esa-sceva/satcom-chunk-collection huggingface dataset:
# The Sun and Space Weather
## 2 Basic Properties of CMEs
### CME Mass and Kinetic Energy  
By estimating the number of coronal electrons from the observed brightness of CMEs, one can derive the number of protons and ions in the corona associated with these electrons and hence the mass of CMEs [50]. The left column of Figure 4 shows the CME mass distribution. The mass ranges over five orders of magnitude from \(\sim\)10\({}^{12}\) g to \(\sim\)10\({}^{17}\) g, with an average value of 3.5 \(\times\) 10\({}^{14}\) g [51]. From the mass and average speed of each CME in the LASCO FOV, we can obtain the CME kinetic energy, which is shown in the right column of Figure 4. The kinetic energy varies over seven orders of magnitude from \(\sim\)10\({}^{26}\) erg to \(\sim\)10\({}^{33}\) erg, with an average value of 1.7 \(\times\) 10\({}^{29}\) erg. For limb CMEs, one can determine the speed and mass without projection effects. The bottom panels of Figure 4 show the mass and kinetic energy of \(\sim\)1100 limb CMEs. We see that the average mass (1.7 \(\times\) 10\({}^{15}\) g) and kinetic energy (2.2 \(\times\) 10\({}^{30}\) g) are higher by an order of magnitude compared to the general case, although the ranges remain the same. These values are consistent with those of the pre-SOHO CMEs [52; 53].  
Figure 4: **#** means Number in Figure 4. Mass (**left column**) and kinetic energy (**right column**) of SOHO/LASCO CMEs from January 1996 to December 2020. The upper (**lower**) panels correspond to all (**limb**) CMEs.  
Figure 3: **(a)** The 11 June 1998 CME at two different instances: in the SOHO/EIT FOV at 09:35 UT and in the LASCO/C2 FIV at 10:28 UT. The CME was also observed in the LASCO/C1 FOV (not shown). The inner and outer white circles represent the solar disk and the LASCO/C2 occulting disk, respectively. **(b)** CME height–time plot that combines SOHO/LASCO (C1, C2, C3) and EIT (E) observations. The solid curve is a third-order polynomial fit to the data points (adapted from [46]).  
It is well-known that faster CMEs are wider [54; 55]. Considering limb CMEs from SC 23, [PERSON] et al. [55] found that the CME width W and speed V are reasonably correlated: W = 0.11 V + 24.3 (W in degrees and V in km/s). Wider CMEs are also more massive [56; 57]: log M = 12.6 + 1.3 log W (M in g and W in degrees). Thus, fast and wide CMEs are more energetic. This is an important characteristic, because energetic CMEs are able to travel far into the IP medium and contribute to severe space weather. The mass loss from the Sun due to CMEs amounts to ~10% of the mass loss due to the solar wind [58; 59]. In a recent investigation, [PERSON] et al. [57] reported that wider CMEs contribute significantly to the Sun's mass loss: halo and partial halo CMEs contribute ~20% each, while CMEs with widths in the range 50-120\({}^{\circ}\) contribute ~60%.
# CaAIS: Cellular Automata-Based Artificial Immune System for Dynamic Environments
## 4 Artificial Immune Algorithm  
An artificial immune system [35] is a branch of computational intelligence that draws inspiration from the natural immune system. It offers various algorithms for solving complex real-world problems [36]. Several applications of AIS algorithms have been reported by researchers, such as optimization [13], power systems [57], scheduling [58], pattern recognition, bioinformatics [59], data mining [60], psychometric technology [61], sensor networks [62], intrusion detection [63], mobile robot control [64], and clinical diagnostics [65]. Taking inspiration from human immune systems, many AIS algorithms are generated. These algorithms include negative selection algorithms (NCA), clonal selection algorithms  
Figure 1: The typical neighborhood model in a 2-D CA; (**a**) [PERSON], (**b**) [PERSON].  
(CLONALG), bone marrow, artificial immune networks (AINE), toll-like receptors (TLR), danger theory, and dendritic cell algorithms (DCA) [36; 60].  
