esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### State-of-the-Art Satellite-Derived GPP Estimates  
As benchmarks for comparison, we use two state-of-the-art data-driven global GPP estimates. FLUXCOM-RS is based on the global upscaling of flux tower data using satellite remotely-sensed (RS) data (no meteorological data) with a machine learning approach [6; 7; 10]. The product is generated at 0.0833\({}^{\circ}\)\(\times\) 0.0833\({}^{\circ}\) spatial and 8-day temporal resolution. It uses flux tower GPP with an ensemble of machine learning-based models and over 200 explanatory variables.  
The Vegetation Photosynthesis Model (VPM) V20 [15; 21; 69; 70; 71] is based on the LUE approach and utilizes many remotely-sensed and climate reanalysis datasets. Different from the MODIS MOD17 GPP product, it uses a linear transformation of the Enhanced Vegetation Index (EVI) as a proxy of FAPAR\({}_{\rm chl}\) (i.e., FAPAR\({}_{\rm chl}\)=1.25 [EVI-0.1]); the maximum LUE only differs for C3 and C4 plants, which is supported by a convergence of LUE when using this FAPAR\({}_{\rm chl}\)[72]. VPM provides data at various resolutions. Here, we use monthly gridded data at 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\) resolution.
## 1 Introduction
[PERSON] [14] applied the Firefly Algorithm (FA) and Fuzzy C-means (FCM) algorithm for semi-automated clustering analysis of rock mass structural planes based on normal vectors. [PERSON] [15] proposed a new method for identifying discontinuous sets of rock masses based on fast search and density peak identification and validated the accuracy of the method using regular geometric datasets. [PERSON], [PERSON], and [PERSON] et al. [16; 17; 18] improved the traditional K-means algorithm using variable-length string genetic algorithms, maximum sample density, and the Artificial Fish Swarm Algorithm (AFSA), achieving clustering analysis of rock mass structural plane orientations. [PERSON] et al. [19] used the fast search and density peak identification (CFSFDP) algorithm for clustering analysis of structural planes and used [PERSON]'s K-index to filter out noise points, thus improving the accuracy of the clustering centers. In summary, many scholars at home and abroad have long been dedicated to the automatic extraction of rock mass structural plane information from 3D point cloud data. Since 3D point cloud data is inherently discrete and lacks topological relationships, data mining methods are needed to extract characteristic values that represent rock mass structural planes, with the accurate calculation of normal vectors being particularly crucial [20; 21; 22; 23]. Factors such as noise, point density, and the complexity of the rock mass surface can affect the accuracy of normal vector calculations. Therefore, this paper adopts the Hough space transform method based on statistical principles to accurately calculate the normal vectors of 3D point clouds and uses the differences in normal vectors between adjacent points for region-growing segmentation, achieving the automatic identification and information extraction of rock mass structural planes. The extraction of dominant orientations of structural planes plays an irreplaceable role in the stability analysis of rock masses. Dominant structural planes determine the sliding direction and failure modes of rock masses under different stress conditions. Accurate extraction of dominant orientations not only provides key input parameters for rock mass stability evaluation but also offers scientific guidance for support design and optimization of construction plans [24; 25]. Existing clustering algorithms for rock mass structural planes are numerous but generally rely on trial and error and experience to determine the clustering centers and the number of clusters, making the results susceptible to subjective influences and limiting the amount of input data. In this paper, the sine squared value of the normal vectors is used as a similarity measure, and the fast search and density peak clustering method (CFSFDP) is applied for clustering analysis. This method automatically determines the clustering centers and numbers, avoiding subjective parameter settings, thereby improving the efficiency and accuracy of clustering and ensuring the precise extraction of the orientation of dominant structural planes [26; 27; 28].
# Towards Sustainable Water Use in Two University Student Residences: A Case Study
## 2 Materials and Methods
### Water Consumption Characterization  
The flushing cisterns in Residence I are dual-flush (6 L/3 L), and the urinals have a flush volume of approximately 2 L. In Residence II, the flushing cisterns are dual-flush (4.5/3 L). The water consumption for these devices was calculated using the following equation:  
\[Cw=\Sigma(Vi\times fi) \tag{1}\]  
\(i\): each of the existing devices;  
\(V\): flushing volume (L);  
\(f\): daily frequence of use.  
