esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Sensorimotor Loop Representation  
One can represent the motor control (movement control) in humans as a simple feedback control system of the form shown in Figure 1. In the schematic shown, the body dynamics is the natural dynamics in the absence of any neural control of the body part of interest (e.g., hand). The feedback path represents all sensory feedback, including proprioceptive feedback, visual feedback, and tactile feedback. These types of sensory feedback are carried to the controller (brain) by afferent nerves. The controller represents the neurosystem's logic, which continuously compares the kinematic variables from sensory feedback (e.g., actual velocity) with the desired kinematic variables (e.g., desired velocity) to determine the motor command. This motor command is then conveyed through the efferent nerves and results in muscle actions to obtain the desired response. We refer to this closed-loop feedback system consisting of motor actions and sensory feedback as the human sensorimotor system. As there are various time delays in the human sensorimotor system, including delays due to nerve conduction times and information processing time, for simplicity, we lump all sensorimotor system delays (delays in various portions of the sensorimotor system) into one transport delay in the closed-loop feedback system. Finally,the saturation in the loop approximates the physiological limit of the transmission of neural control actions [51].  
A number of studies [36; 37; 38; 42; 45; 47; 48; 49; 52] have observed that increased sensorimotor loop delay in PD tends to destablize the sensorimotor system. This is supported by the observation that response time in patients with PD is larger than that in healthy individuals [53; 54; 55; 56]. According to these observations, the sensorimotor system of a healthy individual is a control system with a smaller delay, and the sensorimotor system of an individual with PD is a control system with a larger delay. Thus, this larger delay, that is, a delay beyond a certain threshold, leads to instability in the human sensorimotor system. This observation also explains some of the clinical features of Parkinson rest tremors [47; 49] and provides a possible explanation of how high-frequency deep brain stimulation suppresses low-frequency rest tremors [57]. There are some works that also consider other possibilities for the source of instabilities including an increased gain in the sensorimotor loop [42]. However, as stated earlier, the subsequent development will not rely on this assumption but simply focus on the degradation of stability and will be applicable regardless of the source of instability.  
With this background, in Section 2.2, we provide the main concept of the proposed methodology.
# Automated Data Acquisition in Construction with Remote Sensing Technologies
## 3 Standalone Remote Sensing (RS) Technologies
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**RS Technology** & **Capabilities** & **Limitations** & **Accuracy** & **Refs.** \\ \hline  & (1) & Global access in an outdoor environment & (1) & The acquired data are limited to position and time of objects, and not the identity or type of activities & \\  & (2) & RIK GPS provides centimeter-level & (2) & Dixbys in data processing and transferring (an important issue specially for real-time operations such as equipment tracking or kinematic surveying) & Almost one meter for GPS and few centimeters for RTKGPS & [5,22,23] \\  & (3) & Heisply and reacting quickly based on the needs in a construction site & (4) & Multipath errors in the congested environment & for RTKGPS \\ \hline  & (1) & Enhanced tracking and automation capabilities in the positioning of objects & (1) & The high investment \\ Robotic total & (2) & High positioning update rate & (2) & Limited capability to track only one point at a particular time & Down to few millimeters and arc seconds & [7,24] \\ station (RTS) & (3) & Higher vertical positioning (elevation) accuracy than unaided GNSS and GPS-based technology & (3) & Positioning errors will occur if the angles and distances are not captured simultaneously & are seconds & \\ \hline  & (1) & Reasonable price & (1) & Very limited reading range & \\  & (2) & Straight forward usage with standard implementation protocols & (2) & Sensitivity to the harsh environment 3 (e.g., tags are not readable if covered by snow) & N.A & [20,21,25] \\ Barcode & (3) & Speeding up the computer data entry & (5) & Low data storage capacity & \\  & (4) & Porability & & \\ \hline  & (1) & Longer range than barcodes (up to 100 m for Ultra-high frequencies) & (1) & Lack of accuracy and difficulties for 3D positioning & \\  & (1) & Ultra-high frequencies & (2) & Calibration difficulties (e.g., the need for a path loss model) & \\  & (2) & Non-line-of-sight & (3) & Affected by multipath effect & \\  & (3) & Providing cost efficient location information & & many tags & \\ RFID & (4) & Tags are light and easy to attach & (5) & Active tags are fairly expensive. & Down to few meters for 2D \\  & (5) & Unlike barcodes, RFID tags are durable in construction environments & (6) & Plus, relatively wide range may cause obstruction & localization & \\  & (6) & Batch readability of tags making & (7) & The need for battery replacement in active tags & \\  & & identification process much more efficient & (8) & Influenced by metal and high humidity specially in high frequencies & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Capabilities and limitations of standalone remote sensing (RS) technologies
