esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 On-Site Data Monitoring
### Analysis of Data Monitoring Results  
#### 6.2.1 Monitoring of Anchor (Bolt) Forces in the Return Air Roadway  
Each station was equipped with six stress sensors: one anchor cable was installed on the roof, and one anchor bolt was positioned there as well, one anchor cable was arranged on both sides, and one anchor bolt was arranged on both sides. Figure 16 displays the monitoring results.  
Monitoring results indicated that as the working face progressed, the stress on the bolt and anchor cable consistently increased. There was a significant rise in stress 40 m from working face. The peaks at the working face's reaching station were 90 kN and 300 kN, respectively. The monitoring station moved into the goaf area as the working face passed  
Figure 16: Support system emergency monitoring curve diagram. (**a**) Stress monitoring point No. 1; (**b**) Stress monitoring point No. 2; (**c**) Stress monitoring point No. 3.  
it, and strain on the anchor bolts (cables) progressively lessened. The stress on anchor cables in the deeper rock mass remained elevated due to the influence of overlying rock layers in the goat area, compared to when the working face was ahead. Within the 200 m monitored range, the anchor stress initially rose and subsequently declined. The advance support pressure affected an area extending approximately 40 m from the working face, while the rear goat influenced an area approximately 50 m behind it. The anchor (bolt) force remained within a reasonable range.
## 3 Materials and Methods
### Data Analysis  
The aim is to train an ANN model to predict the 'Action' taken given the other network features. The model is implemented in Python using the Keras library with the TensorFlow backend.  
The method used in the analysis can be outlined as follows:  
Data Preparation: The dataset is first loaded into a pandas DataFrame. The target variable 'Action' is separated from the input features. The input features are normalized using the StandardScaler from the sklearn library, which transforms the data such that their distribution will have a mean value of 0 and a standard deviation of 1. The target variable is label-encoded and then one-hot-encoded to transform into a binary class matrix for use with the categorical cross-entropy loss function during training.  
Data Splitting: The dataset is subjected to a partitioning procedure to create distinct training and testing subsets. Specifically, 80% of the dataset is allocated for training purposes, establishing the foundation for the model's learning. The remaining 20% is reserved for testing, providing an unbiased evaluation of the model's predictive capability on unseen data.
# Smart Healthcare Applications over 5G Networks: A Systematic Review
## 3 Results and Discussion
### Healthcare Benefits
#### 3.2.1 IoMT Devices
[38], is a teleoperated robot consisting of a console for the surgeon, which includes an image and control panel, two manipulators (right and left), and pedals for motion control. It also has a system for the patient, which includes a rotating head, a passive arm, and three slave arms. Similarly, a teleoperated robot used for diagnostic ultrasound of COVID-19 [58] consists of two systems: on the doctor's side, image monitors, a console, and a transducer to control the robotic arm; and on the patient's side, it has an US device, a 6-degree-of-freedom robotic arm equipped with transducers, and a force sensor. Lastly, MGIUS-R3 [59] is a robotic arm that is also equipped with an US device on the doctor's site. Additionally, the audio, video, and mainframe systems are located on both surgeon and patient sites. Continuing with IoMT devices, it is observed that the use of ultra-high-definition cameras was involved in 6.82% (\(n=3\)) of the systems. These are usually equipped inside ambulances. The main objective of these devices is to perform image and video transmissions in real time, in order to provide patient information to a hospital facility and a  
Figure 10: IoMT devices. physician so that remote care and guidance to paramedics can be provided during the process of transferring the patient to the hospital. In the ambulance system presented by [PERSON] et al. [31], through the process of transferring a patient to the hospital center, an ultrasound scan was performed with the use of US. The process was monitored by a remote doctor, who moved the camera to perform the whole ultrasound process. Wearable devices were involved in 6.82% of studies (\(n=3\)), as were the ultra-high-definition cameras. These types of devices, and body sensors, are used to collect biomedical signals (oxygen saturation level, blood pressure, body temperature, heart rate, and respiratory rate). Their use is involved in surveillance, continuous monitoring, and remote care systems. In addition, these types of sensors not only fulfill the function of signal collection, but are also devices developed to fulfill other types of tasks.