The immune network theory was presented by [PERSON] [66], while the artificial immune network (AIN) algorithm was developed for multi-model optimization by [PERSON] and [PERSON] [67]. This algorithm considers the immune cell as a population and its representation as a real value vector with Euclidian distance. One of the main properties of this algorithm is affinity maturation after random initialization, after which cells suffer mutation based on the affinity of cells to produce colonies according to Equation (1)  
\[c^{\prime}=c+\frac{\exp(-f^{*})}{\beta}\times N(0,\ 1), \tag{1}\]  
where \(c^{\prime}\) is the mutated cell, \(\beta\) is a control parameter for the normalization of fitness value \(f^{*}\), and \(N(0,\ 1)\) specifies a Gaussian distribution by mean and variance 0 and 1, respectively. After the mutation of the clones, cells with maximum fitness values were retained, and cells with fitness values smaller than others were replaced with random cells [37]. Indeed, the AIN algorithm aims to attain a set of representations with the least redundancy. Although the AIN algorithm looks like a clonal selection algorithm, the main difference is attributed to the suppression mechanism for cell interaction. This eliminates certain sets of cells with less fitness than others. Algorithm 1 presents the artificial immune network algorithm pseudo-code [68].  
```
1. Initialize the \(Ab\) population as antibodies, and \(\beta\) is a control parameter.
2. Repeat for each \(Ab\).
3. Evaluate \(Ab\).
4. Select the best \(Ab\).
5. Clone and mutate \(Ab\).
6. Retain the highest \(Ab\) as memories.
7. Remove weak memories.
8. Replace random \(Ab\).
9. Until the termination condition is met
```  
**Algorithm 1** Artificial immune network algorithm
## 4 Application and Results
### CSE Corpus Annotation
#### 4.2.1 Goal Declaration  
The annotation goal was to create the appropriate semantic target to facilitate the CSE attacks recognition by assigning the correct tag to in-context words in a sentence. It was crucial to label all related words or sequences of words or text-spans in CSE attack context so we can later perform efficient NER or text classification. Each word or text span was labelled with a type identifier (tag) drawn from a vocabulary created based on the CSE ontology and indicated _what_ various terms denote in the context of a CSE attack and _how_ they interconnect between them.  
As mentioned before, SME employees are vulnerable to sensitive data leakage of type _Personal_, _IT_, and _Enterprise_ details. Moreover, ML/NLP algorithms are more efficient to recognise terms that belong to abstract classes than terms that belong to more specific subclasses. Thus, our aim during annotation was to identify and assign the correct ontology tag to words or text based on CSE ontology.  
Further refinement of the goal resulted in defining the following objectives:  
* Identify keywords, syntax, and semantic characteristics to detect _Personal_ data leakage. If found, label with appropriate tag.
* Use keywords, syntax, and semantic characteristics to detect _IT_ data leakage. If found, label with appropriate tag.
* Identify keywords, syntax, and semantic characteristics to detect _Enterprise_ data leakage. If found, label with appropriate tag.
* Identify noun-verb combinations and verb repetitions based on blacklists.
# Geo-DMP: A DTN-Based Mobile Prototype for Geospatial Data Retrieval
## 4 Experimental Validation and Evaluation
### Partial Intermediate Cached Contents
#### 4.2.2 Validation of Delay-Tolerant Client Visualization  
As confirmed in the previous part, contents can return from multiple sources at different times. The next question is, from the standpoint of the user, whether they can be collectively and correctly visualized in the browser across a prolonged time duration. Recall that as one advantage of gateway-based implementation, the terminal is not required to be aware of the DTN and only a little adjustment is enough for our purpose. In our implementation, through tuning the parameter of response timeout of the browser, the responses returned at a later time will not be dropped.  
In this experiment, we imagine that a user without network access is in need of a map of his surroundings. The map data is retrieved from several other nodes in the form of vector tiles which are data units that we propose in our former work [32]. Seven nodes take part in and their contacts are prearranged. Node 1 acts the requester and Node 7 is as the RSU. In order to achieve the effect of queries being satisfied by multiple sources, we put one-third of the requested contents on Node 2 and one-third on Node 3 in advance, assuming that they have received these contents beforehand. The remaining one-third has to be fetched from Node 7. The bundle exchanges are shown in Figure 19. To make the result more clear, the details of each batch of returned contents are summarized in Table 3. As we can see from the screenshots of the browser in Figure 20, the vector map incrementally becomes complete. This verifies that the client-side can support delay-tolerant content retrieval and visualization.  
