esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
At present, when addressing the problem of fault stability in mines, most attention is paid to the study of the impact of a single mining method on faults. In order to study fault stability in mines more comprehensively, this paper researches the effect of coordinated mining using multiple mining methods. For example, the sublevel caving method without the sill pillar and the lower-layer filling method of coordinated mining can be used to analyze the dynamic response law of the fault, as well as the stability of the fault in different mining conditions. In this paper, the stress field, displacement field and sliding trend index of the fault plane and orebody near the fault are obtained via numerical simulation and theoretical analysis methods, and the main factors affecting fault stability under different mining conditions are analyzed. The results show that under the influence of cooperative mining, the shear stress fluctuation of the fault surface and the ore body near the fault increase gradually with the sublevel height of the sublevel caving method without the sill pillar and the lower-layer filling method, and the indexes of slip tendency become larger, which may be a precursor of fault activation. In particular, the fault surface of the sublevel caving method without the sill pillar reflects the drastic change in the shear stress with the larger displacement, and the instability brought to the fault by the sublevel caving method without the sill pillar is greater than that brought to the fault by the lower layer filling method under the premise of only changing the height of the sublevel.  
coordinated mining; fault dynamic response; lower-layer filling method; mining impact; sublevel caving method without the sill pillar; sublevel height +
Footnote †: journal: Applied sciences
# Safety-Aware Optimal Attitude Pointing for Low-Thrust Satellites
## 2 Methods
### Choosing a New Optimal Path Using Reinforcement Learning  
After the initial guess \(c_{i}\), the values \(c_{i}^{0}\) were amended to the obstacle-avoidance values chosen by RL (which we denote by \(c_{i}^{rl}\), which as we noted in the previous section was determined by \(c_{i}^{rl}=c_{i}^{0}+\text{action}\)), and we constructed a new trajectory that avoided the obstacle. We followed Steps (1)-(3)with the following amendments in order to construct the final trajectory:  
* Step (1a): Determine the solution \(R(1,\lambda_{0},c_{i}^{rl})\) of (1) that minimizes the cost (10) using (15) in terms of the unknown boundary conditions \(\lambda_{0}\) on the imaginary time domain \([0,1]\).
* Step (2a): Construct the \"error matrix\" \(\widetilde{R}(1,\lambda_{0},c_{i}^{rl})=R(1,\lambda_{0},c_{i}^{rl})-R_{T_{j}}\) and the function: \[S(\lambda_{0})=\|\widetilde{R}(1,\lambda_{0},c_{i}^{rl})\|.\] (21)
* Step (3a): Find \(\lambda_{0}\) that minimizes \(S(\lambda_{0})\) using a local minimizer.
# A Feature Selection Algorithm Performance Metric for Comparative Analysis
## 7 Results
Table 11 reports the results for using different values for the classification and penalty scaling constants. The same process was followed to produce the algorithm ranks in Table 11 as for Table 10. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **AMSO** & **GAFS** & **SBFS** & **SFFS** & **PCFS** & **IGFS** \\ \hline D1 & 2 & 1 & 2 & 1 & 4 & 3 \\ D2 & 1 & 1 & 1 & 1 & 1 & 1 \\ D3 & 2 & 3 & 5 & 1 & 4 & 3 \\ D4 & 2 & 1 & 3 & 6 & 5 & 4 \\ D5 & 3 & 1 & 1 & 2 & 5 & 4 \\ D6 & 2 & 1 & 3 & 1 & 4 & 4 \\ D7 & 3 & 1 & 1 & 2 & 4 & 5 \\ D8 & 1 & 1 & 1 & 1 & 1 & 1 \\ D9 & 1 & 2 & 1 & 1 & 1 & 1 \\ D10 & 2 & 1 & 4 & 2 & 3 & 5 \\ D11 & 1 & 1 & 1 & 1 & 1 & 1 \\ D12 & 2 & 1 & 1 & 4 & 3 & 5 \\ D13 & 4 & 1 & 3 & 2 & 4 & 1 \\ D14 & 1 & 1 & 1 & 1 & 2 & 3 \\ D15 & 1 & 2 & 1 & 2 & 3 & 3 \\ D16 & 1 & 2 & 1 & 1 & 1 & 1 \\ D17 & 2 & 3 & 5 & 1 & 4 & 6 \\ D18 & 1 & 1 & 2 & 1 & 1 & 1 \\ D19 & 3 & 2 & 4 & 1 & 5 & 5 \\ D20 & 5 & 2 & 4 & 1 & 3 & 6 \\ D21 & 2 & 1 & 1 & 3 & 4 & 5 \\ D22 & 1 & 1 & 1 & 2 & 4 & 3 \\ D23 & 2 & 1 & 4 & 1 & 3 & 5 \\ D24 & 1 & 1 & 2 & 1 & 1 & 1 \\ D25 & 4 & 1 & 3 & 2 & 5 & 4 \\ D26 & 3 & 2 & 6 & 1 & 4 & 5 \\ D27 & 2 & 1 & 1 & 2 & 3 & 4 \\ D28 & 1 & 1 & 5 & 3 & 4 & 2 \\ D29 & 3 & 2 & 6 & 1 & 4 & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Algorithm performance rankings. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Best** & **Worst** \\ \hline \hline (1) \(k_{c}=1.0\), \(k_{p}=0.25\) & & \\ \hline AMSO & 8 (13\%) & 1 (3\%) \\ GAFS & 17 (29\%) & 2 (6\%) \\ SPS & 10 (17\%) & 6 (20\%) \\ SFFS & 14 (24\%) & 1 (3\%) \\ PCFS & 4 (6\%) & 8 (26\%) \\ IGFS & 5 (8\%) & 12 (40\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Best and worst algorithm counts. Figure 3: Box plot of algorithm ranks. Table 11 shows the relative performance of the six algorithms for different values of the objective function scaling constants, with \(k_{p}\) reduced from 1 to 0 and with different values of \(k_{c}\) (as denoted in the greyed rows). Considering the various different scaling constants under observation, it is clear that there is no one algorithm that reigns supreme. Only one combination of scaling constants, \(k_{c}=1.0\), \(k_{p}=0.0\), showed that the GAFS algorithm did not perform the worst for at least one dataset. The performance complementarity for feature selection algorithms in this study shows that choosing the best feature selection algorithm remains problem dependant.
# _remote sensing_
## 1 Introduction
The NASA's TERRA satellite and its Multiangle Imaging SpectroRadiometer (MISR) [30], operating in Low Earth Orbit (LEO) since 1999, can be considered a good example of a large satellite combining off-nadir perspectives combined with multispectral acquisitions. The MISR is provided with nine cameras pointed at fixed angles, one in the nadir direction (local vertical) and the other viewing symmetrically the forward and afterward off-nadir directions along the spacecraft's ground track. The MISR's data acquired in the last years have been used to measure thermal emissions, troposphere pollution, surface/ocean reflections and main features, to characterize clouds and Earth's radiant energy and properties, and to study the atmosphere and aerosol characteristics at global scale [31]. The same features can be reproduced on a smaller payload installed on a nano-satellite platform. This paper describes the features of HORUS, a 6U CubeSat aimed at performing low-resolution multispectral multiangle observations on CMOS sensors. The HORUS Mission aims to use a low-cost and COTS-based configuration of cameras on-board a 6U CubeSat in order to acquire a combination of multispectral and multiangle data of the same ground targets to be used for remote sensing applications related to the surface, ocean and atmosphere monitoring. Section 2 clarifies the mission objectives and the aim of all the nadir-pointing and off-nadir sensors installed on-board, Sections 3 and 4 describe the payload and bus modules design. Section 5 discusses the potentiality and expected results of the HORUS mission.
# A Game Theoretic Approach for Energy Sharing in the Italian Renewable Energy Communities
## 3 Game Theoretic Approach for Value Redistribution  
The model presented in Section 2 is capable to define an overall cash flow, in which costs and revenues are considered as they were monetary flows of a single entity. Nevertheless, considering the nature of the energy communities, it is clear that multiple stakeholders are involved and the interest of each one has to be taken into account. As a matter of fact, an investment that can be theoretically profitable when considering the cumulative profits, can be halted by insufficient returns from individual investors.  