## 5 Scientific Novelty and Scientific and Practical Significance of the Research Results
### The Main Areas of Practical Application of the Results
#### 5.2.1 Development of Nonlinear Optics and Laser Technology
Crystals with the same double type of nonlinearity include other ferroelectric HBCs, such as triglycine sulfate (TGS) and Rochelle salt [1, 2, 3, 4]. In the mathematical description of these types of effects, it is relevant to use recurrent formulas for the optical refractive index and absorption of light by the sample material at ultra-high amplitudes of the electromagnetic field by analogy with recurrent expressions for complex amplitudes of relaxation modes of volume charge densities in a dielectric at anomalously large values of the parameter \(r\). Thus, the theoretical basis for mathematical modeling of nonlinear optical processes, within the framework of the quasi-classical theory of polarization, at a certain level, is substantiated in recurrent expressions for stationary complex amplitudes of volume charge distributions at \(r>>1\). To complete the nonlinear quasi-classical mathematical model of the interaction of coherent ultra-high-intensity radiation with matter, it is sufficient, in the long term, to marginally correct the nonlinear quasi-classical model of ion-relaxation polarization in ion-molecular dielectrics by replacing the variable homogeneous external electric field in the scheme of solutions of kinetic equations with the electric field of an electromagnetic wave, and the Poisson equation with the d'Alembert wave equation. In this case, at the mathematical level, results obtained will be analogous to the recurrent formulas used for large values of the parameter \(r\) in the theory of relaxation polarization. A rigorous quantum-mechanical approach to the question of the interaction of laser radiation with matter requires the use of the apparatus of the density matrix, calculated from the solutions of the non-stationary Liouville equation for activation centers (relaxers) excited by the action of laser radiation photons. Since, within the framework of this scientific article, we limit ourselves to the quasi-classical approximation in describing quantum effects in classical kinetic theory, calling it the quasi-classical kinetic theory of polarization, we will not elaborate on the features of the structure and properties of strict quantum-mechanical models of proton-relaxation polarization, which is the subject of a separate article. However, already at the quasi-classical level, the universality of the methods of quasi-classical kinetic theory has been substantiated, primarily both in the radio frequency range and in optical frequencies.
# Location-Based Deferred Broadcast for Ad-Hoc Named Data Networking
## 3 Location-Based Deferred Broadcast Scheme
### LBDB Forwarding Priority Timer Design  
As introduced in Section 2, the key part of designing a deferred broadcast scheme is the metric to indicate the forwarding priority. We consider two factors in calculating forwarding priority via the FP timer. On the one hand, the nodes farther away from the last hop should have higher forwarding priority so that the interest can be propagated faster with fewer hops. On the other hand, the nodes closer to data location should have higher forwarding priority thus the interest could have more chance to be forwarded to its desired area. To consider these two factors, we use a reference node, which is defined as the nearest possible geo-coordinate to the data location within the transmission range of the last hop [30]. For example, in Figure 2, node A receives an interest packet coming from node B and the name indicates data location is D. So R is the reference node. In our design, a node closer to the reference node has a shorter rebroadcast deferred timer because of the higher forwarding priority. We use the following equation to compute the timer:  
\[T_{rebroadcast}=T_{max}\frac{D_{R}}{2D_{max}}+T_{random} \tag{1}\]  
where \(T_{max}\) is the maximum deferred time, \(D_{max}\) is the transmission range and \(D_{R}\) indicates the distance to reference node. The value range of \(\frac{D_{R}}{2D_{max}}\) is from 0 to 1 so that the range of deferred time is from 0 to \(T_{max}\). A random CA timer \(T_{random}\) is appended to the equation to avoid packet collision when two calculated timers are close to each other.  
Both CA timer and FP timer play significant roles in the performance of a deferred broadcast scheme. We use both experiment and simulation to evaluate our proposed LBDB. To investigate the impact of CA timer, we first design an experiment running V-NDN code on real wireless testbed when FP timers are the same. Then, in simulation, we compare LBDB with other schemes and evaluate their performance in a vehicular environment.  
Figure 1: Deferred Timer.
## 4 Experimental Results
### Experimental Study 1
#### 4.1.4 Results Obtained Using the DenseNet201 Model  
The default image input size of the DenseNet201 model architecture is 224 \(\times\) 224 \(\times\) 3. Because the image dimensions in the dataset used in this study were 50 \(\times\) 50 \(\times\) 3, the DenseNet201 architecture was customized and retrained. The image input size in the DenseNet201 architecture was rearranged to 50 \(\times\) 50 \(\times\) 3. The classification layer of the model was replaced by a two-output sigmoid activation function. In addition, the weights of the model were initialized randomly, and the model was trained again on the dataset. The model parameters were configured as follows: Adam optimizer, BCE loss function, a learning rate of 0.001, 50 epochs, and a batch size of 200. The model trained for 50 epochs achieved an accuracy of 90.17%, precision of 67.20%, recall of 70.69%, and F1 score of 68.90%. Figure 8 demonstrates the accuracy and loss graph of the DenseNet201 model developed for experimental study 1.
