esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving Human Activity Monitoring by Imputation of Missing Sensory Data: Experimental Study
## 4 Discussion
Moreover, the deep learning model gives the best results when imputed and normalized data from the combination of all three sensors are used, i.e., the accelerometer, magnetometer, and gyroscope. Furthermore, the use of data imputation reported an improvement of 25.36% in accuracy, 21.58% in precision, 25.36% in recall, and 23.52% in F-measure values over the normalized and imputed dataset across all three sensors, as compared to the non-normalized and non-imputed dataset across all three sensors. Therefore, from the above experimental results, it is verified that the performance of the deep learning model significantly increased when normalization and imputation techniques were applied to the dataset across all three sensors. As we are using a proprietary dataset, the results are not comparable with others. However, several limitations were found that are related to the acquisition and positioning of the mobile device, the power processing of the methods implemented, and other involuntary limitations of the study [6; 24]. The results obtained are affected by the reduced sample size. Initially, the data normalization was performed, and the maximum accuracy was around 89.51% [34; 35] with the recognition of the same activities and with the use of the same sensors of this study. The implementation of imputation techniques increased the results with a maximum accuracy of 100%. Thus, we can conclude that the data imputation techniques increased the different results.
# Sensorless Air Flow Control in an HVAC System through Deep Learning
## 3 Predicting Static Pressure Using Deep Learning
### Proposed Deep Learning Model
#### 3.2.1 LSTM Network  
The entire LSTM network consists of input and output layers and two hidden layers, as shown in Figure 9, which is an unfolded representation. In the input layer, seven input samples at the current and the nine previous time steps, \(x_{t-9}\), \(x_{t-8}\), \(\dots\), \(x_{t-1}\), \(x_{t}\), are applied.  
Figure 8: Our Long Short-Term Memory (LSTM) structure for the sensorless system.  
In other words, 10 previously measured sets of seven sample values are sequentially inputted in order to predict the next static pressure. The hidden layers consist of an LSTM layer and a fully connected layer. We found that the accuracy of the predicted static pressure value was higher when placing a fully connected layer between the LSTM layer and the output layer compared with directly connecting the 50 cells of the LSTM to the output layer. Similarly, the number of LSTM cells was selected empirically. When the number of cells is large, training takes longer, but, when it is small, the prediction accuracy is lower. For the activation functions, we employed the hypertangent (tanh) for the LSTM layer and the Rectified Linear Unit (ReLU) for the fully connected layer, as shown in Figure 10.  
We used a stateless LSTM model. In a stateless model, the old state is memorized only by the batch size; if it is stateful, then the old learning contents are memorized throughout the learning process. As shown in Figure 11, in the stateful case, training and validation loss are no longer improved as the epoch continues. In the stateless case, the loss is slightly increased, but the performance is better than that in the stateful case because the loss decreases gradually. Thus, the stateless method is more effective for the static pressure prediction model than the stateful method for maintaining the learned contents throughout the learning process.  
Figure 10: Activation functions: (**a**) hypertangent (tanh) used for hidden layers; (**b**) rectified linear unit (ReLU) activation function used for the output layer.  
Figure 9: Deep learning structure for forecasting static pressure in ducts.
## 1 Introduction
### Contribution  
Within the MFG framework, the solutions in [31; 32; 33; 34; 35] focus on various wireless networking applications. However, despite this extensive research, there is a notable gap in the literature regarding the application of MFG theory to the routing processes in WSNs. The main contributions of the work can be outlined as follows:  
1. The study focuses on enhancing the data transmission phase of the LEACH protocol by introducing the application of the MFG framework, which models the interactions among sensor nodes as a game. Each node independently optimizes its transmission energy based on the collective state of the network.
2. This work introduces an MFG model that considers two network dynamics; distance and the remaining energy of the nodes. This model holds significance as the incorporation of these parameters is crucial for enhancing the performance of the LEACH protocol.
3. Scalability analysis is performed in this work as the proposed technique is evaluated across different node densities.