## 2 Physical and Mechanical Test  
### Project Overview  
The Dongsong Hydropower Station is located in Ganzi Prefecture, Sichuan Province, China, at an altitude of more than 2500 m. This paper relies on No. 2 diversion tunnel with a vertical burial depth of about 300 m, and large deformation is encountered in the construction process because of high geostress with weak and fractured rock mass. In the tunnel area, there are mainly NS (north-south) folds and faults, followed by NE and NW faults. There are 3 large-scale faults, which are the Suorang Fault (F48), Pulubiansong Fault (F48) and Xiangcheng Fault (F50). The surrounding rock is mainly phyllitie slate with a small amount of argillaceous slate and metamorphic sandstone. The lithology is extremely uneven and changes frequently. The shear fracture zone and joint are developed. The precipitation in the tunnel area is less, and the surface water is mainly supplied by the melting of alpine snow.  
Groundwater has the characteristics of less abundant water, large slope and deeper burial. After excavation, the surrounding rock is wet, some parts are dripping.  
In order to gain the physical, chemical and mechanical characteristics of surrounding rock of diversion tunnel, and to provide basic parameters for the analysis of a large deformation mechanism, the optimization of tunnel excavation method, and the design and construction of the support structure, rock samples were taken from the construction site for laboratory tests (tested at the Key Laboratory of Transportation Tunnel Engineering, Ministry of Education, Southwest Jiaotong University, Chengdu, China). The test includes four aspects: mineral composition test, free expansion rate test, rock expansion pressure test, and rock mechanics test. Rock samples were taken from the site, as shown in Figure 1. The rock samples are phyllite slate.
## 2 Literature Selection Strategy  
Our review aligns with the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) guidelines for systematic reviews.  
Relevant literature was collected from four distinct databases: Scopus, Web of Science, IEEE Xplore, and Science Direct. Preferences were shortlisted using the search terms (\"knowledge graph\" OR \"semantic\" OR \"ontology\" OR \"ontologies\") AND \"cyber threat\" on each publication's title, abstract, and keywords. The results were restricted within the time period of 2013 to 12 March 2024.  
After applying the aforementioned preliminary criteria, 225, 21, 80, and 69 papers were obtained from the Scopus, Science Direct, IEEE Xplore, and Web of Science databases, respectively. Works not written in English were later removed, along with book chapters,mini-reviews, early access papers, conference reviews, short surveys, notes, and editorials. Any gray literature, such as technical reports or theses, were also excluded. Thus, our collection consisted exclusively of peer-reviewed articles and conference papers submitted at reputable relevant conferences. After the removal of duplicates and the first stage of the screening process, 215 papers remained.  
In the final stage of the screening process, we examined the 215 remaining papers and removed works that were not focused on either the construction or utilization of ontologies or KGs. ML-related works that resulted in the generation or expansion of ontologies and KGs were included in our final literature collection, while studies that applied ML and DL methods solely to entity and relationship extraction tasks without demonstrating the creation or extension of ontologies and KGs were dismissed. Five papers were removed due to accessibility issues. To ensure the reliability and consistency of our selection process, all accessible works that passed the initial stages of the screening process were independently reviewed by three researchers. Studies that received unanimous agreement (\"yes\") from all three reviewers were immediately accepted. For studies that raised discordance, a second round of reviewing was conducted until consensus was reached. No formal statistical measures of inter-rater reliability were applied; instead, the final selection was made based on the collective agreement of all authors. After the screening process, we settled on a collection of 76 papers for review. The results of the literature collection procedure are presented in Figure 1, while Figure 2 shows the number of produced works per year satisfying our selection criteria.  