# Ferrite-Loaded Spidron Fractal Loop VHF Antenna for UAV Applications
## 3 Experimental Verification
Figure 8: Simulated and measured radiation patterns of the proposed antenna at 60, 180, 300 MHz: (**a**) \(xz\)-plane; (**b**) \(yz\)-plane. Figure 9: Proposed antenna mounted on a UAV model: (**a**) 3-D view; (**b**) Bottom view. Simulated far-field radiation patterns of the proposed antenna mounted on the UAV on two planes (\(xz\)-plane and \(yz\)-plane) are depicted in Figure 11. This figure shows that radiation patterns are influenced less by the UAV structure at 60 MHz due to the longer wavelength. However, when the frequency is higher, the patterns are influenced more by the UAV structure. The \(xz\)-plane and \(yz\)-plane patterns are asymmetric and different from each other due to the shape of the fuselage, the wings of the UAV, and the feeding location. The pattern in the downward direction (\(90^{\circ}\leq\theta\leq 270^{\circ}\)) is directional and can receive communication signals at all downward directions.
## 5 Rate-Compatible IR-HARQ Transmission Scheme Based on PCPP Codes  
In this section, we design a RC IR-HARQ transmission scheme based on PCPP codes, to transmit multiple data blocks continuously over a time-varying channel. In our RC IR-HARQ transmission scheme, a channel capacity estimation is fed back to the sender after each PCPP coding block is successfully transmitted. Then, an optimal initial code-rate is determined by the sender for the next transmission. The optimization method for determining the coding parameters of each 1 st PCPP coding block is as follows.
## 4 Conclusions
Adapted and updated from [22]. of less than 72%. When the distance between the transmitter and the resource-allocating eNodeB was highest, the PDR of the LTE-D2D dropped to values as low as 25%. Furthermore, we put these results into the context of the most popular V2I services and their networking and connectivity requirements. Our results indicate that only the DRSC is fully able to support the V2I services with varying demands on the communication latency and message generation frequency in the investigated urban scenario. The LTE in the infrastructural mode is able to support low-frequency applications for communication perimeters of up to 600 m and even low-latency applications for traffic intensities below 1000 vehicles per hour. The LTE in D2D mode 3 is not suitable for longer-range V2I communication. However, it is worth noting here that the situation may change considerably with the deployment of 5G-based Vehicle-to-Everything (5G V2X) communication systems. These 5G V2X systems are expected to bring major improvements in link quality and throughput, as well as reduced latency. As a follow up, our aim is to benchmark 5G V2X against DSRC and LTE-V2X in a V2I scenario once the appropriate simulation framework for the corresponding simulation stack becomes available. Conceptualization, T.P. and P.P. ; methodology, T.P., P.P., and L.S. ; software, T.P. and L.S. ; validation, T.P., P.P., L.S., and M.D. ; formal analysis, T.P., P.P., L.S., and M.D. ; investigation, T.P., P.P., L.S., and M.D. ; resources, T.P., P.P., and L.S. ; data curation, T.P. and L.S. ; writing--original draft preparation, T.P., P.P., L.S., and M.D. ; writing--review and editing, T.P., P.P., L.S., and M.D. ; visualization, T.P. ; supervision, P.P. and M.D. ; project administration, P.P. and M.D. ; funding acquisition, M.D. All authors have read and agreed to the published version of the manuscript. This work was supported by the Slovak Research and Development Agency under the contract no. SK-IL-RD-18-005 ICT and the Smart Cars for Efficient Emergency Response and Traffic Management (SENECA) program. This publication was realized with the support of the Operational Program Integrated Infrastructure 2014-2020 through the project \"Value of travel time from an individual traveller's perspective\" (ITMS2014+ 313011T241), which was co-financed by the European Regional Development Fund. This publication was realized with the support of the Operational Program Integrated Infrastructure 2014-2020 through the project \"Innovative Solutions for Propulsion, Power, and Safety Components of Transport Vehicles\", code ITMS 313011V334, which was co-financed by the European Regional Development Fund. Not applicable. The simulation data is available upon request. The authors declare no conflict of interest.