Figure 19: The timeline of interactions—in the experiment on client visualization.  
Figure 18: The timeline of interactions—the case of partial intermediate caches.
## 2 Materials and Methods
### Data Processing  
adsorption data were fitted to the Langmuir (Equations (1)-(3)) and Freundlich (Equations (4) and (5)) isotherms, as the widely used adsorption models for natural sorbents, including oxides, aluminosilicates and soils [28; 29]. The Langmuir isotherm is described by Equation (1) and its linearised form by Equation (2):  
\[q=\frac{Q_{t}\cdot K_{L}\cdot c}{1+K_{L}\cdot c} \tag{1}\]  
\[\frac{1}{q}=\frac{1}{Q_{t}}+\frac{1}{Q_{t}\cdot K_{L}\cdot c}, \tag{2}\]  
where \(q\) is an equilibrium concentration of adsorbed ion (adsorbate) in solid phase (mol g\({}^{-1}\)), \(c\) is an equilibrium concentration of adsorbate in solution (mol L\({}^{-1}\)), \(Q_{t}\) indicates the theoretical adsorption capacity (mol g\({}^{-1}\)) and \(K_{L}\) is a Langmuir adsorption constant (L mol\({}^{-1}\)). The equilibrium amount of adsorbate caught in the solid phase (\(q\)) was calculated using Equation (3), where \(V_{0}\) is the volume of the solution (L), \(c_{0}\) is the initial concentration of the  
Figure 1: Illustrative photo of composite adsorbents.  
adsorbate in the solution (mol.L\({}^{-1}\)), \(c\) is the equilibrium concentration of the adsorbate in the solution (mol.L\({}^{-1}\)) and \(m\) is the mass of the solid phase (g):  
\[q=\frac{V_{0}\cdot(c_{0}-c)}{m} \tag{3}\]  
The obvious adsorption parameters of the Langmuir model (\(q_{\max}\)--maximum equilibrium adsorption capacity; \(Q_{l}\)--theoretical adsorption capacity; \(R^{2}\)--correlation factor; \(K_{L}\)--Langmuir adsorption constant) were then used to evaluate the effectiveness of the adsorption systems.  
The Freundlich isotherm can be expressed by Equation (4) and its linearised form by logarithmic Equation (5):  
\[q=K_{F}\cdot c^{n} \tag{4}\]  
\[logq=logK_{F}+n\cdot log\ c, \tag{5}\]  
where \(q\) is an equilibrium concentration of adsorbed ion (adsorbate) in solid phase (mol g\({}^{-1}\)), \(c\) is an equilibrium concentration of adsorbate in solution (mol L\({}^{-1}\)), \(K_{F}\) is a distribution coefficient (L g\({}^{-1}\)) and \(n\) is a temperature-dependent constant (\(n<1\)).  
The Gibbs energy (\(\Delta G\)) associated with the adsorption process was calculated from the Langmuir adsorption constant by Equation (6):  
\[\Delta G=-RT\cdot lnK_{L}, \tag{6}\]  
where \(R\) is the molar gas constant (J mol\({}^{-1}\) K\({}^{-1}\)), \(T\) is temperature (K) and \(K_{L}\) is a Langmuir adsorption constant (L mol\({}^{-1}\)).  
The adsorption efficiency was then calculated using Equation (7), where \(\varepsilon\) is the adsorption yield (%):  
\[\varepsilon=100-\frac{c\cdot 100}{c_{0}} \tag{7}\]  
The adsorption value was calculated as the average of three identical measurements in which the standard deviation did not exceed 10%.
# DDL R-CNN: Dynamic Direction Learning R-CNN for Rotated Object Detection
## 4 Experiment
### Ablation Experiment
#### 4.4.2 Ablation Experiments on the Number of Cores Inside the Module  
To gain a deeper understanding of how the number of kernels within the module affects overall performance, we conducted ablation studies on the DDL module using the UCAS-AOD dataset. Based on the baseline model (R-CNN), this experiment varied the number of kernels embedded in the DDL module within the model to assess changes in the Params, FLOPs, and mAP. The model was evaluated with a single kernel configuration using ResNet-50 (R50) [25] and ResNet-101 (R101) [25] as the backbone networks. Subsequently, we inserted the DDL module into the R50 backbone network and gradually increased the number of kernels to observe changes in performance.  
