esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study
### Discussion
#### 4.2.5 Sensitivity Analysis of Different Confidence Levels  
To analyze the effects of possible inaccuracies in forecasts or assumptions on our proposed method, the incomes of SES, VPP, and PVP alliance are analyzed and obtained by our proposed method under different confidence levels of VPP renewable energy output. The analysis results are given in Table 7.  
It can be seen from Table 7 that as the confidence level of wind and scenery uncertainty increases from 0.8 to 0.95, VPP revenue shows an increasing trend, increasing by CNY 2261.51. SES revenue slightly decreases, but the decrease is not very large compared with the former, decreasing by CNY 254.78. PVP alliance revenue slightly increases, decreasing by about CNY 115.85. This indicates that the increase in the confidence level of renewable energy output uncertainty in VPP will have a direct impact on the income of all entities in the system, but the greatest impact on VPP itself.  
Our proposed energy trading strategy can be used for optimal low-carbon operation of VPPs with large-scale renewable energy and some unaggregated electricity consumers and distributed photovoltaic stations, while SES participates as an independent market entity.  
The limitations of the proposed approach are that the transaction price formed by the game between the VPP and the PVP alliance would fail under policy regulation. And, the transaction price of the game between the SES and the VPP is directly related to the maximum power delivered by the VPP to the outer power grid.
## 2 Related Work
### Surface Defect Detection Methods
A slightly more complex method is to de-compose images using dual deep reconstruction networks-based image decomposition (DDR-ID) while optimizing for three losses, viz., one-class loss, latent space constraint loss, and reconstruction loss. Once trained, the DDR-ID can decompose an unseen image into residual components to determine anomaly scores. It can then, based on predefined thresholds, confidently classify anomalous images [22]. The third reconstructive method uses a multi-stage image resynthesis framework. The method takes defective images and attempts to repair suspicious regions that have large deviations from the original input image. Defects are then localised in the residual map between input images and the repaired outputs [23]. The use of an adversarial autoencoder (AAE) was suggested by [PERSON] et al. [24] in an effort to reduce the influence of the 'contamination' of flawed samples on autoencoder networks. A generative model of the input data can be trained by combining the reconstruction error with an adversarial training criterion, and a discriminator network gains flexibility by learning to distinguish between samples originating from the encoder. The utilisation of DenseNet architecture in anomaly detection for surface defects detection [25] and classification [26] in fabrics has shown promising outcomes because of its capacity to capture intricate patterns and handle noisy data effectively. However, its dense architecture presents a challenge, as it demands substantial computational resources and time for both training and inference processes.
## 3 Energy Calibration
### Channel-Energy Relation  
All the source spectra produced with GMOD followed a primarily Gaussian shape, which varied depending on the underlying interaction with the CeBr\({}_{3}\) crystal. The photopeaks all fit with the following Gaussian function:  
\[G(x)=\frac{N}{\sigma\sqrt{2\pi}}e^{\frac{-(x-y)^{2}}{2\sigma^{2}}}\,, \tag{1}\]  
where \(N\) is the height of the peak, \(\sigma\) is the standard deviation and \(\mu\) is the position of the centre of the peak [46]. In order to translate the ADC channels to energy values, a channel-energy relationship was first determined. Each photopeak from the sources listed in Table 2 was fitted with a Gaussian function, and the channel number for the centre of the photopeak, \(\mu\), was extracted. Figure 6 presents four examples of the functions fitted to energy peaks in the spectra to determine the channel number of the peak centre.  
In the analysis, the gamma-ray photopeaks could not be fitted with a simple Gaussian, as there are other contributions in the photopeak such as from back-scattering off of nearby structures, background contributions and Compton distributions (as seen in the spectra in Figure 5). In general, the peaks were fitted with a combination of a Gaussian function and a polynomial function to account for other contributions to the photopeak. The final fit is plotted as a dashed line and the composite functions are plotted separately as solid lines in Figure 6.  
Figure 5: GMOD FM background-subtracted spectra (teal) in counts/hour for the six sources in Table 2. In each panel, the background spectrum (grey) and the raw source spectrum (purple) are presented.  