To measure the water consumption of timer taps, a 1 L calibrated beaker was used while the taps were on. The flow rates of washbasins and kitchen taps were determined by recording the time it took to fill a 1 L calibrated beaker. Shower flow rates were determined by recording the time it took to fill a 10 L capacity bucket to a certain volume, \"X\", and then obtaining the final volume with a calibrated beaker. All measurements were repeated three times with the devices in the position of use.  
The water consumption for these devices was calculated using the following equation:  
\[Cw=\Sigma(Qi\times fi\times ti) \tag{2}\]  
\(i\): each of the existing devices;  
\(Q\): water flow (L);  
\(f\): daily frequence of use;  
\(t\): duration of each use (minutes).  
According to the manufacturer's specifications, the washing machines in both residences were estimated to use an average of 45 L of water per wash. In both residences, the water heating systems utilize a heat pump with a coefficient of performance (COP) of 2.97.  
Surveys (Appendix A) were conducted to discern water use behavior and patterns of both the users and the cleaning staff. Due to the small number of users (6 in each residence), all were surveyed in person in their respective residence. Additionally, to minimize biases,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Water Devices** & **Residence I** & **Residence II** \\ \hline Washbasin timer taps & 2 & 2 \\ Washbasin mixer taps & 10 & 16 \\ Kitchen taps & 1 & 1 \\ Flushing cisterns with double discharge & 10 & 12 \\ Ceiling mount shower system & 8 & 16 \\ Urinals & 1 & 1 \\ Washing machine & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantity and types of water devices in each residence.  
each resident was asked to simulate their usual water usage by using the flushing cisterns and opening taps and showers as they normally would. The data collected enabled to estimating the average daily frequency of use for all devices, as well as the average daily duration of each use for taps and showers. The cleaning staff were asked to fill the bucket as usual when cleaning the building. The volume of water was measured using a 1 L graduated beaker.
# Unlocking the Potential: How Flying Taxis Will Shape the Future of Transportation
## 2 Regulations from Government
### Regulatory Requirements for Flying Taxis  
The regulatory requirements for flying taxis can vary depending on the country or region, but some potential considerations that a government should keep in mind are represented in Figure 3.  
Just like with other aircraft, flying taxis will need to meet certification standards set by aviation authorities to ensure they are safe for public use [29]. Flying taxis will need licensed pilots who are trained and qualified to operate them safely [30]. Flying taxis will need to follow established airspace regulations and protocols to ensure safe navigation and avoid collisions with other aircraft [31]. As flying taxis will operate in urban or residential areas, they will need to meet noise regulations to prevent excessive noise pollution [32]. Governments will have to require that flying taxis have certain types and levels of insurance coverage to protect passengers and the public in case of accidents [33]. Flying taxis will need to be regularly maintained and inspected to ensure they remain safe and operational [7]. Governments will have to require that flying taxi operators have emergency response plans and procedures in place to handle accidents or other incidents [34]. Flying taxis will collect and transmit data, so governments need to establish regulations to protect the privacy  
Figure 3: Regulatory requirements.  
Figure 2: Policies to be considered by the government.  
and security of that data [35]. Flying taxis will have an impact on the environment, so governments need to consider regulations related to emissions and other environmental concerns [32]. These are just some potential regulatory requirements that a government will need to consider when it comes to flying taxis. It is likely that new regulations will need to be developed specifically for this emerging technology.
# Application of Neural Networks in Distribution of the Load in Cluster-Based Web Systems
## 5 Design of Executor Model
### Multilayer Networks  
The structure of neural networks tested thus far was the basic structure (one neuron, no hidden layers). The next step in the research was to expand the network by one or two hidden layers. The complexity of the neural network should be adapted to the complexity of the problem to be solved. Too simple and too complex structures will yield worse results. The structures of the networks studied are shown in Figure 12.  
Figure 11: Comparison of the results for modified neural networks.  