Table 2 presents the specific experimental results under different kernel configurations. It can be observed that when the number of kernels in the model is within 6 and R50 is used as the backbone network, both the Params and FLOPs increase slightly with the addition of kernels. For instance, as the number of kernels increases from 1 to 6, the Params and FLOPs of the DDL-R50 model rise from 41.18 M to 96.52 M and from 211.45 G to 212.08 G, respectively. More importantly, the mAP increases from 76.97% to 77.38%, showing a consistent upward trend. This indicates that the more kernels there are, the stronger the model's feature representation capability and, consequently, the higher the detection accuracy.  
However, when the number of kernels exceeds 6 to 10, there is an increase in both the model's Params(M) and FLOPs (G), from 96.52 M to 144.53 M and from 212.06 G to 212.68 G, respectively, while the mAP hardly improves. This indicates that having more than 6 kernels not only fails to enhance the model's feature representation capacity but also leads to a significant waste of computational resources due to excessive redundant feature expressions, resulting in reduced operational efficiency for the model.  
Overall, the experimental results show that by increasing the number of cores in the new method to 6, the performance can be significantly improved while maintaining computational efficiency. That is, the model applying the DDL module achieves a good balance between the Params and computational complexity when the number of kernels is set to 6.
# Prediction of Belt Drive Faults in Case of Predictive Maintenance in Industry 4.0 Platform
## 2 Belt Drive Diagnostics Approach  
Vibration diagnostics provide detection of virtually all malfunctions of machinery and equipment operated in industrial conditions; these include imbalance of rotating components, rubbing, mechanical clearances, mechanical gear damage, and bearing damage [15]. Vibrations caused by damaged equipment also produce acoustic waves that can be picked up with a microphone. Real-time analysis of the audio signal and advanced algorithms are able to identify the onset of failure due to defects, degradation, overloading, or improper maintenance. The use of advanced machine learning algorithms for this purpose allows the extraction of valuable information about the device, which can help prevent failures or schedule maintenance work early. Work by researchers around the world on acoustic and vibration signal analysis and processing has seen approaches such as time domain analysis, which is a classic technique for fault detection. Time domain analysis typically uses statistical parameters to determine the condition of a belt drive [16] and an example of time-frequency analysis is presented in [17, 18].  
Most existing approaches used to diagnose belt drive failures are based on a supervised learning paradigm, which requires a collection of data from correct operation and during failure, collected a priori. In this paper, an adaptive approach is presented (Figure 1), with vibrations measured using accelerometers. By this approach, fault data are not required to monitor the current state of the belt drive, among other reasons because fault data in production are usually hard to access.
## 2 Physical Experiments and Results
### Hydrological Characteristics  
Subsurface water content and soil pore water pressure might reflect the effects of rainfall on the processes of surface sealing, runoff generation and soil sediment production. For one thing, high soil moisture content or low negative pore water pressure results in an increased detachability by surface runoff shear forces and raindrop impact [14]. For another aspect, a high water content of the soil might decrease aggregate shaking and breakdown due to less air escaping upward, reducing the detachment of soil by raindrop impact [15,16]. In Figure 1, m1 is at the middle deep position; m2 is near to the surface; and m3 is at the low part. By contrast, p1 is at the middle depth position; p2 is near to the surface; and p3 is at the toe of the slope. Figure 7a demonstrates that m2 near to the surface increases more easily. Due to their depth, m1 and m3 have a certain time lag before increasing of water content. Furthermore, m3 has a higher value and a shorter time lag because of the water flow supply. In Figure 7b, the pore water pressure follows a similar pattern. Point p2, at the surface, is always saturated to some degree and has no potential to increase. Point p3 has a higher value than p1 because of its deeper position. For the same material, a higher rainfall intensity means more water input. Therefore, m and p have higher values and lower time lags in Figures 8 and 9. For the different materials, in Figures 8, 10 and 11 the coarse particles in soil Sample 3 have higher permeability or a higher infiltration rate than fine particles. Thus, the time lag of pore water pressure production in coarse materials is shorter and the value is higher. The moisture value depends on the balance of the water infiltration, supply, and drainage.  
