esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
Three examples of 10 MW power plants were examined in the study: a CSP power plant, a biomass power plant, and a hybrid CSP-biogas power plant. The LCOE for the three systems was 0.239, 0.116, and 0.154 EUR/kWh, respectively. The hybrid system has a higher investment cost than CSP or biomass power plants. Additionally, it demonstrates that the hybrid system produces nearly 2.7 times as much energy as the CSP plant, meaning that the hybrid power plant has a lower LCOE (64%) than the CSP plant. [PERSON] and [PERSON] [14] examined a hybrid system in Saudi Arabia that used renewable energy sources--biomass and solar thermal--while producing electricity,heating, and fresh water. The findings demonstrated that conventional solar thermal storage can be removed, significantly boosting the solar thermal energy resource's economic competitiveness. Cogeneration allows for a maximum energy efficiency of 52%, while the maximum exergy efficiency is 47%. [PERSON] et al. [15] evaluated a biomass-based tri-generation system with solar assistance to satisfy domestic energy needs. In addition to providing 1 MWel of power, the results demonstrated that the intended system could supply 1241 kW of space heating, 101.5 kW of domestic hot water, and 55.35 kW of chilled water for space cooling. [PERSON] and [PERSON] [16] researched a 1 kW power-producing system that combines parabolic solar collectors and biomass furnaces. The scientists suggest that such a system might be used in rural agricultural areas with a plentiful supply of rice husks and enough open space for solar collectors. According to a study by [PERSON] et al. [17], the most effective system combinations featured CSP and at least one PV system. [PERSON] and [PERSON] [18] presented the techno-economic feasibility of a solar biomass system in Ludhiana district of Punjab, India. The proposed system comprises a 1000 kW biomass power plant and a 100 kW PV plant. Finally, a global review [19] found that hybrid renewable energy systems, including solar thermal, have the potential to achieve competitive LCOE values ranging from USD 80 to USD 120/MWh, depending on regional resource quality, system size, and financing conditions. In conclusion, the literature indicates a trend toward reducing the LCOE for hybrid solar thermal power plants through innovative designs, technology integration, and strategic policy support. Continued research is essential to refine cost estimates and develop best practices for implementing these systems in diverse geographic and economic contexts.
## 4 Discussion  
The review provides some insights into the extent of receptivity of remote sensing technologies and specific technological applications. Following is a discussion of the implications and directions of future research.  
RQ1: What has been done regarding the acceptability of remote monitoring of assisted or smart homes in the United Kingdom?
# EEG Channel Selection for Stroke Patient Rehabilitation Using BAT Optimizer
###### Abstract  
Stroke is a major cause of mortality worldwide, disrupts cerebral blood flow, leading to severe brain damage. Hemiplegia, a common consequence, results in motor task loss on one side of the body. Many stroke survivors face long-term motor impairments and require great rehabilitation. Electroencephalograms (EEGs) provide a non-invasive method to monitor brain activity and have been used in brain-computer interfaces (BCIs) to help in rehabilitation. Motor imagery (MI) tasks, detected through EEG, are pivotal for developing BCIs that assist patients in regaining motor purpose. However, interpreting EEG signals for MI tasks remains challenging due to their complexity and low signal-to-noise ratio. The main aim of this study is to focus on optimizing channel selection in EEG-based BCIs specifically for stroke rehabilitation. Determining the most informative EEG channels is crucial for capturing the neural signals related to motor impairments in stroke patients. In this paper, a binary bat algorithm (BA)-based optimization method is proposed to select the most relevant channels tailored to the unique neurophysiological changes in stroke patients. This approach is able to enhance the BCI performance by improving classification accuracy and reducing data dimensionality. We use time-entropy-frequency (TEF) attributes, processed through automated independent component analysis with wavelet transform (AICA-WT) denoising, to enhance signal clarity. The selected channels and features are proved through a k-nearest neighbor (KNN) classifier using public BCI datasets, demonstrating improved classification of MI tasks and the potential for better rehabilitation outcomes.  