Figure 10: Diagrams of modified neural networks: (**a**) before modification—four inputs; (**b**) on the fifth input—the estimated time of the last request; (**c**) on the fifth input—the time of the last request; (**d**) on the fifth input—the difference between the response time of the last request and its estimated value.  
Each of the tested networks had five inputs in the same configuration as the best solution from the previous study \(NN\). The inputs are as follows:  
1. the number of static tasks currently serviced on the server;
2. the number of dynamic tasks currently serviced on the server;
3. is the current task dynamic (value 1--dynamic task, 0--static);
4. is the current task static (value 1--static task, 0--dynamic);
5. value of the last predicted request response time.  
The networks differed in the presence of neurons in hidden layers. The version without hidden layers (earlier solution), 1, 2, 3, and 4 neurons in one hidden layer (Figure 9)  
Figure 12: Diagrams of modified neural networks: (**a**) before modification—1 neuron, no hidden layers; (**b**) 1 hidden layer containing 1 neuron; (**c**) 1 hidden layer containing 2 neurons; (**d**) 1 hidden layer containing 3 neurons; (**e**) 1 hidden layer containing 4 neurons; (**f**) 2 hidden layers, each with 2 neurons.  
and two neurons in two hidden layers (Figure 9) were used. Networks have a linear activation function.  
The results obtained from the experiments conducted for neural networks are presented in Figure 13. The chart shows that the best results for this task were obtained by neural networks with a simple structure (marked as NN in the graph), without hidden layers.
# A Modified Bio-Inspired Optimizer with Capsule Network for Diagnosis of Alzheimer Disease
## 3 Methodology
### Feature Extraction  
In the segmented sMRI images, features are captured by integrating the ResNet-50 model [39], VGG-16 model [40], and PLTP descriptor [41]. ResNet-50 and VGG-16 are robust to changes in MRI scan procedures and settings, whereas PLTP is robust to noise and artifacts in MRI scans. Furthermore, high-level features that are affected in Alzheimer's disease, such as objects and forms, can be extracted using ResNet-50, and VGG-16. PLTP, ResNet-50, and VGG-16 are used together to monitor the development of the disease and identify small variations over time. Utilizing the hybrid PLTP, ResNet-50, and VGG-16 guarantees a thorough and reliable method of feature extraction for Alzheimer's disease diagnosis and monitoring. A clear description of these extraction models is provided below.  
The ResNet-50 is a 50-layer CNN model, which incorporates one average pooling layer, one max-pooling layer, and 48 convolutional layers. This residual neural network is an efficient artificial neural network, which creates networks by stacking numerous residual blocks. On the other hand, the VGG-16 model comprises convolutional layers, which  
Figure 3: Segmented hippocampus portion from the sMRIs: (**a**) ADNI dataset, (**b**) real-time dataset, and (**c**) AIBL dataset.  
are followed by fully connected and dense layers. In the context of image classification, pre-trained models such as ResNet-50 and VGG-16 serve as powerful feature extractions. The initial layers of the pre-trained models extract general features such as shapes, textures, and edges. Then, the deeper layers extract more complex and abstract features. These pre-trained models (ResNet-50 and VGG-16) are beneficial, particularly when the undertaken sMRI dataset is large.  
Furthermore, the LTP is a texture descriptor, which is robust to noise and achieves valuable relationships between the centre pixels and their neighbours. Three RoIs are defined in the ADNI and real-time datasets (NC, AD, and MCI) and the AIBL dataset (NC, sMCI, and pMCI). The LTP descriptor has three codes (1, 0, \(-\)1) to capture tumour patterns to segment three RoIs. In this study, the LTP descriptor is updated on the basis of the probability density value (PDV), especially as it relates to the histogram bins. In the PLTP descriptor, upper and lower patterns are utilized for comparing the PDV of every bin with the neighbours. In this phase, this hybrid feature extraction (ResNet-50, VGG-16, and PLTP descriptor) extracts 12,650, 11,980, and 14,548 features from the ADNI, real time, and AIBL datasets, respectively. These obtained features are further passed into the MGTO algorithm for selecting informative features to enhance classification performance and reduce overall processing time.