# Detecting IoT Anomalies Using Fuzzy Subspace Clustering Algorithms
## 3 Proposed Methods  
The methods proposed in this article are a two-staged hybrid approach consisting of subspace generation and fuzzy clustering algorithms. In stage 1, the rough set-based approach is used to generate subspace. In stage 2, fuzzy clustering methods are employed to generate fuzzy clusters. The stage 1 of the proposed method is described as follows. Our dataset \(S=(U,\,A)\) is an information system consisting of both conditional and decision attributes. First of all, a data pre-processing techniques is applied to convert it to set-valued ordered information system. Then, a dominance relation, a nano topology and its basis, is generated. Then, the criterion reduction process is used to generate CORE (\(A\)) as a subset of \(A\). This way new information system \(E=(U,\,\text{CORE}\,(A))\subseteq\mathbb{S}\) is computed. The pseudocode of the algorithm for the criterion reduction is given below (Algorithm 1).  
```
Input. (\(U,\,A\)): the information system, where the attribute set \(A\) is divided into C-conditional attributes and \(D\)-decision attributes, consisting of \(n\) data instances. Output: Subspace of (\(U,\,A\)) Step 1. Generate a dominance relation \(R^{\geq}_{C}\) on \(U\) corresponding to \(C\) and \(X\subseteq U\). Step 2. Generate the Nano topology \(\tau^{\geq}_{C}(X)\) and its basis \(\hat{\rho}^{\geq}_{C}(X)\) Step 3. for each \(x\in C\), find \(\tau^{\geq}_{C-\{x\}}(X)\,\,\) and \(\hat{\rho}^{\geq}_{C-\{x\}}(X)\,\,\)  Step 4. if (\(\hat{\rho}^{\geq}_{C}(X)=\hat{\rho}^{\geq}_{C-\{x\}}(X)\)) Step 5.  then drop \(x\) from \(C\), Step 6. else form criterion reduction Step 7.  end for Step 8. generate CORE(C) = \(\cap\)\(\cap\)criterion reductions Step 9. Generate subspace of the given information system.
```  
**Algorithm 1**Nano Topology-based Subspace Generation  
The above algorithm supplies the CORE of the attribute set by removing insignificant attributes which gives us a subspace \(E=(U,\,\text{CORE}\,(A))\) of the given information system \(S=(U,\,A)\). Since, the nano topology is generated for the generation of CORE. The above algorithm is named the nano topology-based subspace generation algorithm. Then, stage 2 of the method starts. For stage 2, different variations of fuzzy clustering algorithms are explored. The algorithms are described as follows.
# Comparison of EPIC-Simulated and MODIS-Derived Leaf Area Index (LAI) across Multiple Spatial Scales
## 3 Results
### Comparison 2: 12.0 km--EPIC LAI v. MODIS (Mc5/Mc6) LAI
#### 3.2.2 EPIC Modeled LAI 12.0 km  
Calibrated LAI values from our prior study were used to model LAI values for all four research sites. For example, calibrated pgnut hickory LAI was used in place of the generic \"Mixed Upland Hardwood\" FIA forest stand type for the Appomattex, Fairystone, and Hertford sites. Apple was used as a surrogate for sassafras and persimmon on the Fairystone site. The Umstead site was unique in that it has been relatively undisturbed from harvest operations over the last 100 years. Therefore, maturity dates of loblolly pine were adjusted from 55 years to 90 years. We have provided an example of EPIC-modeled LAI on the Hertford research site (see Appendix A, Figure A1).
## Processing Setup and Description
### Uncertainty Modeling
#### 3.5.3 GNSS Uncertainty Estimates  
The quantification of systematic and random uncertainty estimates for the GNSS satellites is based on the exploitation of existing analyses from the GNSS expert community. For the random uncertainty we introduce a fixed conservative bound estimate of 1 cm, adapted from the analysis of formal errors of GPS [100]. The systematic uncertainty is specified with a 3 cm conservative bound estimate, denoted \(u_{\text{CB}}^{\text{Tx}}\) hereafter, established from SLR investigations [101; 102] and long-arc analysis [62; 63]. Although only two GPS spacecraft were equipped with an LRR, their position quality is expected to be of similar level as they are less influenced by gravitational forces through their higher orbit. In addition to the conservative threshold values the accuracy codes (AccCode) given in the header of the SP3 orbit files ([[ftp://igs.org/pub/data/format/sp3c.txt](ftp://igs.org/pub/data/format/sp3c.txt)]([ftp://igs.org/pub/data/format/sp3c.txt](ftp://igs.org/pub/data/format/sp3c.txt))) serve as a daily indicator of formal  
Figure 2: Propagated random uncertainty estimates (based on propagating standard errors) from Bernese for Metop-A on an example day (1 July, 2013), for position (**left**) and velocity (**right**), for the radial, along-track, and cross-track components. Coverage factor of 2 applied.  
