esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Analysis
### Spatiotemporal Evolution of Carbon Sink and SDE Analysis
#### 3.1.2 Spatial Distribution of Carbon Sink and SDE Analysis  
To reflect the differences in the carbon sinks of the key SOFRs within the spatial scope, the carbon sink values of the 40 forest regions in 2019 were sorted, and ArcGIS 10.7 was used to link this information with the locations of the forest regions. Then, the natural breaks (Jenks) classification method was selected to classify the carbon sinks in 2019 and visualize them (Figure 3). Additionally, to calculate the direction of the spatial distribution of the carbon sink in the 40 SOFRs, this paper used the spatial statistical tools in ArcGIS 10.7 software to calculate the statistical parameters of the SDEs of the carbon sink in each forest region each year (Table 2).  
Regarding the distribution of the carbon sink in each forest region in 2019, the carbon sink of each forest region was quite different, and the overall regional distribution of the carbon sink was characterized by \"more in the north and south, but less in the middle\", as shown in Figure 3. Among them, the four forest regions of ZhanHe, DongFanghong, DongJingcheng, and SuiYang had the highest carbon sinks, ranging from 9.925 million to 14.951 million tons. Mainly due to the large scale of these forest regions and the implementation of key forestry projects, the quality of these forests is better, and thus, the carbon sink value is higher. The seven forest regions of ShuangFeng, DaiLing, HeLi, WuMahe, TangWanghe, WuYing, and ShangGanLing had the lowest carbon sinks, with values in the range of 1.683-2.803 million tons. Most likely because of the small scale of these forest regions, the management and maintenance of large-scale forest regions is relatively low.  
Figure 3: Spatial distribution of the carbon sink and SDE analysis.  
From 2001 to 2019, the SDE of the SOFRs showed an overall pattern of \"southeast-northwest\" and reflected the characteristics of \"from southeast to northwest\" migration, but the range of change was small. The turning angle \(\theta\) showed a fluctuating downward trend, but the magnitude of change was small, indicating that the carbon sink growth rate of the SOFRs in the southeast of the ellipse axis was slightly slower than that of the SOFRs in the northwest of the ellipse axis. The area of the SDE showed a fluctuating downward trend. The overall area of the ellipse decreased by 7351.1 km\({}^{2}\) compared with the value in 2001, and the rate of decrease was 5.61%. The area of the ellipse reached its maximum value in 2007 at 133,949.3 km\({}^{2}\). The short-axis standard deviation fluctuated downwards, decreasing by 7299.2 m; the long-axis standard deviation also fluctuated, decreasing by 3113.1 m. The changes in the carbon sink values in the SOFRs in Heilongjiang Province showed a trend of agglomeration and migration to the northwest.
## 3 Network Anomaly Detection
### The Structure of Network Anomaly Detection  
Our proposed approach integrates the concept of an autoencoder and adversarial techniques, aiming to compute anomaly scores for network data at distinct time intervals via feature transformation. In detail, this approach incorporates two specific autoencoder models, referred to as the generating model \(AE_{1}\) and the discriminating model \(AE_{2}\). These models are involved in a mutual adversarial interaction designed to optimize their functions. The generator and discriminator are each composed of distinct decoder networks, namely \(D_{1}\) and \(D_{2}\), that operate in conjunction with a commonly shared encoder, \(E\). For better comprehension, the architecture of the proposed network anomaly detection model is visually presented in Figure 2, with further detailed explanations provided subsequently.  
Figure 2: The architecture of the anomaly detection model and the prediction model.
# Internet of Things (IoT) and the Energy Sector
## 4 IoT in the Energy Sector
### Smart Buildings  
The energy consumption in cities can be divided into different parts; residential buildings (domestic); and commercial (services), including shops, offices, and schools, and transport. The domestic energy consumption in the residential sector includes lighting, equipment (appliances), domestic hot water, cooking, refrigerating, heating, ventilation, and air conditioning (HVAC) (Figure 4). HVAC energy consumption typically accounts for half of energy consumption in buildings [124]. Therefore, the management of HVAC systems is important in reducing electricity consumption. With the advancement of technology in the industry, IoT devices can play an important role to control the energy losses in HVAC systems. For example, by locating some wireless thermostats based on occupancy, unoccupied places can be realized. Once an unoccupied zone is detected, some actions can be taken to lower energy consumption. For instance, HVAC systems can reduce the operation in the unoccupied zone, which will lead to significant reduction in energy consumption and losses.  