Figure 1: Number of records in each stage of the selection process.
# An Experimental Study of Industrial Site and Shaft Pillar Mining at Jinggezhuang Coal Mine
## 5 New Shaft Pillar and Panel Design  
Shaft pillar mining is an integrated technology for unconventional underground coal mines. A shaft pillar influences the buildings constructed on the surface, such as head frames, shafts and their facilities, shaft stations, and tunnels. It is a 3D space protection system. The research and design of shaft pillars has two purposes. The first is to minimize the influence of shaft pillars on surface buildings. The second is to ensure the safety of production facilities.  
According to the excavation and monitoring results obtained for panel 0091, a new shaft pillar was designed to improve the recovery ratio. The new design was based on two rules. The first was that the required protection level of the buildings in the industrial square should be reduced.  
The second was to ensure the safety of the shaft until the colliery was closed.  
A new shaft pillar was designed with a boundary angle of = 49\({}^{\circ}\) obtained from panel 0091, as shown in Figure 9.  
The panels were designed for production until the coal mine was closed in December 2017. The schematic of the new panels is shown in Figure 10 and their parameters are provided in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Panel No.** & **Mine Time** & **Mine Depth (m)** & **Mine Thickness (m)** & **Dip Angle (\({}^{\circ}\))** & **Production (t)** \\ \hline
0091 & 2008.08\(\sim\)2009.05 & \(-\)290\(\sim\)\(-\)321 & 6.8\(\sim\)7.1 & 3\(\sim\)12 & 1,177,039 \\
0092 & 2010.06\(\sim\)2011.05 & \(-\)282 \(\sim\)\(-\)321 & 6.8\(\sim\)7.1 & 4\(\sim\) 8 & 643,910 \\
0021D & 2014.01\(\sim\) 2014.12 & \(-\)334\(\sim\)\(-\)339 & 4.75\(\sim\)5.15 & 1\(\sim\)9 & 610,488 \\
0090N & 2015.01\(\sim\)2015.08 & \(-\)228\(\sim\)\(-\)297 & 3.5\(\sim\)5 & 1\(\sim\)19 & 281,536 \\
0094 & 2015.08\(\sim\)2016.01 & \(-\)276\(\sim\)\(-\)301 & 4 & 2\(\sim\)14 & 210,790 \\ \hline \end{tabular}
\end{table}
Table 1: Mine scheme and plan of JGZ.  
Figure 8: Back analyzed subsidence vs. measured subsidence.  
Figure 10: Map of working panels and newly designed pillar.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Panel No.** & **Mine Time** & **Mine Depth (m)** & **Mine Thickness (m)** & **Dip Angle (\({}^{\circ}\))** & **Production (t)** \\ \hline
0024B & 2015.07\(\sim\)2015.11 & \(-\)314\(\sim\)\(-\)340 & 1.7\(\sim\)2 & 1\(\sim\)14 & 14,141 \\
0024B & 2016.05\(\sim\)2016.07 & \(-\)314\(\sim\)\(-\)340 & 1.7\(\sim\)2 & 1\(\sim\)14 & 69,118 \\
0096 & 2016.07\(\sim\)2016.11 & \(-\)271\(\sim\)\(-\)289 & 4.6\(\sim\)4.8 & 1\(\sim\)11 & 148,404 \\
0025D & 2016.12\(\sim\)2017.04 & \(-\)325\(\sim\)\(-\)349 & 3.6\(\sim\)3.8 & 3\(\sim\)12 & 133,063 \\
0098 & 2017.10\(\sim\)2017.12 & \(-\)275\(\sim\)\(-\)285 & 5.6\(\sim\)7.0 & 0\(\sim\)4 & 309,120 \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 9: Geology of Section 1-1.