Taking this challenge into consideration, game theory is an effective tool to address the interactive nature of energy sharing, since it provides general mathematical techniques for analysing situations in which two or more individuals make decisions that will influence one another's welfare [20]. The game, that can also be referred as conflict or interaction, describes any situation in which the decision-making processes of the players, i.e., the game participants, are interrelated. The players are said to be rational, as their decisions are guided solely by the desire to complete their own objectives, that are assumed to be the maximization of their expected payoff, mathematically described by an utility function. The players are also said to be intelligent, and it is assumed that any player is aware about the rules of the game and can think of consistent assumptions to make his decisions. The game theory is generally divided into two classes defining the level of constraint of the agreements taken among the players. Cooperative games analyse situations in which commitments are fully binding and enforceable. On the other hand, in non-cooperative (or strategic) games there is no obligation to fulfil such commitments.
# Application of Workflow Technology for Big Data Analysis Service
## 3 Case Study
### Service Recommendation  
Workflow definition: For telecommunication services, customer behavior has a specific mode, which can help operators in service recommendation and proposing new services according to customers' needs. Service recommendation mainly includes two sub-tasks: customer cluster and service recommendation. Figure 13 shows the workflow process of service recommendation.  
* Customer Cluster: Customers are divided into multiple clusters, in which customers having similar behavior characteristics are grouped. The \"Input\" component imports data to the platform. The \"Select\" component selects related features for applications because raw data have many features. The \"K-means\" component divides customers into multiple clusters of similar consumer behavior.
* Service Recommendation: We use a classification method to find the latent relationship between customers' properties and selected services on the basis of the result of customer clustering. In this scenario, the classification labels correspond to service numbers. The \"Input\" component imports data to the platform. The \"Select\" and \"Delete\" components filter features. The service numbers are discrete; thus, we select the \"C45\" component to classify new customers into service types.  
Workflow instance: Analyzing class data requires preprocessing by filtering 39 columns and 300 million rows of raw data into 20 columns including customers' attributes, charge, voice communication (calling, called, local, roaming, long distance, and the corresponding number of calls and time of calls), and message information. In the application, customers can be clustered into six clusters: advanced, advanced call, advanced value-added, midrange call, midrange value-added, and low-end customers. The clusters of customers and the number of services are used to train the classification model, which can predict customers' behavior to recommend suitable services for new coming customers.  
Figure 12: Workflow scheduling.  
Result: The cloud platform involved 86 nodes and each node had four Intel Xeon E5504 CPUs, 8 GB memory, and 1 TB drives. In this application, we employed 29 GB raw data for two months, which involved personal information, customers' behavior, and the services they actually selected. The workflow analyzed 29 GB of raw data in 37 min and 46 s and obtained 89% accuracy compared with the actual result. The service recommendation model was applied to the operator's business. Practical application demonstrates that the proposed workflow has certain practical value and can help enterprises to improve their business experiences.
# Influence of Social Overhead Capital Facilities on Housing Prices Using Machine Learning
## 3 The Proposed Scheme  
### Raw Data  
In this study, we aim to construct a model to predict housing prices in areas in close proximity to subway systems, a significant component of living SOC facilities. We utilized the Metro-Adjacent Residential Transaction data, provided by the Korea Real Estate Board and available on the National Transportation Data Open Market, as our primary input variables. This dataset, shown in Table 1, comprises actual housing transaction data extracted from the Ministry of Land, Infrastructure, and Transport's real estate transaction disclosure system, specifically, transactions occurring within 500 m of subway stations. Based on this dataset, we incorporated additional data on SOC facilities and life convenience facilities as independent variables in our analysis, considering them as constant factors that could influence housing prices.  