Figure 6: Results of soil erosion experiments.  
Figure 8: Soil Sample 1-45 mm/h: (**a**) Moisture content of m1, m2, and m3; and (**b**) pore water pressure of p1, p2, and p3.  
Figure 9: Soil Sample 1-65 mm/h: (**a**) Moisture content of m1, m2, and m3; and (**b**) pore water pressure of p1, p2, and p3.
# Spoofing Detection Algorithm Based on Pseudorange Differences
## 2 Spoofing Detection Algorithm  
In this section, we introduce the theoretical analysis of the algorithm. We build the single receiver pseudorange double-difference model, then apply Taylor expansion and iterative calculation to the pseudorange double-difference model to obtain the position at the current epoch. By comparing the results of the traditional least squares method to the spoofing detection algorithm proposed in this paper, we can identify the authenticity of the signal. The parameter setting of the proposed algorithm is pointed out in Section 2.2. The algorithm summary and explicit flowchart are given in Section 2.3.
# Influence of Social Overhead Capital Facilities on Housing Prices Using Machine Learning
## 3 The Proposed Scheme
### Modeling Process  
In this section, we delve into the intricacies of our modeling process, starting with an assessment of the input variables. Our model incorporates a total of fifteen input variables. Among these, three variables, the 'NEAR_SUB_DIST', 'FLR' (Floor), and 'XUAR' (Exclusive Area), are directly extracted from Table 1 of our foundational dataset. They respectively represent the distance from a subway station (within 500 m), the floor number of the building, and the exclusive area of the house. The remaining variables have been refined through various preprocessing steps. For instance, the 'WTD_SUBW_RANK' variable was derived by summing the boarding and alighting counts at subway stations and then normalizing these values. A significant aspect of this preprocessing involved geocoding based on the 'ADRES' variable, which allowed us to utilize the resulting latitude and longitude coordinates. In addition, we introduced the 'INC_BY_REG' variable, which is based on the 2021 wage income settlement details specific to the housing's region, be it a city or district. The 'PLC_RATE' variable reflects the benchmark interest rate set by the Bank of Korea on the transaction day. Furthermore, the variables 'ELET_SCH' and 'MDL_SCH' denote the count of elementary and middle schools, respectively, within a 500 m radius. In a similar vein, the 'CNT_PARK' and 'CNT_LIB' variables capture the number of parks and libraries, respectively, within the same proximity. Lastly, the 'COMP_NM' variable serves as an indicator signifying whether the housing was constructed by a renowned construction company. A comprehensive list of the input variables utilized in our final model can be found in Table 3.
# MIMO Over-the-Air Computation for Distributed Estimation
## 6 Conclusions  
This article presented the MIMO-AirComp framework to efficiently compute the target multifunction of various applications while reducing the spectrum utilization and delay of wireless networks. The representative target function of the distributed estimation was explicitly converted to the nomographic function, which was matched to the structure of the multiple access channel with proper processing functions. The proposed framework efficiently computed the nomographic function by collaborating with the local preprocessing at each node, the signal aggregation of the wireless channel, and the postprocessing at the fusion center. The MIMO-AirComp framework with a large number of nodes suppressed the noise impact of both the wireless channel and the measurement, while the conventionally separated communication and computation approach mainly reduced the impact of the channel noise at the cost of the time delay to collect measurements. Specifically, the proposed approach achieved a high computation throughput compared to the conventional approach when the network relied on a large number of noisy measurements obtained by distributed sensors under the fair channel condition.  
Conceptualization, P.P.; Investigation, P.P., H.S. and P.D.M.; Methodology, P.P.; Software, P.D.M.; Validation, H.S.; Writing--original draft, P.P.; Writing--review & editing, P.P. and P.D.M. All authors have read and agreed to the published version of the manuscript.  
The work of Pangun Park was supported in part by the Basic Research Laboratory of the National Research Foundation under Grant NRF-2020R1A4A2002021 and in part by Regional Innovation Strategy funded by the Ministry of Education (2021 RIS-004). The work of [PERSON] was supported by the Centre of Excellence on Connected, Geo-Localized and Cybersecure Vehicles (EX-Emerge), funded by the Italian Government under Grant CIPE 70/2017 (7 August 2017).  
Not applicable.  
The authors declare no conflicts of interest.