# Telehealth Secure Solution to Provide Childhood Obesity Monitoring
## 6 Security Analysis
### Bit Error Rate  
The cryptogram can be altered either by noise attack or noise jamming during the transmission process, which can affect the ability to correctly recover the original plain text in the receptor. The noise robustness is presented with BER in percentage between the plain text and the recovered (decrypted) text. The results are based on different scenarios of additive white Gaussian noise (AWGN) added to the cryptogram of the five users, with a signal-to-noise (SNR) ratio of 0.001 dB, 0.01 dB, 0.1 dB, 1 dB, 3 dB, and 0 dB. The lower the SNR, the greater the noise added. Correct and incorrect secret keys in Table 4 are used, where only users 2, 3, and 5 use the correct secret key. In this sense, the recovered text of users 1 and 4 is totally different to the (original) plain text, as can be seen in Table 5. The BER is calculated with the _\"biterr\"_ function of MATLAB [37], and the results are presented numerically in Table 6. Users 1 and 4 have a BER ratio (in percentage) close to 50% for all cases with any SNR added noise, which indicates that decrypted text and plain text are very different, since incorrect secret keys were used in all noise cases. Conversely, the BER ratio for users 2, 3, and 5 is less than 1% in the worst case of AWGN noise (0.001 SNR). The BER ratio of 0% means that the decrypted text is identical to the plain text.  
Based on the above results, the proposed scheme is highly robust against noise attack or noise jamming, since less than 1% of bits are lost despite high levels of noise added to the cryptogram. Considering that each user has plain data of 160 bits, only 1 bit was lost in the decryption process.
# A Space-Interconnection Algorithm for Satellite Constellation Based on Spatial Grid Model
## 2 Methods
### GeoSOT-3D and Aerospace Grid BigTable  
#### 2.1.1 GeoSOT-3D Grid Model and Coding Method  
The geographic coordinate subdivision grid with one-dimensional integer coding on a 2\({}^{\text{n}}\)-tree (GeoSOT) model is a subdivision and coding method that subdivides the Earth's surface into grids [31]. The subdivision method is as follows: via a three-time virtual expansion of the earth (the real space on the earth surface is extended to \(512^{\circ}\times 512^{\circ}\), 1\({}^{\circ}\) extended to \(64^{\prime}\), 1\({}^{\prime}\) extended to \(64^{\prime\prime}\)), the global subdivision model can maintain grid code integral degrees, minutes and seconds, when a quad-tree subdivides the latitude and longitude coordinate space [32]. Finally, a multi-scale grid system is formed up to the global level (level 0) and down to the centimeter level (level 32) [33].  
Figure 1: Research process.  
The GeoSOT-3D model expands the height dimension of the GeoSOT grid model, and divides the three dimensions of longitude, latitude, and elevation continuously through octree division, forming a multilevel 3-D grid system [34]. Finally, we obtain a multilevel grid model of integer degrees, integer minutes, and integer seconds, and form an earth grid 3-D model with a space of 50,000 km up to the outer periphery of the earth and down to the center of the earth, with 32 levels. The grids of the same level have no gaps or overlaps, and the grids of adjacent levels are nested within each other. Each grid is then assigned a code, which is used to uniquely identify the spatial area. Codes of grids at the same level are not random, but are sorted according to the Z-order space-filling curve (a spatial indexing method), which will result in neighboring grids having codes with close values [36]. Eventually, the high-dimensional spatial information is converted into a one-dimensional aerospace coordinate code, which is convenient for the identification, storage, and calculation of spatial information (Figure 2).
## 2 Experimental Methods
### Evaluation Databases
#### 2.2.1 ISBI Public Database  
[PERSON] et al. [5] released a public database of 400 cephalometric images, where 19 of the more common landmarks were annotated on each image. A list of all the annotated landmarks is presented in Table 1. Radiographs were collected from 400 patients ranging from 6 to 60 years old. All cephalograms were captured by the same X-ray device. Every image was annotated manually by two experienced medical doctors. A ground truth was determined as an average of the annotations of both doctors. The images have the same dimension of \(1935\times 2400\) pixels with 10 pixel/mm spatial resolution.  