The ADC channel numbers associated with each peak centre, \(\mu\), were then plotted against the known energy of the photopeaks, as listed in Table 1, to map the channel numbers to energy. Figure 7 presents the channel-energy relation for GMOD FM. The relationship between the ADC channel number and energy, E, was best fitted with a quadratic of the form  
\[E=a(\text{Channel Number})^{2}+b(\text{Channel Number})+c \tag{2}\]  
Figure 6: A sample of four fits to GMOD spectra in terms of ADC channel number. Each panel shows the fit (dashed grey line) to the background-subtracted spectrum. The composite functions of the fit are plotted as solid lines.  
where \(a\), \(b\) and \(c\) are the fit coefficients. The coefficients of the fit to the GMOD channel energy relation are \(a=3.56\times 10^{-5}\), \(b=0.42\) and \(c=-17.30\).
# An Improved Visual SLAM Based on Map Point Reliability under Dynamic Environments
###### Abstract  
The visual simultaneous localization and mapping (SLAM) method under dynamic environments is a hot and challenging issue in the robotic field. The oriented FAST and Rotated BRIEF (ORB) SLAM algorithm is one of the most effective methods. However, the traditional ORB-SLAM algorithm cannot perform well in dynamic environments due to the feature points of dynamic map points at different timestamps being incorrectly matched. To deal with this problem, an improved visual SLAM method built on ORB-SLAM3 is proposed in this paper. In the proposed method, an improved new map points screening strategy and the repeated exiting map points elimination strategy are presented and combined to identify obvious dynamic map points. Then, a concept of map point reliability is introduced in the ORB-SLAM3 framework. Based on the proposed reliability calculation of the map points, a multi-period check strategy is used to identify the unobvious dynamic map points, which can further deal with the dynamic problem in visual SLAM, for those unobvious dynamic objects. Finally, various experiments are conducted on the challenging dynamic sequences of the TUM RGB-D dataset to evaluate the performance of our visual SLAM method. The experimental results demonstrate that our SLAM method can run at an average time of 17.51 ms per frame. Compared with ORB-SLAM3, the average RMSE of the absolute trajectory error (ATE) of the proposed method in nine dynamic sequences of the TUM RGB-D dataset can be reduced by 63.31%. Compared with the real-time dynamic SLAM methods, the proposed method can obtain state-of-the-art performance. The results prove that the proposed method is a real-time visual SLAM, which is effective in dynamic environments.  
visual SLAM; dynamic environment; map point reliability; geometric constraint +
Footnote †: journal: Applied  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON] An Improved Visual SLAM Based on Map Point Reliability under Dynamic Environments. _Appl. Sci._**2023,** 13, 2712. [[https://doi.org/10.3390/app13042712](https://doi.org/10.3390/app13042712)]([https://doi.org/10.3390/app13042712](https://doi.org/10.3390/app13042712))
## 3 Results and Discussion
### No\({}_{2}\)  
Studying the performance of each variable individually, Figure 7 shows the results obtained for the variable NO\({}_{2}\).  
It is clear that the linear and Kalman imputation methods are the ones that obtain the lowest RMSE values, being between 1.83-2.53 for LI and between 1.86-2.48 for Kalman. The values obtained are quite consistent for each method for all stations. Slightly higher values were obtained at station ST 12 (2.53 and 2.48, respectively) and the lowest RMSE values were obtained at station ST 20, with an error of 1.83 for LI and 1.86 for Kalman. ST 12 is the station that obtained the highest RMSE in all the methods, being that the UMI method is the one that generates a much higher RMSE error in all the stations.
# A Satellite Task Planning Algorithm Based on a Symmetric Recurrent Neural Network
## 4 Mixed Integer Programming Model
Equations (9)-(11) indicate that the total memory, total energy, and total working time of all tasks in the planning sequence do not exceed the available memory space, total energy cap, and working time. Equation (12) presents the total profit of the satellite during the planning process. The core purpose of task planning is to maximize \(P\).