IoT can also be applied to manage the energy losses of lighting systems. For example, through applying IoT-based lighting systems, the customers will be alerted when the energy consumption goes beyond the standard level. Furthermore, by an efficient analysis of the real-time data, load from  
Figure 4: Share of residential energy consumption.  
Figure 3: A centralized data connectivity in a smart city concept.  
high-peak will be shifted to low-peak levels. This makes a significant contribution to optimal use of electrical energy [119; 125] and reducing related greenhouse gas emissions. Using IoT, the demand response will be more agile and flexible, and the monitoring and demand side management will become more efficient.
# A Receiving Antenna Allocation Scheme for Downlink MU-MIMO-OFDM Transmission
## 4 Performance Evaluation
### Analysis of Limited CSI Feedback Sequence  
Figure 6 plots the averages of the total channel capacity per subcarrier on the downlink MU-MIMO-OFDM transmission and the reduction rate by using the proposed frame sequence versus the threshold value, \(\rho_{th}\). Reduction rate means the rate between the average amount of CSI feedback by using the proposed scheme and the amount of CSI feedback based on two antennas. First, the total channel capacity increases with the threshold value. This is because the number of antenna combinations also increases. As expected, the overhead reduction rate falls. However, even low threshold values greatly reduce the number of antenna combinations and the CSI feedback overhead. For example, the reduction rates are 0.59 (\(\rho_{th}\) = 0.5), 0.62 (\(\rho_{th}\) = 0.6), 0.7% (\(\rho_{th}\) = 0.7), 0.78 (\(\rho_{th}\) = 0.8), and 0.88 (\(\rho_{th}\) = 0.9) in our indoor school environment. From these plots, the channel capacity and overhead reduction rate have a trade-off relationship, so the threshold value should be adjusted to suit the use case. Furthermore, system performance, including the overhead penalty of the CSI feedback, can be evaluated by defining the durations of the data and overhead frames. For example, assuming that the durations of data and overhead frames are the same, we found that the system performance of the proposed scheme with threshold optimization is about 30% higher than that of the conventional scheme using random antenna allocation.  
Figure 5: Cumulative distribution function (CDF) of the total channel capacities of the coordinated transmitter-receiver (Tx-Rx) BD technique, MU-MIMO-OFDM transmission with optimum/random antenna allocations, and single user MIMO (SU-MIMO) transmission on the time division multiplexing access (TDMA).  
Figure 7 plots the CDF of the total channel capacities for the proposal using three \(\rho_{th}\) values, (0.5, 0.7, and 0.9), and random/optimum antenna allocations. Optimum means to use the antenna that offers the maximum SINR among all antenna combinations. This figure showed that the proposed scheme offers a higher channel capacity than random antenna allocation. Moreover, the offsets from the channel capacity with optimum antenna allocation are less than 20% at a CDF value of 50%.
## 2 Literature Review
### Feature Selection and Feature Importance  
It is obvious in the literature that a wide range of ML and DL models are used for PV power prediction in single and hybrid models. Among these models, the LSTM model has shown superior performance. However, in this context, managing the data, especially the feature selection process, is as critical a factor as model selection in increasing the accuracy of the prediction models [75]. Feature selection enhances the accuracy and efficiency of the model by identifying the most significant variables within the dataset. This process enables more reliable predictions by reducing the data size, eliminating unnecessary or repetitive information, and enhancing the model's interpretability. An effective feature selection method reduces computational time and improves overall performance by allowing the model to focus only on the most important variables [76].  
In classification and regression tasks, feature importance is carefully considered. This process involves evaluating the impact of each feature on the target variable. One commonly used method to determine feature importance is to remove a feature from the dataset and analyze how the model's prediction error changes. This process is repeated for each feature separately, allowing the system to understand which variables are more informative in predicting the target value. As a result of this analysis, unimportant features can be dropped, and only the most critical variables can be selected, thus reducing system complexity and optimizing computational time. In the final stage, the impact of the features determined by the cross-validation method on the model accuracy is evaluated. This step is critical in terms of increasing the prediction model's reliability and strengthening the model'sgeneralization ability by eliminating unnecessary variables. This comprehensive analysis allows for selecting only the parameters that significantly contribute to the prediction performance and provides an effective strategy for increasing model accuracy [77,78].