This database is divided into three sets. The first 300 out of 400 images are from the 2015 Automatic Cephalometric X-Ray Landmark Detection Challenge [4]. These 300 images were split into a training set (150 images) and testing set 1 (the remaining 150 images). The 2016 Automatic Cephalometric X-Ray Landmark Detection Challenge brought another 100 images to this public database. These 100 images are denoted as testing set 2. Figure 1a depicts a sample annotated image from this public database. Landmarks are actually pixels, but they are depicted as white circles in this image.  
\begin{table}
\begin{tabular}{c c c c} \hline \(-1\)i—Lower incisal incisior & \(+1\)i—Upper incisal incisior & ANS—Anterior Nasal Spine & Ar—Articulate \\ Gn—Gnathion & Go—Gonion & Li’—Lower lip & Ls’—Upper lip \\ Me—Menton & N—Nasion & Or—Orbitale & Pg—Pogonion \\ Pg—Point Soft Pogonion & PNS—Posterior Nasal Spine & Po—Portion & S—Sella Turcia \\ Sn’—Subnasale & SS—Subspinale (Point A) & SM—Supramentale (Point B) & \\ \hline \end{tabular}
\end{table}
Table 1: A list of 19 cephalometric landmarks annotated in the ISBI public database. A description of the landmarks and their significance can be found in [3].
## 3 Results
### Workload Measures and Match Category  
Of the matches analyzed (N = 890), 60.90% (N = 542) were pool play, 12.58% (N = 112) were cup quarterfinals, 13.71% (N = 122) were cup semifinals, and 12.81% were cup finals. The greater incidence of cup semifinals compared to cup quarterfinals stemmed from differences in tournament structures by the hosted event, where some tournaments moved from pool play to quarterfinals and others moved from pool play directly to semifinals. There was a main effect for match category for mechanical work (\(F(3,886)\) = 10.12, \(p\) < 0.05), sRPE (\(F(3,886)\) = 10.12, \(p\) < 0.05), and the SDC model (\(F(3,886)\) = 8.79, \(p\) < 0.05). [PERSON]'s post hoc tests identified significant differences in workload between pool play and cup quarterfinals and cup finals for mechanical work (pool play-cup quarterfinals mean difference = -6512.51, 95% CI [\(-\)12,168.26, \(-\)856.76], \(p\) < 0.05, \(d\) = \(-\)0.31, indicating a small-to-moderate effect; pool play-cup finals mean difference = -6627.18, 95% CI [\(-\)12,222.40, \(-\)1033.96], \(p\) < 0.05, \(d\) = \(-\)0.31, indicating a small-to-moderate effect), sRPE (pool play-cup quarterfinals mean difference = -15.31, 95% CI [\(-\)26.87, \(-\)3.75], \(p\) < 0.05, \(d\) = \(-\)0.36, indicating a small-to-moderate effect; pool play-cup finals mean difference = -21.09, 95% CI [\(-\)32.52, \(-\)9.66], \(p\) < 0.05, \(d\) = \(-\)0.50, indicating a moderate effect), and the SDC model (pool play-cup quarterfinals mean difference = -22,665.50, 95% CI [\(-\)40,647.23, \(-\)4683.77], \(p\) < 0.05, \(d\) = \(-\)0.06, indicating a trivial effect; pool play-cup finals mean difference = -25,629.96, 95% CI [\(-\)39,629.52, \(-\)11,830.39], \(p\) < 0.05, \(d\) = \(-\)0.45, indicating a moderate effect) (Figure 1). Further differences between pool play and cup semifinals were present in sRPE (mean difference = -10.22, 95% CI [\(-\)21.34, 0.90], \(p\) < 0.05, \(d\) = \(-\)0.24, indicating a small effect) and between cup quarterfinals and cup semifinals in  
\begin{table}
\begin{tabular}{c c} \hline
**Match Category** & **Operational Definition** \\ \hline Pool & Denotes games happening in the first phase of play (pool play) to determine a ranking within a small group of teams determined ahead of the tournament. \\ Cup Quarterfinal & First knockout stage of the tournament for teams, after pool play, whereby the team finished in top half of their respective pool and advanced in the tournament. \\ Cup Semifinal & Second knockout stage of the tournament, after the quarterfinal, whereby the team is guaranteed a top four finish in the tournament. \\ Cup Final & Final knockout stage of the tournament, after the medal semifinal, whereby the team plays for a final placing, with a win meaning either a bronze medal (third place) or gold medal (first place) in the tournament. \\ \hline \end{tabular}
\end{table}
Table 1: Operational definition of match categories.  
the SDC model (mean difference = \(-\)30,380.84, 95% CI [ \(-\)47,950.51, \(-\)12,811.18], \(p\)\(<\) 0.05, \(d\) = \(-\)0.21, indicating a small effect) were identified (Figure 1).