###### Abstract  
This paper investigates the usefulness of combining dielectric and conductive materials with various geometries to build resonators and enhance patch antenna performance when considering the decoupled design approach. Three materials (distilled water, glycerin, and graphite) and fifteen geometries are considered. The obtained results through numerical simulations show how the selected materials and geometries can be combined to strategically design resonators that can effectively confine electromagnetic fields, shift the resonant frequency, improve antenna directionality and effective gain, facilitate multi-band operation, broaden the bandwidth, and improve impedance matching and the radiation pattern. Overall, the analyses and discussions offer insights and guidelines for combining dielectric and conductive materials with several geometries to build a resonator that can improve patch antenna performance when a low-quality substrate is considered.  
patch antenna; resonator; distilled water; glycerin; graphite; geometric shapes Article
# Performance of Solar Control Films on Building Glazing: A Literature Review
## 5 Discussion of Existing Studies on the Performance of Solar Control Films
### Glazing Type, Film Type and Installation Position  
Figure 8 shows, in terms of percentage, the different types of glazing substrates, types of SCFs and respective installation positions assessed by the existing studies reported in the literature review.  
Double-glazing is the most common type of glazing substrate used to assess the performance of SCFs, followed by single-glazing. The majority of the studies considered clear glazing without film as the reference scenario to compare with the results obtained in the presence of SCFs, with the exception of the studies conducted by [PERSON] et al. [34] and [PERSON] et al. [49] that considered the installation of films in tinted glazing.  
Reflective and spectrally selective SCFs are commonly evaluated by existing studies, followed by neutral and low-E films. These types of SCFs cover the majority of films available on the market and usually installed in building glazing. Existing studies tend to focus on SCFs with external application, followed by internal SCFs. The scarce number of studies on the performance assessment of SCFs installed in the air gap, corresponding to the installation of low-E films, could be explained by the fact that SCFs are usually used as rehabilitation solutions and, as a result, the installation tends to be restricted to the internal and external glass surfaces.
## 3 Results
Figure 4: Median split analysis showing higher (\(n=13\)) vs. lower (\(n=13\)) groups for peak force during the isometric mid-thigh pull and subsequent differences in golf shot data when using a driver. * indicates significant difference between groups (\(p<0.05\)) as shown by Hedges \(g\) effect size (ES) data with 95% confidence intervals. Figure 5: Median split analysis showing higher (\(n=13\)) vs. lower (\(n=13\)) groups for peak force during the isometric bench press and subsequent differences in golf shot data when using a driver. * indicates significant difference between groups (\(p<0.05\)) as shown by Hedges \(g\) effect size (ES) data with 95% confidence intervals. Figure 6: Median split analysis showing higher (\(n=13\)) vs. lower (\(n=13\)) groups for jump height during the countermovement jump and subsequent differences in golf shot data when using a driver. * indicates significant difference between groups (\(p<0.05\)) as shown by Hedges \(g\) effect size (ES) data with 95% confidence intervals. Figure 4 (IMTP peak force) shows significant differences between the groups for CHS (\(g=1.09\) [0.24, 1.88]; \(p<0.05\)), ball speed (\(g=1.18\) [0.35, 2.02]; \(p<0.05\)), and carry distance (\(g=1.06\) [0.24, 1.88]; \(p<0.05\)), but not smash factor (\(g=0.76\) [\(-0.03\), 1.57]; \(p>0.05\)). Figure 5 (isometric bench press peak force) shows significant differences between the groups for all golf shot metrics: CHS (\(g=1.14\) [0.31, 1.97]; \(p<0.05\)), ball speed (\(g=1.41\) [0.55, 2.26]; \(p<0.05\)), carry distance (\(g=1.23\) [0.40, 2.07]; \(p<0.05\)), and smash factor (\(g=1.17\) [0.34, 2.00]; \(p<0.05\)). Similarly, Figure 6 (CM) height) also showed significant between-group differences for all golf shot data: CHS (\(g=1.28\) [0.44, 2.12]; \(p<0.05\)), ball speed (\(g=1.34\) [0.49, 2.19]; \(p<0.05\)), carry distance (\(g=1.53\) [0.66, 2.40]; \(p<0.05\)), and smash factor (\(g=0.81\) [0.01, 1.61]; \(p<0.05\)). Finally, Figure 7 (medicine ball throw for distance) showed no significant between-group differences for any golf shot metric: CHS (\(g=0.81\) [0.01, 1.61]; \(p>0.05\)), ball speed (\(g=0.69\) [\(-0.10\), 1.48]; \(p>0.05\)), carry distance (\(g=0.48\) [\(-0.30\), 1.27]; \(p>0.05\)), and smash factor (\(g=0.20\) [\(-0.57\), 0.98]; \(p>0.05\)).