EEG; feature extraction; channel selection; stroke patients; BAT algorithm +
Footnote †: journal: 8 August 2024
## 5 Experimental Results
### Results without Pathologies
#### 5.2.5 Results for Decision Tree with Evolutionary Feature Selection  
The following results were obtained with the DT using the evolutionary GA.  
**GA with _Stud_ selection for Decision Tree (DT-S):** Using the _Stud_ selection method, a total of 14 variables were chosen, with an average accuracy of 0.6934 and 0.7322 for the best case, as can be seen in Table 3. In this case, the age variable was taken but not the sex variable. The variables chosen include education, population, hours of sleep, alcoholic drinks, soft drinks, legumes rations, vegetable rations, and the stress variable as a new incorporation.  
Figure 10 shows the impact of the different variables for the Decision Tree model with Stud for the set without pathologies. First, age appears again, the next variable is education and it is observed that, for higher values and higher level of studies, the probability of being overweight/obese is lower. In the case of soft drinks, higher consumption implies a higher probability of being overweight or obese. Another variable to note is vegetable, where it is observed that most of the red dots are on the left side of the graph, so it can be considered that it is less likely to be overweight/obese. It would be interesting to study in the future the causes of the behaviors of the variables stress and ex-smoker.  
**GA** with tournament selection for Decision Tree (DT-T): In this case, the number of variables used was 15, obtaining an average accuracy of 0.6862 and reaching 0.7525 in the best case, as shown in Table 3. Among the variables chosen are portions of legumes, fermented drinks, the time without smoking of an ex-smoker, and the IPAQ, the variable that reflects physical exercise. Other variables found are the most common, such as sex, age, soft drinks, and education, among others.  
Figure 11 shows the heat map obtained by representing the frequency of variables selected from the dataset without pathologies using the FS models with genetic algorithms. There is a closer correlation between the variables selected with the GB and DT models, compared to the case of the dataset with pathologies. Again the most commonly selected  
Figure 10: Impact of Variables with SHAP, Decision Tree with _Stud_ Selection, without Pathologies.  
variables are age, sex, and education followed by alcoholic beverages. As a new addition among the most selected variables, we find the variable soda.
## 2 Materials and Methods
### Data Sources and Processing  
In this study, the land use/cover data, carbon emission data, nighttime light (NLT) data, population grid data, and gross primary productivity (GPP) data were used to analyze the spatio-temporal dynamics of land use carbon emissions and their driving factors in the study area (Table 1). The specific data information is as follows:  
(1) Land use/cover data: the annual land use/cover data from 2000 to 2019 at a spatial resolution of 300 m were collected from the European Space Agency ([[http://maps.elie.ucl.ac.be/CCI/viewer/](http://maps.elie.ucl.ac.be/CCI/viewer/)]([http://maps.elie.ucl.ac.be/CCI/viewer/](http://maps.elie.ucl.ac.be/CCI/viewer/)), accessed on 1 June 2022) and were divided into seven types, including cropland, grassland, forestland, wetland, water, unused land, and construction land (Figures 1c and A1).  
(2) Carbon emission data: the monthly carbon emission datasets from 2000 to 2019 were published by the Open-source Data Inventory for Anthropogenic CO\({}_{2}\) (ODIAC) ([[http://db.cger.nies.go.jp/dataset/ODIAC/data_policy.html](http://db.cger.nies.go.jp/dataset/ODIAC/data_policy.html)]([http://db.cger.nies.go.jp/dataset/ODIAC/data_policy.html](http://db.cger.nies.go.jp/dataset/ODIAC/data_policy.html)), accessed on 1 May 2022). These datasets had a spatial resolution of 1 km and were estimated by combining the space-based nighttime light data and the individual power plant emission, a method that has been widely used for a variety of research applications (e.g., CO\({}_{2}\) flux inversion, urban emission estimation, and observing system design experiments) [9].  