# Research on an ID-PCA Early Fault Detection Method for Rolling Bearings
## 4 Experimental Results and Analysis
### Simulation Results and Analysis of Rolling Bearing Outer Ring Faults
#### 4.3.2 Performance Comparison  
This section compares the experimental results of I-PCA, DE-PCA, DV-PCA, and ID-PCA by analyzing the failure detection rate and detection delay of the incipient failure experimental results of the rolling bearing outer ring.  
Table 4 shows the missing detection rate, fault detection rate, and detection delay of the rolling bearing rolling body fault by I-PCA, DE-PCA, DV-PCA, and ID-PCA methods. We can obtain that the missing detection rate of I-PCA is about 40%, and the fault detection rate of I-PCA is about 20%. We can obtain that the I-PCA method has almost no early failure detection capability. The missing detection rate of DE-PCA and DV-PVA is about 15%, while the fault detection rate of DE-PCA and DV-PVA is 38% and 20%, respectively. Although the detection ability has been improved, the detection effect of rolling element faults is still not ideal. ID-PCA has a missing detection rate of 0 and a fault detection rate of 2%, which is significantly lower than other methods, and has a certain incipient fault detection capability. From the perspective of detection delay, the detection delay of the ID-PCA method is zero. There is a certain improvement in detection delay. Based on the above comparison results, it can be seen that the IDPCA method proposed in this paper has a good effect on the rolling element fault.
## 2 Materials and Methods
### Field Measurements  
Twenty-five sampling sites, measuring 100 m\({}^{2}\) (10 \(\times\) 10 m), were designated. The sites were established within compartments at least 25 m away from the compartment line. The selected experimental plots met four basic criteria. First, the stands were present in the same habitat type and under comparable climatic conditions (temperature, precipitation). They were single-species pine stands after maintenance treatment. Moreover, the age of the pine trees (35 years) was identical in all the plots.  
The diameter at breast height (DBH) (at the height of 1.3 m) and the height (H) were measured for all 630 trees in early spring (March), prior to the start of vegetation, in all the experimental sites. The diameter at breast height was measured exact to 1 mm with a metal Mantax Blue tree calliper (Haglof, Langsele, Vasternorrland, Sweden). When measuring the diameters at breast height, each tree was precisely checked to record damages and the number of bark damage instances (NBDI) on a given tree caused by bark stripping. The heights of the trees were measured with a Vertex III height finder (Haglof, Sweden).  
The following biometric parameters were analysed: the diameter at breast height (DBH), the height (H), the basal area of the stand (\(G_{1,3}\)), the mean basal area of the trees (\(g_{1,3}\)), the stand volume (\(V\)) and the mean tree volume (\(v\)).
## 2 Materials and Methods
### Data Sources  
The administrative division data of Inner Mongolia used to determine the extent of the study area in this paper were obtained from the Resource and Environment Data Center of the Chinese Academy of Sciences ([[https://www.resdc.cn/](https://www.resdc.cn/)]([https://www.resdc.cn/](https://www.resdc.cn/)), accessed on 5 December 2021); the DEM data used to extract the elevation and slope came from the ASTER GDEM dataset on the Geospatial Data Cloud platform with a resolution of 30 m ([[http://www.gscloud.cn/](http://www.gscloud.cn/)]([http://www.gscloud.cn/](http://www.gscloud.cn/)), accessed on 5 December 2021); the remote sensing data used to calculate NDVI and NDWI came from the Landsat8 dataset on Google Earth Engine platform, with a resolution of 30 m ([[https://developers.google.cn/earth-engine](https://developers.google.cn/earth-engine)]([https://developers.google.cn/earth-engine](https://developers.google.cn/earth-engine)), accessed on 5 December 2021); the land use data came from the global land cover data provided by GlobeLand30, with a resolution of 30 m and data dated to 2020 ([[http://www.globallandcover.com/](http://www.globallandcover.com/)]([http://www.globallandcover.com/](http://www.globallandcover.com/)), accessed on 5 December 2021); the annual precipitation data came from the Chinese ground-based meteorological observation dataset provided by China Meteorological Data Network, with data dated to 2021 ([[https://data.cma.cn](https://data.cma.cn)]([https://data.cma.cn](https://data.cma.cn)), accessed on 5 December 2021); and the NPP data came from the MODIS Land Standard Product MOD17A3H dataset with a resolution of 500 m for the year 2021 ([[https://ladsweb.modaps.eosdis.nasa.gov/](https://ladsweb.modaps.eosdis.nasa.gov/)]([https://ladsweb.modaps.eosdis.nasa.gov/](https://ladsweb.modaps.eosdis.nasa.gov/)), accessed on 5 December 2021). All the data mentioned above were used in the same scope as the study area, and the forms used included both the image data and the attribute data it carried.  