## 3 Circuit Models  
In this section, the circuit models of some representative structures of resonance-based and frequency splitting structures are provided and validated. As is well known, the accuracy of the models is subjected to the electrical size of the considered resonant elements.
## 2 Materials and Methods
### Observational Forcing Datasets
#### 2.1.2 NDVI and LAI at 10m Resolution Derived from Sentinel-2  
We calculate the Normalized Difference Vegetation Index (NDVI) from the Sentinel-2 reflected radiance by  
\[NDVI=\frac{R_{nir}-R_{red}}{R_{nir}+R_{red}} \tag{1}\]  
where \(R_{nir}\) and \(R_{red}\) are the spectral bands at near infrared (842 nm) and red (665 nm), respectively. The Sentinel-2 reflectance data are available at the USGS EROS Center ([[https://www.usgs-gov/centers/eros](https://www.usgs-gov/centers/eros)]([https://www.usgs-gov/centers/eros](https://www.usgs-gov/centers/eros)), accessed on 8 April 2021). The leaf area index (LAI) at a 10m resolution was derived from the retrieved Sentinel-2 NDVI using a nonlinear regression model between LAI and NDVI,  
\[LAI=a*exp(b*NDVI)+c \tag{2}\]  
where the parameters \(a\), \(b\) and \(c\) are determined as 12.4, 6.4 and 0.6, respectively.  
The determination process was based on MODIS-based NDVI and LAI products, which was described as: (i) The MODIS LAI (MOD15A2H) and surface reflectance (SR) products (MOD09A1) at 500m were collected over the study area (Beijing Sponge City) for the summer months (June, July, and August) from 2013 to 2019. The NDVI was then estimated using the 500m SR product (Equation (1)). (ii) MODIS LAI and NDVI values were collocated on the pixel basis. As the MODIS LAI product has a valid range between 0 and 6.9, with a precision of 0.1, we classified all NDVI values into 69 groups based on unique LAI values (eliminating the zero-LAI group). The probability density plots for each group are shown in Figure 0(a). (iii) For each LAI-based value group, the probability density was fitted using the Gaussian distribution function. Then, the NDVI value corresponded by the maximum probability density was extracted and collocated with the specific LAI value.  
The result shows a strong exponential relationship between NDVI and LAI, especially when the LAI value increased beyond 0.5-0.6 (Figure 1b). Therefore, the scatter values were fitted using the exponential model (Equation (2)), which resulted in an R\({}^{2}\) of 0.82, implying that such exponential model in Equation (2) is robust for the Beijing Sponge City.
## 3 Results
### Operating Conditions of the Cascade Cycle  
For the cascade cycle, the parameter that must be optimized is the intermediate temperature level (T\({}_{\text{I}}\)), the condensing temperature of CO\({}_{2}\) (T\({}_{\text{K,L}}\)) being considered in this case for its representation. As mentioned, exit of CO\({}_{2}\) cascade condenser is in saturation, no subcooling is considered, because it provides lower efficiency results. Figure 9 presents the evolution of the overall COP of the cascade solution for an evaporation level of 0 \({}^{\circ}\)C for all the considered environment temperatures. Limits of variation of T\({}_{\text{K,L}}\) are any temperature over the evaporating pressure up to a condensing temperature 5 K below the environment temperature (if T\({}_{\text{env}}\) < 25.978 \({}^{\circ}\)C) or the critical temperature. In Figure 9 it becomes clear that an optimum T\({}_{\text{K,L}}\) temperature exists. No more emphasis is done because different authors studied it in detail [29; 30]. COP values at the optimum T\({}_{\text{K,L}}\) are presented in Figure 10. In contrast to the COP evolutions of the MS cycle, it needs to be highlighted that the reduction of COP of cascade systems due to variations of the environment temperature is smoother, being these systems less sensitive to variations of environmental conditions, as previously mentioned by [PERSON] et al. [14]. Also, to compare the design of the cascade system, the ratio of the high-temperature and low-temperature power consumption are presented in Figure 11. In this case, the power consumption of the high-temperature compressor inside the evaluated range is of the same order of magnitude as that of the CO\({}_{2}\) cycle. With a design of the plant as cascade, it could operate with the MS cycle but not the other way round.  