## 3 Legal Challenges Posed by Mega-Constellations to Existing International Space Law Regime
### The Issue of Space Traffic Management
#### 3.3.3 Flags of Convenience under the Space Traffic Management Issue
This may in turn devalue patent protections, especially if space actors opt to register in countries where patents are not filed. Projections indicated that flags of convenience would not be an immediate problem [67]; however, with the current technological trends, it can be stated that space companies do in fact have the freedom to establish themselves and launch spacecraft from any country on Earth. This is clearly a major challenge because it is rendering the outer space patent system ineffective in protecting outer space inventions. The dangers of flags of convenience have been explored in regard to maritime activities; however, it is also imperative that the dangers that they pose to outer space are addressed. On 20 March 2012, the head of the US Federal Aviation Administration forecast that space tourism would become a one-billion-dollar industry within the next ten years. We are clearly within the ten-year prediction, and a 2019 report estimated space tourism with both suborbital and orbital levels at a potential market value of three billion dollars by 2030. Therefore, even if [PERSON] initially believed that the flags of convenience would not be a problem due to the infancy of the space industry [67], state shipping registries frequently lack the resources or will to monitor the safety and conditions of ships. Similar outcomes may occur in space, since the rapid development of space technologies and the resulting commercial space industry could create a competitive environment that poses a threat to both the environment and space tourists [74], such as space debris and environmental degradation. Space debris can potentially increase in the Earth's orbit if the flags of convenience states do not follow the Space Debris Mitigation Guidelines, such as limiting spent rocket break-ups, deorbiting vessels at the end of their useful lives, and moving them to graveyards in orbit [75]. With space travel becoming a common occurrence, debris will increase, including operational and fragmented debris, which is, of course, a danger to other users. As it accumulates, the probability of inter-debris collisions will greatly increase, increasing the likelihood of the Kessler syndrome [76] as inter-debris collisions will be uncontrollable once they start, encasing the Earth in an impenetrable cloud of broken debris and making space travel all but impossible [77; 78]. Therefore, because the right to take part in space activities, whether governmental or nongovernmental, is provided for by the Outer Space Treaty [9], all states, including flags of convenience, can enjoy this right. Now, with the popularity of mega-constellations, it is expected that flags of convenience will become a major trend, especially in developing countries, which feel that they are missing out on space resources due to their inability to take part.
## 11 Exploring Hybrid Approaches: Integrating Diverse Techniques
### Exploring Hybrid Strategies for Internal and External Artifact Removal: Case Studies Insights  
Ref. [140] describes an innovative two-step method for removing artifacts from EEG signals, using DWT, CCA, and an anomaly detection algorithm. This approach is effective against various types of artifacts, with particularly good results for ocular ones, while it is less effective for muscular artifacts due to their wide frequency distribution. Removal of power line artifacts improves as the noise intensity increases. The method demonstrates excellent performance at low SNR, maintaining the original EEG information. The analyses confirm the effectiveness of the method, especially through comparisons with other techniques, underlining its potential for different EEG applications, thanks to its ability to adapt to data and experimental conditions. Ref. [141] discusses the use of the Artifact Subspace Reconstruction (ASR) method to automatically clean EEG signals of artifacts, underlining the importance of this technique for improving data reliability in contexts such as brain-computer interfaces and clinical monitoring. ASR proves effective in removing artifacts caused by eye and muscle movements, among others, while ensuring the preservation of neural components essential for accurate analyses, while the paper highlights the potential of ASR in real-time applications and the need for parameter optimization to maximize effectiveness, specific quantitative details illustrating the degree of improvement or efficiency of the method are lacking. Ref. [142] presents an automated framework based on the use of two-dimensional Convolutional Neural Networks (CNNs) for the recognition and removal of artifacts from EEG signals, represented in the scalp topographies of the Independent Components (ICs). This method stands out for its ability to accurately identify and classify various types of artifacts, including those due to eye movements, muscle and cardiac activity, as well as general electrical interference. The proposed strategy significantly improves the reliability and performance of EEG-based brain-computer interfaces (BCIs), providing a fast, accurate, and scalable system for cleaning EEG signals from both external and internal artifacts. Thanks to its optimized architecture and ease of training, the framework is perfectly suited for use in online BCI contexts, where a fast processing response is crucial. This innovative solution promises a dramatic improvement in the quality of interpretation of brain signals, facilitating the application of BCI technologies in various practical and clinical scenarios.
