform circular plate in the range below the first-order resonance frequency and at some frequencies of the damping-controlled region. While in the mass-controlled region, the TL of the ABH circular plate is greater than that of the uniform circular plate at some frequencies, but lower at the other frequencies. The above experimental phenomena and trends are the same as those of the FEM analysis. It can be seen from Figure 8b that, when the ABH circular plate and uniform plate are manufactured with material of steel, the characteristics of their transmission loss are similar to those of transmission loss of ABH circular plate and uniform plate manufactured with aluminum whether in the stiffness-controlled region, the damping-controlled region, or the mass-controlled region.
## 3 Architecture of Proposed System  
Our proposed interactive healthcare system architecture focuses on three phases, as shown in Figure 1: data collection with the unique interaction of smart biosensor devices, analysis of a massive volume of data from individual patients and hospitals for future disease prediction, and discovery of patterns within similar patients.  
The system will compute the biomedical data and the emotional condition of the patient through biosensor devices. The medical history, images, and signal data will be gathered from the hospital server. Those data are then sent for initial evaluation to the HPC server. In our system, the data-collecting phase in HPC is called the Primary layer of the data analysis scheme. In this Primary layer, all types of data (current and previous patient data) will be collected including disease symptoms, genetic data about the patients and emotional data.  
The next segment is the Operating layer. Here all of the collected data from the Primary layer will be the input of this layer and all of the uncategorized and unorganized data will be categorized by some programming model such as the Hadoop MapReduce framework, which will be applicable for creating a structured database with a log file and metadata. Those metadata and databases will make for good user interaction in data management. After the log file and the database has been created, these will be incorporated and sent to another high-performance server called a data warehouse. The log file is used as metadata for providing a fast searching capability and analysis. This layer also stores all the structure data of the patient, and all the data transfer to the next layer via HPC, which is the Application layer.  
In the Application layer, the data warehouse sends the database to the data center for data storage located in the cloud. The cloud provides the facility of fast computing, to diagnose a disease and predict future diseases, and enables other facilities based on the prediction outcome. Finally, all the analyzed results will be sent to both users (patient and physician).
# Localization of a BLE Device Based on Single-Device RSSI and DOA Measurements
## 5 Single Scanner-Based DoA Estimation
### RSS Measurements on Advertising Channels  
With the BLE proximity profile, the beacon device broadcasts an advertising packet periodically at a pre-configured advertising interval. Different implementations of the beacons and services have different advertisement packet structures. However, in general, the advertisement packet contains one-byte transmit power level data, which is the measured radiated transmit power at 1 m distance from the beacon.  
The proximity monitor device scans the advertising channels for advertisement packets. Scan results are timestamped and RSS-captured per broadcast as part of advertisement packet decoding by the BLE protocol stack software layer [25]. Convenient APIs are provided by all major operating systems, including Windows, Linux, macOS, iOS, and Android, to retrieve the scan results. The RSS value in the scan results can be used together with the transmit power level data from the advertisement packet to determine the approximate distance between the reporter and monitor devices [26].  
RSS of radio signals measure of power level that a receiver receives from an RF transmitter device. This measure is typically monitored by wireless devices to ensure signal quality and switch to different access points or base stations when it falls below a threshold level.
## 4 Results and Discussions
### Joint LUT and GS-Based CD Post-Compensation Algorithm at the Receiver  
Figure 8 shows the BER of different schemes versus the number of FFE taps at an ROP of 0 dBm. No matter how much the number of taps increases, it is impossible to recover the signal in the absence of the GS-based CD post-compensation algorithm. FFE alone cannot compensate for the nonlinear impairment due to the nonlinearity of the modulator and the interaction of optical fiber dispersion and PD. The GS-based CD post-compensation algorithm with 80 iterations showed great advantages in compensating for fiber dispersion. The BER was greatly reduced. Based on the GS-based CD post-compensation algorithm, the addition of FFE showed little advantage. However, the GS-based CD post-compensation algorithm has no ability to compensate for the nonlinearity of the modulator. In order to alleviate the influence of the nonlinearity of the modulator, an error table considering 7 symbols was generated under the condition of using the GS-based CD post-compensation and FFE algorithm with 501 taps. At the transmitter, the mapped PAM-4 signal was first processed by LUT. As shown in Figure 8, the BER was below the threshold of 3.8 \(\times\) 10\({}^{-3}\) when using the joint LUT and GS-based CD post-compensation algorithm. Similarly, the use of FFE provided a slight performance advantage. Thus, we ignored the effect of FFE in the subsequent discussion.  
As shown in Figure 9, the effect of the iteration number of the GS-based CD post-compensation algorithm on the BER was further analyzed. The more iterations, the lower the BER. After 80 iterations, the increase in the number of iterations showed a slight improvement in performance. Considering the performance and complexity comprehensively, the number of GS iterations was fixed at 80 in subsequent simulation.  