# MicroForest: Lightweight Bottleneck Prediction for Manufacturing Processes on Edge Devices
## 5 Experimental Results
### Discussion  
In summary, the proposed MicroForest demonstrates three key advantages for bottleneck prediction: First, it consistently achieves high recall. MicroForest effectively detects true bottlenecks across varying task counts and network complexities, whereas RF and DCT suffer from noticeable drops in recall as complexity grows even though they exhibit higher precision. Second, MicroForest's model size is orders of magnitude smaller than that of competing methods (RF, LGBM, DCT). Although SSF yields the smallest footprint, its extreme simplicity comes at the cost of substantially lower accuracy. Third, MicroForest delivers efficient inference, scaling far better than the baselines as task count increases.  
Overall, MicroForest strikes a balanced trade-off between accuracy, compactness, and speed, making it a superior approach for bottleneck prediction under resource-constrained conditions.  
Despite its promising performance, this study has two main limitations. First, evaluations rely on a synthetic DAG-based simulation and thus do not account for real-world issues such as missing sensor readings or dynamic fluctuations in task cycle times. Second, when compressing multiple tasks into a single MicroTree, we currently apply a simple heuristic to group tasks and order splits; however, deciding the optimal grouping of tasks and the sequence of split rules is known to be NP-hard. Developing efficient algorithms for task grouping and split-ordering could be important future work.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **P** & **R** & **F1** & **Size (MB)** & **Latency (ms)** \\ \hline
1-RF & 0.70 & 0.85 & 0.76 & 3.91 & 24.74 \\ LGBM & 0.75 & **0.86** & **0.80** & 1.65 & 3.72 \\ DCT & 0.70 & 0.82 & 0.76 & 0.20 & 2.16 \\ SSF & 0.71 & 0.78 & 0.74 & 0.03 & 2.01 \\
**MT (Ours)** & **0.76** & 0.62 & 0.68 & **0.003** & **0.01** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison on a real-world data: APS failure at Scania trucks [24]. Bold indicates best performance.
# A Case Study on User Evaluation of Scientific Publication Summarization by Japanese Students
## 6 Discussion
### Evaluation Metric and Request
#### 6.1.1 Paper Components and Content Richness  
Concerning content richness, as shown in Table 6, the subjects commented on whether there are important components of scientific papers (e.g., topic and method) in the summaries. For example, some subjects' answers are about papers' components, like, for example, \"the problem of prior work and the purpose of research are easy to understand\" (by AmM05) and \"the purpose and the proposed method are not clear\" (by CmM16). In general, scientific publications have a common structure consisting of multiple sections, including a background, experimental setup, and results ([PERSON] et al., 2018), so most readers (students) will briefly evaluate and confirm whether these structures appear in the summary of a paper. However, such components in summaries have not been studied in previous text summarization research because the focus therein has tended to be on \"news\" articles, which do not have any common structure. In addition, to evaluate the appropriateness of the information in a summary, [PERSON] and [PERSON] (2018) proposed a pyramid method to place importance on the information mentioned in more reference summaries, but this method does not consider any structural components such as those in scientific papers.  
In experiment 2, the subjects recorded whether they would like to read the original papers after reading their summaries. We observed that subjects mainly made this judgment based on either \"interest\" or \"quality and comprehension.\" In the answers about \"interest,\" 62.6% and 17.4% were about research topic and method, respectively, so it appears that subjects tend to regard these components as most important. Note that 3% of the answers mentioned multiple components of research articles. This indicates that subjects paid attention to a limited number of components. [PERSON] and [PERSON] (2018); [PERSON] and [PERSON] (2018) proposed summarization methods to equally select some sentences from each component (e.g., methods and results) based on the structure of scientific publication. While these systems balance the amount of each component, it can be said that they do not consider the importance of each component.  
Moreover, the Master's students tend to have less positive attitudes in terms of interest. One possible reason is that upper-grade students have already obtained specific knowledge about their research fields, which could result in more attention being paid to the quality or comprehension of summaries. Thus, the importance of each component depends on readers' attributes (e.g., background of research topic). From the above, we can infer that considering readers' preferences for components are important to improve the qualities of scientific research paper summarizations.