(3) Nighttime Light (NLT) data: the Defense Meteoro-logical Satellite Program Operational Line scan System (DMSP-OLS) and Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS/DNB) Nighttime Light (NLT) data were obtained from the NOAA website's NGDC data center ([[https://www.ngdc.noaa.gov/eog/download.html](https://www.ngdc.noaa.gov/eog/download.html)]([https://www.ngdc.noaa.gov/eog/download.html](https://www.ngdc.noaa.gov/eog/download.html)), accessed on 4 August 2022) [31].  
(4) Population grid data: the population grid datasets at a spatial resolution of 1 km for the years 2000-2019 were obtained from World Pop ([[https://www.worldpop.org/project/list](https://www.worldpop.org/project/list)]([https://www.worldpop.org/project/list](https://www.worldpop.org/project/list)), accessed on 13 August 2022).  
(5) Gross primary productivity (GPP) data: the annual GPP datasets at a spatial resolution of 1 km were derived from the National Tibetan Plateau/Third Pole Environment Data Center ([[https://data.tpdc.ac.cn/zh-hans/data/582663F5-3](https://data.tpdc.ac.cn/zh-hans/data/582663F5-3) be7-4f26-bc45-b56a3c4 fc3b7]([https://data.tpdc.ac.cn/zh-hans/data/582663F5-3](https://data.tpdc.ac.cn/zh-hans/data/582663F5-3) be7-4f26-bc45-b56a3c4 fc3b7), accessed on 13 September 2022).  
Figure 2: The overall technique diagram map.  
In order to keep the resolution of all data consistent, we resampled these data to ensure that the data resolution was 1 km.
# A Simple Neural-Network-Based Decoder for Short Binary Linear Block Codes
## 2 Preliminaries of Conventional Soft Decision Decoder
### Chase-II Decoding Algorithm  
The Chase-II algorithm [11] generates the test error patterns based on bit reliability \(|y_{i}|\) and the error-correcting capability \(t\), and then utilizes the HDD to obtain the candidate codewords. Finally, the best codeword is selected on a maximum likelihood basis. Figure 2 illustrates the procedure of the Chase-II algorithm. The detailed steps are described as follows:  
* Step 1: Obtain the received signal sequence \(y\) and demodulate it as the received word \(r\).
* Step 2: Find the \(t\) least reliable bits and generate \(2^{t}\) test error patterns.
* Step 3: Decode \(2^{t}\) words and check their syndromes. The decoded word with zero syndrome will be viewed as the candidate codeword \(dr\).  
Figure 2: Chase-II Algorithm.  
* Step 4: Evaluate \(D_{1}(dr)=\{i|r_{i}=dr_{i},0\leq i\leq n-1\}\) and compute the maximum likelihood metric \(\sum_{i\in D_{1}(dr)}|y_{i}|\) of the codeword \(dr\).
* Step 5: Output the best codeword as \(dc\) with the minimum value of \(\sum_{i\in D_{1}(dr)}|y_{i}|\).
# Exploring the Utility of 3-D-printed Laboratory Equipment
## 3 Results
### Application in a Portable Multispectral Imaging System  
A custom-designed low-profile linear stage and mini-LJ were installed on the 3-D-printed MSI device shown in Figures 8 and A6 This instrument utilizes a smartphone to perform as an MSI detector while using a linear motor to sweep a color filter to generate a series of spectral reflectance images. Installing a lower-profile linear stage and mini-LJ was critical to ensure horizontal sample positioning and  
Figure 6: **(A)** Schematic diagram of a jack with original height, assuming length of the scissor is \(L\) and distance between the two short rods is \(a\). **(B)** Schematic diagram of a jack after one revolution from the original height with a decrease of \(1/18\) in the distance between the two short rods on both sides. **(C)** Line chart of laboratory jack height versus number of revolution comparing real and theoretical height.  