Figure 10 shows the BER versus ROP with different algorithm schemes. The deployment of the GS-based CD post-compensation algorithm brought a great performance improvement from 1.5 \(\times\) 10\({}^{-1}\) to 3 \(\times\) 10\({}^{-2}\). In the presence of system nonlinearity, the use of LUT is necessary. Furthermore, the more symbols considered in the LUT algorithm, the more obvious the improvement of system performance. At the 1 \(\times\) 10\({}^{-2}\) threshold, a receiver sensitivity of \(-\)4 dBm was achieved by using GS-based CD post-compensation algorithm and 5-symbol LUT. When using the 7-symbol LUT, a 2 dB performance improvement was observed. Meanwhile, a receiver sensitivity of \(-\)2 dBm was achieved by using the GS-based CD post-compensation algorithm and 7-symbol LUT at the 3.8 \(\times\) 10\({}^{-3}\) threshold.  
Figure 8: BER versus the number of FFE taps for different algorithms for 28 GBaud PAM-4 under 40 km SSMF transmission at an ROP of 0 dBm.
###### Abstract  
With the large-scale penetration of distributed generation (DG), the volatility problems of active distribution networks (ADNs) have become more prominent, which can no longer be met by traditional regulation means and need to be regulated by introducing flexible resources. Soft open points (SOP) and energy storage systems (ESS) can regulate the tidal currents on spatial and temporal scales, respectively, to improve the flexibility of ADN. To this end, in-depth consideration of DG admission is given to establish flexibility assessment indicators from the power side of ADN. The conditional deep convolution generative adversarial network (C-DCGAN) is used to generate the output scenario of DG. On this basis, the SOP and ESS two-layer planning models, which take account of the potential for improvement in the flexibility of ADN, are constructed. Among them, the upper layer is the site selection and volume determination layer, which considers the economy of the system with the optimization objective of minimizing the annual integrated cost; the lower layer is the operation optimization layer, which considers the flexibility of the system and takes the highest average daily flexibility level as the optimization objective. The planning model is solved using genetic algorithm-particle swarm optimization (GA-PSO) and second-order cone programming (SOCP). The case analysis verifies the rationality and effectiveness of the planning model.  
flexibility; soft open point; energy storage system; conditional deep convolutions generative adversarial network +
Footnote †: journal: Applied Sciences  
0000-0001-9993-3227][PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Coordinated Planning of Soft Open Points and Energy Storage Systems to Enhance Flexibility of Distribution Networks.
## 2 Microalgae-Based Wastewater Treatment  
### Bioprocessing of Wastewater  
Microalgae, beyond acting as biological indicators, are also used for the treatment of various wastewater streams, as they can metabolize organic compounds, heavy metals, nitrogen, and phosphorous found in wastewater treatment plants to increase their biomass, thus solving the problem of eutrophication phenomena in the final recipients. [PERSON] [20] studied ammonium nitrogen from industrial wastewater utilizing immobilized microalgae, which reached an expulsion proficiency of up to 60% (_C. vulgaris_) and 42% (_S. armatus_). An effective means of wastewater treatment with satisfactory results is high-performance tanks (HRAPs), which are open culture systems, but also immobilized cell methods. The biomass produced by these systems can be utilized for animal feed, and for the production of biofuels and other products, but not as food. Indicatively, the removal of 99% of phosphates from the effluents of the anaerobic digester of a starch industry plant with Spirulina platensis, cultivated in HRAP, and the removal of pigments from textile industry effluents from the microalgae _Chlorella vulgaris_ via the method of immobilization in alginate beds, have been recorded.  
The use of microalgae can also be undertaken with a combination of different species, with satisfactory results, such as the successful withdrawal of nitrogen and phosphorus from domestic wastewater operating in semi-continuous operation via the use of immobilized _Chloella vulgaris_ together with _Scenedesmus obliquus_ and other microorganisms. In this case, oxygen produced by the photosynthetic mechanisms of microalgae reduces the aeration needs of wastewater, and is used by aerobic microorganisms for the degradation of organic pollutants, but also contributes to the removal of volatile substances produced during anaerobic treatment.  
As biological markers, _Pseudo kirdneriella subcapitata_, _Dunailella tertioleta_, _Isochrysis galbana_, and other species of the genus _Chlorela_ have been used for many years. In relation to the assessment of phosphorus and nitrogen presence in freshwater zones, _Ankistrodensus convolutes_, _Chloella vulgaris_, and _Scenedesmus quadricauda_, which are of tropical origin, are widely used [21]. The main approaches to processing and removing nutrients from different types of wastewater in large-scale experiments are the use of open systems and bioreactors. Efficacy varies depending on the type of microalgae used for each effluent and depending on the cultivation system followed, but it is proven that in open systems, nitrogen and phosphorus removal yields are more satisfactory than those of bioreactors [22].