For data partitioning, we utilized approximately one year's worth of data, from November 2021 to November 2022, as our training dataset, while we used about one month of data for December 2022 as our testing dataset. Considering the scope and objectives of our study, we chose to focus solely on sales data. Hence, out of the total 360,084 instances in our training dataset, we utilized 58,342 instances of sales data, and out of the 25,622 instances in our testing dataset we utilized 2804 instances of sales data. We noted that there were missing values observed in our dataset, specifically, in the 'BLDG_YEAR' (Building Years) variable. In the training dataset, we identified 810 missing instances, while in the testing dataset we found 35 missing instances. The specific approach for handling these missing values is detailed in the subsequent methodology subsection of this study.
## 4 Conclusions  
The effects of vacancy and hydrogen on the growth and morphology of n-type phosphorus-doped C (001)-2\(\times\)1:H and C (111)-1\(\times\)1:H diamond surfaces are theoretically studied in this investigation. The research subjects included single P atoms, PV complexes, and PVH complexes. The calculations are based on DFT under periodic boundary conditions. The properties included: (i) the formation energies of doping a single P atom, PV complex, and PVH complex, (ii) surface structural deformation caused by dopants, and (iii) bond populations of the chemical bonds around the dopants. Based on our calculations, the following conclusions are drawn from the results:  
(1) For (111) diamond surfaces, the formation energy of the PV complex is lower than the formation energy of a single P atom in the third, fourth, fifth, and sixth layers. In the third and fourth layers, the formation energy of a single P atom is even higher than that of the PVH complex, indicating that when doping P atoms on (111) surfaces, the doped P atoms are likely to form PV and PVH complexes with the surrounding vacancies and H atoms. Thus, it is very important to control the number of vacancies and H atoms around doped P atoms during MPCVD fabrication of (111) diamond surfaces. (2) The formation energy of a single P atom is generally the lowest in (001) surfaces in the second to sixth layers, which means that compared with a single P atom, PV and PVH complexes are less likely to form on (001) surfaces (but PV and PVH complexes form more easily than on (111) surfaces because the lowest formation energies of PV and PVH complexes in each layer of (001) surfaces are all lower than those of (111) surface. The lowest formation energy of doping a P atom in each layer of (001) surface is at least 0.74 eV lower than that of (111) surface). However, in the third and fourth layers, the formation energy of PVH complexes is close to or lower than the formation energy of doping a single P atom. For example, the lowest formation energy of PVH complexes in the fourth layer is 3.75 eV, and the lowest formation energy of doping a P atom in the fourth layer is 3.86 eV. Thus, decreasing the amount of hydrogen in the formation process of (001) diamonds can effectively reduce defects. (3) Dopants cause surface structural deformation, and the formation energy is partly related to the extent of deformation. When the structural deformation is insufficient, distortions caused by doping are limited and eventually increase the formation energy, similar to doping a single P atom. When the structural deformation is excessive, more energy is required to reconstruct the surrounding structures, similar to the PV and PVH complexes. (4) Around the PVH complex, the P atom does not bond with the H atom, but instead bonds with the five nearby carbon atoms. Since phosphorus has only five valence electrons, compared with the six C-P bonds around PV complexes, the P-C bond populations around PVH complexes are higher, indicating that the P-C bonds around PVH complexes are stronger than those around PV complexes and P-C complexes are more stable than PV complexes.
# Gaussian Processes for Signal Processing and Representation in Control Engineering
## 8 Various Optimization Problems
### Image Processing  
The last presented application of GP is from the field of image processing. The whole idea in this example is to use GPR for image time series rap-filling and crop monitoring [47]. Basically, the optimization problem here is mainly about optimizing the GPR itself for a better performance in this field, but it is still a very good example of its general application. GP proves to be a very competitive solution in the field compared to the others available, especially as it has an uncertainty band. However, its comparative complexity is problematic especially in the per-pixel approach mainly for GPR training rather than the fitting step. The authors proposed their own solution to get rid of this burden.  
The authors demonstrate different ways to accomplish GPR fitting. They start with the base of other attempts to use GP for image processing which have been successful such as in [48; 49; 50; 51] and many more. The authors focus mainly on describing the process of setting up the model for their case. A kernel function called Squared Exponential deserves special attention, as it is the most commonly employed covariance function in this area as it reflects the prior beliefs of the problem. SE is described in detail in the work, especially by means of its parameters. The process of optimizing hyperparameters is maximising the log marginal likelihood.  