# Resistance of Concretes to External Chlorides in the Presence and Absence of Sulfphates: A Review
## 2 Resistance of Concrete to External Chlorides
### Chloride Binding Isotherms
#### 2.4.2 Non-Linear Chloride Binding Isotherm  
Due to the limitations and inaccuracies of the linear isotherm (as seen in Figure 5), most researchers now use non-linear chloride binding isotherms. The two most commonly used non-linear isotherms are the Freundlich and Langmuir isotherms. These isotherms were originally derived to describe gas adsorption on solid surfaces, where the Langmuir isotherm assumed monolayer coverage on a homogeneous surface and the Freundlich isotherm assumed multilayer coverage. In solid-solution systems, the situation is much more complicated and the underlying assumptions may no longer be valid. Therefore, the isotherms should be considered merely as empirical models and may be expressed in the forms given below:Freundlich isotherm:  
\[C_{b}=\alpha\cdot C_{f}^{\beta} \tag{3}\]  
Langmuir isotherm:  
\[C_{b}=\frac{\alpha\cdot C_{f}}{(1+\beta\cdot C_{f})} \tag{4}\]  
where \(\alpha\) and \(\beta\) are adsorption constants, which vary for different cement types. These constants do not have any physical meaning as they are not material properties but can be used to give an indication of the chloride binding capacities of the cementitious materials [13; 40].  
The Freundlich isotherm is more applicable at free chloride concentrations higher than 0.01 mol/L, while the Langmuir isotherm is more suitable for free chloride concentrations lower than 0.05 mol/L [59].  
The third type of non-linear chloride binding isotherm, the BET isotherm, is usually used in gas adsorption. However, [PERSON] [117] used it to model the relationship between free and bound chlorides, finding a good correlation when the free chloride concentrations were less than 1 mol/L. A modified form of this model (shown in Equation (5)) was also used by [PERSON] [118] (cited in [53]) to describe chloride binding.  
\[\frac{c_{b}}{c_{bm}}=\frac{\alpha\frac{c}{c_{s}}\Big{[}1-(1-\beta)\Big{(}1- \beta\frac{c}{c_{s}}\Big{)}^{2}\Big{]}}{\beta\Big{(}1-\beta\frac{c}{c_{s}} \Big{)}\Big{(}1-\beta\frac{c}{c_{s}}+\alpha\frac{c}{c_{s}}\Big{(}1-\beta\frac {c}{c_{s}}+\frac{c}{c_{s}}\Big{)}\Big{)}} \tag{5}\]
# Digital Mapping of Soil Organic Matter in Northern Iraq: Machine Learning Approach
## 3 Methodology  
The proposed method comprises three main stages, as illustrated in Figure 2. These stages include data collection, feature extraction, and model training.  
\begin{table}
\begin{tabular}{l c c} \hline
**Explanatory Variable** & **Formula** & **Reference** \\ \hline \multicolumn{3}{c}{Shuttle Radar Topography Mission} \\  & & (SRTM) from \\ \multicolumn{3}{c}{Digital Elevation Models (DEM)} \\  & & ([[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)), \\  & & accessed on 20 August 2021) \\ \hline \multirow{8}{*}{**Landsat 8 (OLI)**} & b 1: Coastal aerosol, \\  & b 2: Blue, \\  & b 3: Green, \\  & b 4: Red, \\  & b 5: NIR, \\  & b 6: SWIR 1, \\  & b 7: SWIR 2, \\  & b 10: TIRS 1, \\  & b 11: TIRS 2 \\ \hline \multicolumn{3}{c}{**Normalized Difference Vegetation Index**} & \(NDVI\ =\ \frac{NIR-Rad}{NIR+Rad}\) [31] \\ \hline \multicolumn{3}{c}{**Soil-Adjusted Vegetation Index**} & \(SAVI\ =\ \frac{(NIR-Rad)}{(NIR+Rad+L)}(1+L)\), \\  & \(L\ =\ 0.5\) & [27] \\ \hline \multicolumn{3}{c}{**Brightness Index**} & \(BI\ =\ \frac{1}{2}\times\sqrt{Rad^{2}+Green^{2}}\) & [15] \\ \hline \end{tabular}
\end{table}
Table 1: The 13 variables selected for SOM mapping.  