Data processing mainly used ArcGIS, ENVI, MATLAB, SPSS, and other software.
## 5 Experimental Setups and Procedures
### Setup II Configuration  
This setup uses a similar configuration to setup I. However, it additionally incorporates an SDN controller and uses a multi-Ethernet interface computer running Open vSwitch (OVS), instead of a normal Ethernet switch. Both WiFi routers (for the sending and receiving nodes) and the EPC system are connected to the OVS switch. The SDN controller is implemented using Ryu for simplicity. It employs the standard OpenFlow protocol [42] to manage the operation of the OVS switch. This includes controlling the switch's data flow, forwarding rules, and other traffic flows.
# A Novel and Adaptive Angle Diversity-Based Receiver for 6G Underground Mining VLC Systems
## 2 VLC System Model Applied to Underground Mining Environments
### Receiver Optical Power  
For the UM-VLC system, we denote as \(P_{t}\) the optical power transmitted by a single LED. For simplicity, we assume that all LEDs in set \(I\) transmit the same \(P_{t}\). Therefore, the optical power received at PD \(j\) from LED \(i\) is expressed as  
\[P_{r,j,i}=R_{PD}P_{t}H_{miner,i,j}+N_{j}, \tag{13}\]  
where \(P_{r,i,j}\) is the power received of PD \(j\), \(R_{PD}\) is the PD responsivity, and \(N_{j}\) is the additive noise in PD \(j\) that includes two types of noise that particularly affect UM environments, shot noise and thermal noise whose variances are denoted as \(\sigma_{shot}^{2}\) and \(\sigma_{thermal}^{2}\) respectively.  
As mentioned in the contributions of the work, one of the objectives of this research is to study the impact of an adaptive orientation receiver applied to the presented UM-VLC system. As such, we will focus on a proposal which will allow us to obtain better system performance in terms of the power received at the receiver through the RSSR parameter. This proposal is based on an array of multidirectional PDs, which exploits the angular diversity of the array and the random orientation of the receiver located on the top of the mining workers' helmets. The following section presents a rigorous mathematical analysis of the proposed receptor structure.
## 5 Performance Evaluation
### Simulation Results
The reason is that for this particular hardware, the optimum value of the \(SNR\) is closer to 0 dBm, thus, there is not a big margin for improvements, as strategy #2 is practically a strategy that tries to achieve a \(SNR=0\) dBm at the receiver. To sum up, the proposed strategy always guarantees a desired accuracy in the distance estimation and, at the same time, yields energy savings, especially when the optimum value of the \(SNR\) is low. As  
Figure 12: Average energy consumption and average number of received packets at the furthest node of the three strategies for the CC2420 consumption model. Figure 13: Relation between the average energy consumption per localization and the distance estimation accuracy for the three strategies for \(l\) = 4 bytes. (**a**) TOA-based localization; (**b**) RSS-based localization. this optimum value depends on the particular energy consumption characteristics of the wireless device, bigger savings are expected for devices with more similar characteristics to that of the ideal case.