## 2 Hydration Model of Cement-Eggshell Powder Binary Blends
### Summary of Eggshell Powder Hybrid Hydration Model  
The hydration model proposed in this paper considers the hydration reaction of cement, the dilution effect of eggshell powder, and the nucleation effect of eggshell powder. The input parameters of the model include the parameters B, C, kr, and \({\rm De_{0}}\) (shown in Equation (1)) of the cement hydration model and the nucleation parameter nu (shown in Equation (2)) of eggshell powder. These parameters can be obtained through calibration of experimental results. Furthermore, based on the hydration degree calculated by the eggshell powder hybrid hydration model, the property development of hybrid concrete can be predicted.
## 5 Indoor and Outdoor Application Testing
The signal spectrum was of a Rician fading channel, rendering the proposed antenna scheme suitable for outdoor digital terrestrial television reception [34]. Figure 20: Distance between the transmitting and receiving antennas without obstructions (line of sight). Figure 23 demonstrates the indoor reception performance of the proposed antenna scheme at 514 MHz. The power obtained was 26.6 dB\(\mu\)V (\(-\)82.2 dBm) with a C/N of 10.9 dB. The signal spectrum was of a Rayleigh fading channel, rendering the proposed antenna scheme suitable for indoor digital terrestrial television reception [36]. Figure 24 shows the experimental setup used to assess the reception performance of the proposed antenna scheme in actual use, whereby the AUT was connected by a 75 \(\Omega\) RG6 coaxial cable to the set top box and a television set. The results indicate that the  
Figure 21: The portable wideband directional antenna scheme with a DVB signal receiver. Figure 22: The outdoor reception performance of the portable wideband directional antenna scheme. proposed antenna scheme is capable of receiving the transmitted signals from all television channels, as evidenced by high-quality images and sound.
## 3 Methodology
### Pretraining Phase  
#### 3.2.1 Data Augmentation  
Data augmentation is one of the key components in SSL for SEI. It plays a crucial role in constructing positive and negative sample pairs for contrastive learning. To ensure that the extracted RFFs remain invariant to signal content, we design three effective augmentation methods tailored for space-specific emitter signals: Shift, Noise, and Scale. These augmentations introduce diversity while preserving emitter-specific features, thus enabling the model to learn discriminative and robust representations.  
Shift: Since RFFs are typically time-invariant, the proposed shift method enhances data diversity by altering the temporal alignment of signals while preserving the underlying RFFs. This helps the model learn more robust and generalizable representations. Unlike general (non-circular) shifts that may lose important parts of the signal, circular shifts retain the complete content, making them particularly suitable for SEI tasks. The proposed shift method circularly shifts the signal by a random offset \(\Delta t\) samples,  
\[\tilde{\mathbf{x}}_{\text{shift}}[n]=\mathbf{x}[(n+\Delta t)\mod L] \tag{4}\]  
where \(\Delta t\) is sampled from a uniform distribution, \(\Delta t\sim\mathcal{U}(-100,100)\), and mod denotes the modulo operation, which ensures circular wrapping of indices, thereby preserving the full signal content and avoiding index overflow or truncation.  
Noise: To simulate real-world signal impairments such as thermal noise or channel interference, we inject zero-mean Gaussian noise with adaptive intensity, encouraging the model to learn noise-robust features.  
\[\tilde{\mathbf{x}}_{\text{noise}}=\mathbf{x}+\epsilon \tag{5}\]  
where \(\epsilon\) is a Gaussian white noise sequence sampled from \(\mathcal{N}(0,\sigma^{2}I)\), with standard deviation \(\sigma=0.05\), corresponding to a 5% perturbation of the signal amplitude.  
Scale: To simulate realistic power variations in satellite communication systems caused by transmission power adjustments or dynamic range differences among receivers, we apply randomized linear scaling to the input signals:  
\[\tilde{\mathbf{x}}_{\text{scale}}=\alpha\mathbf{x} \tag{6}\]  
where \(\alpha\) is sampled from a uniform distribution, \(\alpha\sim\mathcal{U}(0.8,1.2)\).