# Data-Driven Approaches for Predicting and Forecasting Air Quality in Urban Areas
## 2 Materials and Methods  
According to the Office of the New York State Comptroller [64], between 1980 and 2017, the Bronx's population increased by 26%, reaching nearly 1.5 million people. Public and private investments supported this growth, which fueled the creation of new businesses and thousands of jobs. For ten years ending in 2017, the Bronx added 40,900 private sector jobs and was the only New York City borough that did not lose jobs during the Great Recession. The Bronx has set employment records for ten consecutive years [64]. An extended transportation network, including seven significant highways and seven subway lines, has further bolstered the Bronx's economy. New ferry services and Metro-North rail stations are also being developed to improve connectivity with Manhattan. This industrial growth and increased mobility have contributed to the Bronx's economic resilience, encouraging further research into the area's development. The borough saw a 24% drop in businesses between 1975 and 1984, but since then, the number has risen by 35%, reaching almost 18,000 enterprises by 2017 [64]. This increasing population, industrialization, and transportation improvements justify research interest in the Bronx's AQ due to industrialization and traffic patterns.  
In this study, we use data from the United States Environmental Protection Agency (EPA) through its comprehensive outdoor AQ database, which offers daily measurements of various air pollutants across different locations in the United States. Daily statistics related to traffic levels and meteorological conditions in Bronx, New York, were imported from the Open NY (the New York State's open data platform [65]) and Open Meteo API Portal (OMAP) [66]. These data sources were employed to establish key variables influencing _AQI_ predictions, helping to develop the model by integrating traffic volumes, weather patterns, CO, NO\({}_{2}\), SO\({}_{2}\), and PM\({}_{2.5}\). The units of measurement for AQ and weather factors are as follows: temperature in \({}^{\circ}\)C, CO is measured in parts per million (ppm), NO\({}_{2}\), SO\({}_{2}\), and PM\({}_{2.5}\) are measured in micrograms per cubic meter (\(\upmu\)g/m\({}^{3}\)), wind speed in kilometers per hour (km/h), humidity in percentage (%), and vehicular traffic in number of cars. The data correspond to the period from 1 January 2020 to 31 December 2023, representing a large volume of data that allows us to obtain a model training with a high accuracy in values prediction.
###### Abstract  
Digital terrestrial television is now implemented in many countries worldwide and is now mature. Digital Video Broadcasting-Terrestrial, second generation (DVB-T2) is the European standard adopted or deployed by European and African countries which uses Orthogonal Frequency-Division Multiplexing (OFDM) modulation to achieve good throughput performance. However, its main particularity is the number of subcarriers operated for OFDM modulation which varies from 1024 to 32,768 subcarriers. Also, mobile reception is planned in DVB-T2 in addition to rooftop antenna and portable receptions planned in DVB-T. However, the main challenge of DVB-T2 for mobile reception is the presence of a carrier frequency offset (CFO) which degrades the system performance by inducing an Intercarrier Interference (ICI) on the DVB-T2 signal. This paper evaluates the system performance in the presence of the CFO when Gaussian noise and a TU6 channel are applied. Universal Filtered Multicarrier (UFMC) and non-uniform constellations (NUCs) have previously demonstrated good performance in comparison with OFDM and Quadrature Amplitude Modulation (QAM) in DVB-T2. The impact of CFO on the UFMC- and NUC-based DVB-T2 system is additionally investigated in this work. The results demonstrate that the penalties induced by CFO insertion in UFMC- and NUC-based DVB-T2 are highly reduced in comparison to those for the native DVB-T2. At a bit error rate (BER) of \(10^{-3}\), the CFO penalties induced by the native DVB-T2 are 0.96 dB and 4 dB, respectively, when only Additive White Gaussian Noise (AWGN) is used and when TU6 is additionally considered. The penalties are equal to 0.84 dB and 0.2 dB for UFMC/NUC-based DVB-T2.  