## 3 Performance Analysis of NOMA-Enabled HSTRNs
### Channel Model  
First, we assume independent and identically distributed (i.i.d.) channel conditions for each hop. Moreover, under Shadowed-Rician fading model, the probability density function (PDF) of the squared amplitude of the channel coefficient \(h_{SR}\) between satellite and the relay is given by [13]  
\[f_{\left|{h_{SR}}\right|^{2}}\left(x\right) =\alpha e^{-\beta x}{{}_{1}F_{1}}\left({m_{SR}};1;\delta x\right),x>0 \tag{13}\] \[=\alpha\sum\limits_{k=0}^{m_{SR}-1}\zeta\left(k\right)x^{k}e^{- \left(\beta-\delta\right)x},\]  
where \(\alpha=\left(2b_{SR}m_{SR}/\left(2b_{SR}m_{SR}+\Omega_{SR}\right)^{m_{SR}}/2b_{ SR}\right.\), \(\beta=0.5b_{SR}\), \(\delta=\Omega_{SR}/\left(2b_{SR}\right)\)\(\left(2b_{SR}m_{SR}+\Omega_{SR}\right)\) with \(\Omega_{SR}\), \(2b_{SR}\) and \(m_{SR}\) denotes the average power, multipath components and the fading severity parameter, respectively. \(\zeta(k)=\frac{\left(-1\right)^{k}\left(-1-m_{SR}\right)_{k}\delta^{k}}{\left( kt\right)^{2}}\), and \(\left(\bullet\right)_{k}\) is the Pochhammer symbol. Moreover, the PDF of \(\tilde{\gamma}_{SR}\) is given as  
\[f_{\tilde{\gamma}_{SR}}\left(x\right)=\sum\limits_{i_{1}=0}^{m_{SR}-1}\cdots \sum\limits_{i_{M}=0}^{m_{SR}-1}\frac{\Theta\left(M\right)}{\left(\rho_{S} \right)^{\Delta}}x^{\Delta-1}e^{-\left(\frac{\beta-\delta}{P_{S}}\right)x}, \tag{14}\]  
where \(\Theta\left(M\right)=\alpha^{M}\prod_{\ell=1}^{M}\zeta\left(i_{\ell}\right)\prod _{j=1}^{M-1}8(\sum_{l=1}^{j}i_{l}+j,i_{j+1}+1)\), \(\Delta=\sum_{q=1}^{M}i_{q}+M\) and \(\delta(.,.)\) is the Beta function. The cumulative distribution function (CDF) of \(\tilde{\Gamma}_{SR}\) is expressed as  
\[F_{\tilde{\gamma}_{SR}}\left(x\right)=1-\sum\limits_{i_{1}=0}^{m_{SR}-1}\cdots \sum\limits_{i_{M}=0}^{m_{SR}-1}\frac{\Theta\left(M\right)}{\left(\rho_{S} \right)^{\Delta}}\sum\limits_{p=0}^{\Delta-1}\frac{\left(\Delta-1\right)!}{p!} \left(\frac{\beta-\delta}{\rho_{S}}\right)^{-\Delta+p}x^{p}e^{-\left(\frac{ \beta-\delta}{P_{S}}\right)x}. \tag{15}\]Regarding channels in ground, the PDF and CDF of \(\left\|\mathbf{h}_{1}\right\|^{2}\) are given as respectively  
\[f_{\left\|\mathbf{h}_{i}\right\|^{2}}\left(x\right) =\frac{x^{N_{i}m_{i}-1}}{\Gamma\left(N_{i}m_{j}\right)\Lambda_{i}^ {N_{i}m_{j}}}e^{-\frac{x}{\Lambda_{j}}}, \tag{16}\] \[F_{\left\|\mathbf{h}_{i}\right\|^{2}}\left(x\right) =1-e^{-\frac{x}{\Lambda_{j}}}\sum_{n_{i}=0}^{N_{i}m_{i}-1}\frac{x ^{n_{i}}}{\Lambda_{i}^{n_{i}}n_{i}!}. \tag{17}\]