To mitigate the mentioned computational burden, the authors proposed to substitute the per-pixel optimization step with the creation of a cropland-based precalculations for the GPR hyperparameters. The whole process is shown in Figure 5 and consists of:  
1. Crop type selection : for each crop type found, select random parcels;
2. Hyperparameter optimization: performed by assessing individually each pixel;
3. Hyperparameter average: mean of the previously trained hyperparameters for each crop type--also global average is computed;
4. Time series prediction: LAI-reconstructed time series computation with different GPR parameterizations;
5. Statistical analysis for performance comparison: evaluation of the performance of the different GPR models in terms of reconstruction with RSME indicator;
6. Phenological metrics extraction: analysis on how different GPR parametrizations affects estimations.  
Overall, the results of this optimization are quite promising. The proposed method of the calculation of hyperparameters can be even 90 times faster, while being just slightly less accurate than non-reconstructed parameters. Moreover, the authors point out that GP is underutilised currently in time series processing and just some recent studies started to explore it in crop monitoring and as can be seen there is still room for GP to be researched in this field [47].
## 2 State of the Art  
### WiFi Sensing and Localisation  
WiFi sensing is predicated on the propagation of WiFi signals through physical environments [2,3]. These signals incur multipath propagation effects such as scattering, reflection, refraction, and fading when wireless signals are sent from a transmitter to a receiver [14]. These multipath effects can be encapsulated by the CSI, which is a complex-valued metric that describes the total attenuation and phase delay incurred on the wireless signal as it propagates between Tx and Rx. For a WiFi system with OFDM, when a static object is placed in the Line of Sight (LoS) of the wireless signal channel, it can affect each subcarrier uniquely, and we can use these CSI time series patterns for further data processing. Recent work has capitalised on this metric and applied machine learning (ML) in order to infer qualities about the environment based on changes in CSI [3,15].  
One such application of CSI is localisation, in which users can be tracked as they move through indoor environments [16]. Currently, CSI-based indoor human localisation and tracking is achieved mainly based on Multiple-Input-Multiple-Output (MIMO) systems and the usage of the CSI phase, which, for instance, includes phase offset calibration [17], MUSIC-based phase calibration [18], phase sanitization [19], and so on. Whilst current endeavours in the literature are successful in localising large objects in the channel [20], it is less clear whether the position of smaller entities can be discerned.
## 4 Experiment Results
### Cooperative Visual Augmentation Results Based on Fusion  
Figure 11 shows the final visual augmentation results: the left column are the fusion results based on affine transformation and the right column shows the results on deep-affine transformation. If the front vehicle detected the object on the street, it will send its image data to the host vehicle to realize fusion. After being filtered, the occluded objects are fused with the fusion region in the host image. The fusion process blends the pixel colors in the back vehicle image with the corresponding pixels in the occluded objects' area in the front vehicle image. The fusion region is a circle. As described in Section 4.5, the blending weight is adjusted to use more color of the pixels from the front image close to the center and retain more pixel color from the host image away from the center.  
As shown in Figure 12, the left column images show the final fusion results by using the original affine matrix in [19] and the right column images are the results of the new deep-affine matrix in our method. The top three rows of images give the real occluded situation, showing that the occluded vehicles are blocked by other vehicles in road. However, in the bottom three rows of images, the vehicles are artificially blocked by a white panel to simulate occlusion. In either situation, the occluded vehicles can be visually perceived by drivers or autonomous systems of ego vehicles. Furthermore, the fusing size and location of the blind spots are closer to the ground truth after adding the depth information.  
Figure 12: _Cont._  
Certainly, our method fails to obtain accurate infused images in some cases. As shown in Figure 13, for example, the existence of many mismatched and sparse feature pairs between the inter-vehicle images result in incorrect fusion and terrible IoU performance.  
Figure 12: Visual augmentation results based on multiview image fusion.