Figure 7: Mass versus percentage deflection for all three types of laboratory jacks. **(A)** Percent deflection for pure plastic and metal-plastic laboratory jacks when individual height was set at maximum and at 50% of the maximum. Plastic laboratory jacks showed close to 30% changes in height when 0.9 kg was placed on top. **(B)** Similar relationship for the custom-designed mini laboratory jack for the MSI. Maximum deflection was around 1% of the total height even with a load of 0.9 kg.  
to account for large size differences in the objects under investigation. Since smartphone systems were configured as macro-MSI (imaging large surface area) and micro-MSI (zoom-in on a small area), a simple attachment such as the mini-LJ ensured image focus, particularly for the micro-MSI, which served as a low-power microscope. As a proof of concept, 7-color band reflection images were acquired and average grayscale intensities plotted for fake and real fruit (Figure 9). The spectral intensity plot clearly distinguishes two classes, particularly in the red color region (600-650 nm), where real fruit provides less intensity than artificially colored fruit.  
Figure 8: Utilizing the proposed 3-D printed XY stages and laboratory scissor jack with a portable multispectral imaging device. The system consists of a multispectral imaging platform, a low-profile XY stage for lateral sample alignment, and a mini laboratory scissor jack for vertical focusing. (**A**) System with combination of mini laboratory jack and low-profile XY stage. (**B**) System with low-profile XY stage only.  
Figure 9: Application of spectral reflectance imaging using 3-D printed stages and laboratory jack. (**A**) Thumbnail images of fake and real strawberries. (**B**) Spatially averaged spectral reflectance of the strawberry images. (**C**) Thumbnail images of fake and real grapes. (**D**) Spatially averaged spectral reflectance of the grape images. Note that a mini laboratory jack was utilized for the grape images. Shaded color on (**B**) and (**D**) represents standard deviation of three repetitions.
## 5 Results and Discussion
### Routing Overhead  
Figure 7 shows a performance comparison between the proposed RALAR, KALAR, and LAR protocol in terms of routing overhead. The results show that the routing overhead of the proposed RALAR algorithm is much lower compared to the KALAR and LAR protocol. The achieved routing overhead value for the RALAR algorithm has equal to 74.72, whereas the KALAR and LAR achieved routing overhead values equal to 88.45 and 88.43, respectively. The better performance of the proposed RALAR algorithm is due to the adoption of an estimation-based heuristic approach that was developed to reject bad GPS location data and accept good ones based on a moving rectangular zone according to the node's mobility model. It is also noticeable that the performance of the LAR protocol is slightly lower than the KALAR approach in routing overhead.  
Figure 5: PDR vs. Time.
## 3 New Generation Sensor Web Enablement
### 3.3. Evolvement of SWE Interface Model  
The SWE interface model comprises standards that specify the interfaces of the different Sensor web services. Four service interfaces were defined for the first generation of SWE: The Sensor Observation Service (SOS) [64] offers pull-based access to sensor measurements as well as metadata.  
The Sensor Alert Service (SAS) [65] allows subscribing to alerts in case of a sensor measurement event that fulfills certain criteria. The Sensor Planning Service (SPS) [66] can be used for tasking sensors and setting their parameters. The Web Notification Service (WNS) [67] is, unlike the other three services, not directly sensor related. It is a supportive service which provides asynchronous notification mechanisms between SWE services and clients or other SWE services (e.g., delivery of notifications) including protocol transducing capabilities.  
In the new generation of SWE, the SOS and SPS have evolved to version 2.0, as shown in Figure 4. The conducted changes are in detail described in Sections 3.3.2 and 3.3.3. A common basis for the service development has been introduced, the SWE Service Model specification (Section 3.3.1), which serves as a foundation for SOS 2.0 as well as SPS 2.0. While the SAS has evolved to the more powerful Sensor Event Service (SES), the WNS has not yet been further developed since an approved standard for eventing needs to be in place first (Section 3.3.4). Furthermore, new service interfaces to support the sensor specific aspects of discovery have come up and are being discussed at OGC (Section 3.3.5). In the following subsections, the interface model of the new generation SWE is analyzed and its conceptual changes are highlighted.