## 3 Materials and Methods
### IBC Identification  
In [20], a study was conducted to measure the signal transmitted characteristics through galvanic coupling. The signal includes information about the transmission channel (channel gain profile). This information is primarily determined by the channel gain/attenuation profile, which develops due to the unique body biological and geometric characteristics [25]. Conducted measurement used a miniVNA Pro vector network analyzer on five subjects and two phantoms. The phantoms represent a five-layer model constructed using materials that accurately mimic the tissues dielectric properties (dielectric permittivity and conductivity), as shown in [26]. Over 150 measurements were conducted at distances between the TX and RX of 10, 15, and 20 cm. The possibility of using the channel gain profile in the galvanic link frequency range to obtain unique characteristics defining the biometric identity of each individual was investigated.  
To identify a subject, the received signal is compared to previously recorded identifiers (unique to that subject) using machine learning algorithms. In [20], the following machine learning algorithms were used: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), J48, Random Forest, and Naive Bayes classifier. These algorithms were used to train and test models capable of identifying individuals using features extracted from IBC channel characteristics obtained from the measurements. After training the model on IBC channel features, the model was tested on a test set of instances, where the model must accurately and reliably identify the subject.  
In [20], a biometric classifier is used to determine the user measurement sample. IBC channel features were selected in order to not make the model more complicated, but to maintain accuracy and performance. The IBC channel gain was used as a feature. An identification accuracy ranging from 92% to 100% was observed when measurements were conducted at a distance of 10 cm. However, when using data for larger distances (15 and 20 cm), accuracy decreased as signal attenuation increased with increasing distance. The study concluded that KNN provides high accuracy and resilience against noise, which can make it particularly suitable for real-time applications. The simplicity of KNN also contributes to reduced computational complexity compared to more involved algorithms like Support Vector Machine or Random Forest, which may require more extensive tuning and computational resources. Overall, while several algorithms were tested, KNN's optimal balance of accuracy and computational efficiency positions it as a highly suitable choice for biometric identity verification.  
Therefore, the KNN algorithm was selected for modeling in our research.
###### Abstract  
This review paper delves into the challenges and opportunities associated with multimedia transmission in vehicular ad hoc networks (VANETs), with a particular focus on audio-visual transmission using IEEE 802.11p. The currently deployed message dictionaries for VANETs only allow for textual exchange. By examining current research in the field of multimedia transmission within transportation systems, we explore the technical issues, challenges, and opportunities involved in transmitting audio-visual-enhanced emergency notifications to transportation users. Additionally, we shed light on the challenges related to video transmission in VANETs and propose focused research areas where Artificial Intelligence can be applied to address the bandwidth constraints imposed by devices. This work makes three significant contributions. Firstly, it presents a detailed comparison between video and image transmission, highlighting their respective strengths and limitations. Secondly, it identifies and discusses the challenges associated with multimedia transmission, emphasizing the need for quality of service and resource availability. Lastly, it examines the opportunities for using intelligence at the edge for transmitting short clips of audio-visual emergency notifications within VANETs to support new services that can coexist with the currently deployed message dictionaries.  
v +
Footnote †: 2023: 12, 4310. [[https://doi.org/10.3390/electronics12204310](https://doi.org/10.3390/electronics12204310)]([https://doi.org/10.3390/electronics12204310](https://doi.org/10.3390/electronics12204310))
# Model-Based Slippage Estimation to Enhance Planetary Rover Localization with Wheel Odometry
## 4 Wheel Odometry Correction for Rover's Localization  
### Simulated Trajectory  
The dynamical model was used to define the trajectory of the rover in a Martian-like environment characterized by drift soil and hazards randomly distributed on the ground. The rover task is to reach the target point \(\left(X_{f}^{*},\ Y_{f}^{*}\right)=\left(100.0,\ 350.0\right)\) m in the local reference frame avoiding obstacles along the path. We assumed to perfectly know the location of each object on the ground. To generate the rover's reference trajectory for the numerical simulations on the localization method with WO correction, we implemented a simplified PD control that is a simplified approach compared to other strategies (e.g., [33; 34; 35]).  
The initial condition of the rover's trajectory consists of heading angle \(\psi_{0}=0^{\lx@math@degree}\), commanded speed \(\overline{V}=40\) m/h and actual speed \(V=\overline{V}\). To reach the goal, we used the PD control on the commanded speed presented in Section 3.2, and a control with variable steering strategies [30]. The integration of the dynamical equations allows us to determine the longitudinal slip and the value of the angle \(\theta_{0}\). The values of the interaction forces are evaluated at each step accordingly to Equations (3), (6) and (9).  