Figure 2: Flowchart showing study methodology.
## 4 Experiments and Analysis of Results
### Data Preprocessing  
The vibration signals of rolling bearings are inherently nonlinear and nonsmooth, and the signals collected by sensors often include random noise and shock interference. As a result, direct feature extraction and sample generation from one-dimensional vibration signals tend to be less effective. However, when these signals are transformed into images, the extracted patterns and features more intuitively reflect the dynamic changes in the signals. To fully leverage the limited vibration signal data, RGB images are generated by fusing time-domain, frequency-domain, and time-frequency domain information. Figure 5 illustrates the corresponding images for the ten categories of CWRU bearing states across the time domain (TD), frequency domain (FRD), time-frequency domain (TFD), and fusion domain (FD).  
The time-domain transformation retains the temporal characteristics of the original signal, while the time-frequency domain transformation captures the dynamic variations within the signals. By employing multi-domain feature fusion, the model effectively captures the nonlinear and nonsmooth characteristics associated with complex mechanical failures, enabling improved feature extraction and fault analysis.  
To ensure consistent visual representation, all generated RGB images were constructed by normalizing each of the time-domain, frequency-domain, and time-frequency domain channels to the [0, 255] range. These normalized values were rendered using the standard RGB colormap without additional contrast enhancement or nonlinear remapping in order to faithfully preserve the structural properties of the signal transformations. All subfigures  
Figure 5: Multi-domain RGB image fusion.  
use consistent image dimensions and interpolation settings to support fair cross-model visual comparison.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c} \hline
**Environment** & **Distribution** & **Parameter** & **Estimated Value** \\ \hline \multirow{6}{*}{Urban} & \multirow{2}{*}{Lognormal} & \(\mu\) (mean) & \(6.49\) \\  & & \(\sigma\) (standard deviation) & \(0.48\) \\ \cline{2-4}  & & \(\alpha\) (shape alpha) & \(4.19\) \\  & & \(\theta\) (scale thita) & \(178.5\) (deg) \\ \cline{2-4}  & & \(\alpha\) (scale alpha) & \(846.87\) \\  & & \(\beta\) (shape beta) & \(1.77\) \\ \cline{2-4}  & & \(\mu\) (mean) & \(747.90\) \\  & & \(\sigma\) (standard deviation) & \(469.06\) \\ \hline \multirow{6}{*}{Suburban} & \multirow{2}{*}{Lognormal} & \(\mu\) (mean) & \(6.37\) \\  & & \(\sigma\) (standard deviation) & \(0.37\) \\ \cline{1-1} \cline{2-4}  & & \(\alpha\) (shape alpha) & \(6.37\) \\ \cline{1-1}  & & \(\theta\) (scale thita) & \(99.79\) (deg) \\ \cline{1-1} \cline{2-4}  & & \(\mu\) (mean) & \(635.22\) \\ \cline{1-1}  & & \(\sigma\) (standard deviation) & \(370.55\) \\ \hline \end{tabular}
\end{table} 
Table 3: Estimated parameters for the distributions that approximate the E-field measurements in urban and suburban environments. Box-and-whisker plots for the aforementioned worst-case scenario are presented in Figure 6. A comparison between Figures 2a and 6a (or Figures 2b and 6b) indicates that the whole range of values in Figure 6 is narrower, i.e., the whiskers are shorter in Figure 6 than in Figure 2, albeit the interquartile range is slightly wider in Figure 6 than in Figure 2. This is a reasonable outcome, as the plots of Figure 2 comprise all measurements that, evidently, span a broader range than the highest values (one for each school) that constitute Figure 6. Moreover, the familiar remark, derived from Figure 2, that the box for the suburban environments is narrower than the one that corresponds to the urban areas, may be verified from Figure 6, too.