CFO; channel coding; DVB-T2; multicarrier modulation; UFMC NUC Article
## 6 Application of the Optimization Techniques: Routing Protocols
### Multipath Routing Protocols
#### 6.4.4 M-MPR (Mesh Multipath Routing)  
This protocol presents two operation modes [28]. Firstly, in the disjoint MPR (D-MPR) with Selective Forwarding each packet is individually analyzed by the source and it is routed through different routes. Secondly, the D-MPR with data replication is based on the simultaneous emission of multiple copies of the same packet through different routes. Specifically, all the known routes that communicate the source and the destination propagate the packet. For the route discovery, information about the position of the nodes and about their residual energy is exchanged.
## 3 Results  
### Similarity Scores  
Different embedding models produced qualitatively different similarity scores. Figure 2 shows histograms of similarity scores between given sentences and corresponding responses based on TF-IDF (A) and SBERT (B). For TF-IDF, the similarity scores of both normal and abnormal groups were bi-modal, with small values close to 0 or larger values close to 1 more frequent (Figure 2A). The median values of the similarity scores were 0.60 (normal) and 0.63 (abnormal). However, this difference in median between the two groups was not statistically significant (\(p\) = 0.07, Mann-Whitney U test). In contrast, the similarity scores based on SBERT were clustered around higher values for both normal and abnormal groups (Figure 2B). The abnormal group tended to have lower similarity scores (mean = 0.67) than the normal group (mean = 0.77). This difference was statistically significant (\(p\) < \(10^{-5}\), \(t\)-test). Thus, the similarity scores differed between the normal and abnormal groups for SBERT but not for TF-IDF.  
The similarity scores of the normal participants were higher than those of the abnormal participants, and this difference was significant for some, but not all, of the sentences. The left panel of Figure 3 shows the similarity scores of the normal (black circles) and abnormal (red squares) groups as a function of the sentence ID in the stimulus text, with the error bars representing the standard errors of the mean and the asterisk representing the statistical difference between the normal and abnormal groups. The corresponding \(p\)-values are shown in the right panel of Figure 3, with the blue horizontal line representing a significance level of 0.05. For both embedding models, the similarity scores of the normal group tended to be higher than those of the abnormal group. However, this difference was more pronounced for SBERT than for TF-IDF. Specifically, the similarity scores based on TF-IDF differ significantly for only one sentence (Figure 3A right). In contrast, the similarity scores based on SBERT differ significantly for five sentences (Figure 3B right). Notably, the sentence (4) that produced a significant difference between the normal and abnormal groups based on TF-IDF did not produce a significant difference between the normal and abnormal groups based on SBERT.  
Figure 2: Histograms of similarity scores between sentences and responses based on TF-IDF (**A**) and SBERT (**B**).
## 2 Principle of Passive Microwave Remote Sensing from Space
It is impossible to use a single channel to accurately derive geophysical parameters. ATMS uses 22 channels to simultaneously acquire information about surface parameters, atmospheric profiles of temperature and water vapor, as well as clouds. The NOAA Microwave Integrated Retrieval System (MiRS) utilizes the multiple channel measurements to derive the geophysical parameters simultaneously. The radiance assimilation systems at numerical prediction centers around the world utilize brightness temperatures from multiple microwave and infrared channels onboard various satellitesand improve the initial state of geophysical parameters simultaneously. Radiance assimilation demands more computational resources than what we have. In this study, we use MiRS to study the impact of the ATMS channel 1 measurements on TPW retrievals.
## 4 Results
### Mini-Terminal Platform  
A mini terminal platform was developed to evaluate system performance and simulate user interactions. The platform is an integrated system comprising both hardware and software. It captures, processes and analyzes information, including sound, through an array of sensors and control modules. It also transmits the data to the server for business logic processing, as shown in Figure 10. The highlighted areas on the diagram represent the system's respective modules. The system is communicated via a Wi-Fi wireless communication module. The hardware components include the ESP32-s3 primary controller development board, a microphone, a voice wake-up module, an LCD display, an audio amplifier module, and a speaker. The master control ESP32-s3 development board is used as the core control unit and is responsible for operating and transmitting data throughout the system. The microphone is used to capture the sound signal of the user's speech and detect and recognize it through the voice wake-up module. The server is mainly responsible for processing the business logic, parsing user requests and generating the required audio files, and then returning the processed files to the mini-terminal. A deep neural network model is deployed by the server for recognizing audio files and converting them into text. After being processed by the translation or conversational system, the server can respond to user requests and return the response text converted into an audio file to the speakers, thus realizing the function of multilingual interaction.