The first trajectory leg \((t\leq 4.2\) h) is a straight line that is followed by tuning the steering angle as follows:  
\[\delta=-K_{d}d+K_{h}(\psi^{*}-\psi) \tag{14}\]  
where \(d\) is the distance between the rover position and the desired path, \(\psi^{*}\) is the desired heading angle and \(K_{d}\) and \(K_{h}\) are the control gains equal to 0.08 and 0.02, respectively. The desired speed is \(V_{d}=0.4\ v_{max}\). Then, this speed is kept constant with a steering angle \(\delta=1^{\lx@math@degree}\) for ~3.3 h. The last segment of the trajectory only changed the steering angle to \(0^{\lx@math@degree}\). Once the distance between the rover's position and the target is less than 20 m, a proportional control on the steering angle and on the desired speed is adopted to reduce \(V_{d}\) in approaching the target, which is reached after 13.2 h.  
Figure 10 shows the path followed by the rover to enable hazard avoidance. The resulting trajectory is significantly longer than the straight line connecting the starting and target positions (shortest path). The rover's speed is shown in Figure 11, together with the commanded and maximum speed. The evolution of the steering angle \(\delta_{1R}\) of wheel 1R is reported in Figure 12.  
Figure 10: Simulated rover’s trajectory vs. shortest path. The initial coordinates of the rover and the final target are marked in red and green, respectively. The obstacles are marked in black.
## 3 Results
### Exposure Time to Hazards According to Musculoskeletal Pain
#### 3.2.3 Hazard Exposure Related to Lower Limb Pain  
Figure 4 and Table 5 display the \(t\)-test results comparing daily hazard exposure among workers who complain of lower limb pain and those who do not. Among physical hazards, the daily exposure times showed significant differences for vibration (\(t=-2.437\), \(p=0.016\)), noise (\(t=-2.699\), \(p=0.008\)), and low temperature (\(t=-3.532\), \(p=0.001\)). Workers who reported lower limb pain experienced daily exposure times that were 1.582 times longer for low temperature, 1.366 times longer for noise, and 1.269 times longer for vibration than those without complaints. No significant differences were found between the groups regarding biochemical hazards.  
Regarding ergonomic hazards, significant differences were found between groups in daily exposure time for awkward posture (\(t=-4.909\), \(p<0.001\)), handling heavy loads (\(t=-3.917\), \(p<0.001\)), and repetitive motion (\(t=-2.552\), \(p=0.011\)). Workers reporting lower limb pain indicated that their daily exposure time was 1.502 (3.732/2.485) times  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Factor**} & \multirow{2}{*}{**Hazard**} & \multicolumn{2}{c}{**No Pain**} & \multicolumn{2}{c}{**Upper Limb Pain**} & \multirow{2}{*}{\(p\)**-Value**} \\ \cline{3-3} \cline{5-6}  & & **Mean** & & & **SD** & \\ \hline \multirow{3}{*}{Physical hazards} & Vibration & 2.241 & 2.184 & 2.672 & 2.450 & 0.030 \({}^{\star}\) \\  & Noise & 1.588 & 1.713 & 2.017 & 2.145 & 0.011 \({}^{\star}\) \\  & High temperature & 1.313 & 1.363 & 1.581 & 1.588 & 0.036 \({}^{\star}\) \\  & Low temperature & 1.264 & 1.438 & 1.838 & 2.901 & \(<\)0.001 \({}^{\star}\) \\ \hline \multirow{3}{*}{Biochemical hazards} & Vapor & 0.853 & 0.359 & 0.913 & 0.391 & 0.060 \\  & Chemical contact & 0.821 & 0.200 & 0.923 & 0.479 & 0.002 \({}^{\star}\) \\  & Tobacco smoke & 0.821 & 0.204 & 0.879 & 0.231 & 0.002 \({}^{\star}\) \\ \hline \multirow{3}{*}{Ergonomic hazards} & Awkward posture & 2.344 & 2.082 & 3.293 & 2.468 & \(<\)0.001 \({}^{\star}\) \\  & Handling heavy loads & 2.188 & 1.936 & 2.921 & 2.231 & \(<\)0.001 \({}^{\star}\) \\ \cline{1-1}  & Standing posture & 4.232 & 2.342 & 4.619 & 2.464 & 0.060 \\ \cline{1-1}  & Repetitive motion & 4.117 & 2.495 & 4.367 & 2.463 & 0.007 \({}^{\star}\) \\ \hline \end{tabular} Note. * significant at 0.05, ** hours per day, SD = standard deviation.  
\end{table}
Table 4: \(T\)-test results comparing daily hazard exposure between groups regarding upper limb pain.  
Figure 3: Mean exposure times (hours per day) for upper limb pain complaints. * Differences between groups exist at significance level of 0.05 (red letters); error bars = standard deviation.  
longer for awkward posture, 1.390 times longer for handling heavy loads, and 1.151 times longer for repetitive motion than those who did not report pain.