## 4 Results
### Minimize the Power Consumption
#### 4.2.2 Rotation about X-Axis  
We solve the inverse kinematic and dynamic problems for the _StudyVariables_. We observe that the 325 _StudyVariables_ fulfill the requirements of the motors and the linear guides and they are, thus, suitable GP combinations. Figure 9 shows the total power consumption for all the suitable GP combinations for the rotation about X-axis. The GP combination (H, L, R) = (0.75, 0.3, 0.6) m is the combination that consumes the most for this trajectory: 7.5687 W. The lowest power consumption is 0.683 W and it corresponds to the GP combination (H, L, R) = (0.35, 0.2, 0.2) m. Thus, (H, L, R) = (0.35, 0.2, 0.2) m is the best GP combination for the rotation about X-axis.
## 2 Unified Modeling of LEO Satellite Constellation
### Satellite Constellation Model Validation  
The verification of the correctness of the model is an indispensable link from the establishment to the application, and three methods are usually adopted: theoretical analysis, data comparison and simulation comparison. For equipment or systems that can obtain experimental data, validating and calibrating models with real experimental data must be the preferred method. If the device lacks experimental data but has a validated simulation model, the correctness of the model can be verified by comparing the simulation results generated under the same test scenarios. If neither of the above two are available, then the simulation results can only be qualitatively analyzed in combination with the principles of modeling. Figure 5 shows the calculation results of the orbit in MWorks and STK, including true anomaly, inertial position and subsatellite position. Obviously, the deviation between the calculation results of the constellation system model we established and the results of STK is far less than 5%, which can prove that the calculated results of our model in terms of orbit are credible.  
Figure 5: _Cont._
# Study of Fast and Reliable Time Transfer Methods Using Low Earth Orbit Enhancement
## 1 Introduction
[17] assessed the performance of BDS-3 enhanced by an LEO satellite constellation through data simulation, and the findings demonstrated that the PPP convergence time was decreased from approximately 30 min to 1 min. [PERSON] et al. [2] appraised the efficacy of the integrated LEO/GNSS PPP under adverse environmental conditions and executed comparative experiments with observational simulations, revealing that the convergence velocity and precision of both fixed and floating solutions derived from the LEO enhancement of GNSS were enhanced. [PERSON] et al. [18] developed three types of LEO constellations with 60,192 and 288 satellites and simulated the observation data with GNSS observations over the ground station. Concurrently, the LEO constellation was designed to transmit navigation signals at three frequencies: L1, L2 and L5. The results demonstrated that the introduction of LEO constellations can markedly enhance the performance of both single-system and multi-GNSS PPP AR. [PERSON] et al. [19] studied the feasibility of providing real-time PPP services with LEO-augmented GNSS. The results confirmed that LEO-augmented GNSS can accomplish spatial signal ranging with an error at the centimeter level. Based on the aforementioned studies, the integration GNSS with LEO satellites can enhance the performance of PPP. However, a scarcity of studies have applied LEO enhancement to time transfer. Consequently, this study concentrates on the application of LEO-augmented GNSS to time transfer. This paper employs GPS data as a case study. Bydesigning a constellation of LEO satellites and simulating satellite observations, we conducted an analysis of the performance of GPS time transfer enhanced by LEO, incorporating actual GPS observation data.
## 3 Comparative Analyses Results
### Rain-Gauge vs. Satellite Precipitation Products
#### 3.2.2 MW Sensor-Based Product
[52] over southeastern part of Austria, using the WegenerNet rain-gauge gridded dataset as a reference, the authors found that the backward morphing scheme of IMERG-Late (V03) had only little effects compared to the simple forward scheme of IMERG-Early (V03). Furthermore, in their study they marked that EARLY and LATE tended to overestimate low rain rates, such as is observed with the case study over the JUR area and, inversely, that high-intensity precipitation amounts were underestimated by the IMERG family, as is observed over the NMC area during the June case.