Figure 8: Ratio of compressor’s power consumptions of the MS cycle.  
Figure 10: COP of the cascade cycle at optimum conditions.  
Figure 9: COP dependence on the low-temperature condensing temperature of cascade system. (T\({}_{\rm o}\) = 0 \({}^{\circ}\)C).
# A Linear Fitting Algorithm Based on Modified Random Sample Consensus
## 4 Algorithm Verification and Discussion
### Technical Indicators  
After fitting the line models using the various algorithms, their performance should be accessed based on several technical metrics, including the Residual Sum of Squares (RSS), the coefficient of determination \(R^{2}\), and the Mean Absolute Error (MAE).  
1. The RSS quantifies the squared differences between the observed values and the predicted values, reflecting the degree of deviation between the model and the data. A smaller RSS indicates a better model fit and improved predictive capability. The RSS is defined as follows: \[\text{RSS}=\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}\] (21) where \(y_{i}\) represents the actual values and \(\hat{y}_{i}\) denotes the predicted values.
2. The coefficient of determination \(R^{2}\), also known as the goodness-of-fit statistic, reveals the extent to which the independent variables can explain or predict the variation in the dependent variable. The value of \(R^{2}\) ranges from 0 to 1: a value close to 1 suggests an excellent model fit and a strong explanation of the data's variance, while a value close to 0 indicates poor explanatory power regarding the changes in the dependent variable. The \(R^{2}\) is calculated based on the relationship between the RSS and the total sum of squares, defined as \[R^{2}=1-\frac{RSS}{TSS}=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i =1}^{n}(y_{i}-\bar{y}_{i})^{2}}\] (22) where \(\bar{y}\) is the mean of the data.
3. The MAE measures the average magnitude of the errors in predictions, with smaller errors indicating greater accuracy. The MAE is defined as follows: \[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}|y_{i}-\hat{y}_{i}|\] (23)
# Beyond Mitigation: New Metrics for Space Sustainability Assessment +
## 2 Space Sustainability by Design--Backward and Forward Compatibility  
The issue of the sustainable use of space and its protection has become very urgent. Space operations have become challenging as the margin of error for maintaining separation between satellites becomes reduced and the consequent higher probability of collisions leads to further debris increase. Beyond debris, there are other issues that urgently need to be addressed, such as interference with astronomical observations, radio frequency interference with other communication systems, and challenging spectrum management. Several recent initiatives and projects are ongoing to face the space sustainability challenge. Most of the initiatives and research activities are focused on mitigating the damages caused by the wild use of satellite orbits and an uncontrolled generation of space garbage. However, just mitigating the already-made damages will not guarantee long-term space sustainability and safe use of space. We must start designing novel systems with sustainability in mind from the beginning. The Space Sustainability by Design approach places the space sustainability requirements at the start of the design phase or even during the planning stage. A sustainable system should take into account what is already available in space, before sending new hardware out, and also ensure that it is possible to upcycle any of pre-existing ones. This capability would indicate that a system is backward-compatible, meaning it can reuse already deployed space infrastructure. On the other hand, a sustainable system should also be flexible enough to be used for different missions in the future and even for different purposes. In the last case, the designed mission/constellation is forward-compatible. Such by-design flexibility would allow a reduction in the space/aerial nodes to be launched and a more efficient use of the platforms, payload, and links that are involved, thus making a real, more sustainable Space. Key enabling technologies have been identified, such as softwarization pervasiveness, high data rate Inter Satellite Links (ISLs) and interlayer links at millimeter waves, optical and THz frequencies and the use of AI and ML tools [3]. However, to provide system designers with a flexible tool to optimize their design to meet the best trade-off between performance and long-term space sustainability, novel KPIs must be identified to _measure_ the impact on space sustainability of different design choices.