accuracy and may increase the systematic uncertainty estimate for the individual GNSS satellites as follows:  
\[u_{s}^{\text{Tx}}=\max\left(u_{\text{CB}}^{\text{Tx}},\ 2^{\text{AccCode}} \right), \tag{4}\]  
where the accuracy codes map as an integer base-2 logarithm: \(\text{AccCode}=\log_{2}(\sigma)\)[60].  
Analogous to the combined uncertainty of the LEO, the systematic and random uncertainty estimates for the GNSS are combined in a RMS sense (Equation (2)). The GPS satellite orbit uncertainty estimates depicted in Figure 3 suggest a factor around 1/5000 from position to velocity for the random errors. In order to provide also some room for unmodeled systematic effects, the corresponding uncertainty estimates for velocity are conservatively computed with a conversion factor of 1/3000 from GNSS position uncertainty estimates (in units cm) to velocity uncertainty estimates (in units mm/s).
## 3 Results and Discussion
### Validation of Lifetime Data Sets
#### 3.1.2 Calculation and Verification of Lifetime
\begin{table}
\begin{tabular}{c c c c c c} \hline
**No.** & **\(\mathbf{n_{1}}\) (rpm)** & **\(\mathbf{P_{3}}\) (kPa)** & **\(\mathbf{P_{34}}\) (kPa)** & **\(\mathbf{Qm_{t}}\) (kg/s)** & **\(\mathbf{T_{34}}\) (K)** & **EOH Increments (h)** \\ \hline
1 & 0.047599 & 0.002423 & 0.000000 & 0.123522 & 0.40163 & 0.000000 \\
2 & 0.000000 & 0.000000 & 0.010254 & 0.714323 & 0.266652 & 0.125527 \\
3 & 0.646053 & 0.211596 & 0.103201 & 0.761335 & 0.059797 & 0.221033 \\
4 & 0.667572 & 0.215596 & 0.122717 & 0.855482 & 0.141488 & 0.264292 \\
5 & 0.667456 & 0.21525 & 0.126121 & 0.118456 & 0.134114 & 0.329359 \\
6 & 0.969942 & 0.982835 & 0.970026 & 0.488695 & 0.000000 & 0.469595 \\
7 & 0.907062 & 0.936506 & 0.91264 & 0.114499 & 0.377102 & 0.487253 \\
8 & 0.927423 & 0.900578 & 0.958006 & 0.00000 & 0.67072 & 0.642145 \\
9 & 0.976506 & 0.905042 & 0.916885 & 0.106072 & 0.706214 & 0.707132 \\
10 & 1.000000 & 1.000000 & 1.000000 & 1.000000 & 1.000000 & 0.880289 \\
11 & 0.992645 & 0.990454 & 0.984139 & 0.739875 & 0.897642 & 0.952550 \\
12 & 0.992288 & 0.988131 & 0.980131 & 0.701384 & 0.884362 & 1.000000 \\ \hline \end{tabular}
\end{table} 
Table 3: The EOH increment obtained from finite element calculations.
# Adhesion Promoters in Bituminous Road Materials: A Review
## 3 Survey of Types of Adhesion Promoters Currently Used in the Asphalt Industry and Their Functions
[50] who have followed a fracture mechanics approach to quantify the adhesion between the modified bitumens and the aggregates in selected asphalt mixtures. The best result was achieved for modification with OSAPs, confirming that the addition of organosilane-based AP is a useful method to bridge the interface between the organic bitumen and the inorganic mineral aggregate, especially for the silica-rich granites. A class of compounds that contain both basic (ammine) and acid (phosphate) polar residues can be represented by natural phospholipids such as, for example, phosphatidylcholine choline, phosphatidylcholine inositol, phosphatidylcholine ethanolamine, and phosphatidic acid [51]. Figure 3: Surface aggregate areas covered with (**A**) untreated bitumen and (**B**) pre-blended bitumen with organosilanes-based adhesion promoters (OSAPs) to a final concentration of 0.01 wt. %. Reproduced with permission from [40], Elsevier, 2016. They can be efficiently dissolved in hydrocarbons due to their significant lipophilic properties [52; 53]. Motivated by the increasing need to seek environmental friendly low cost and non-toxic additives, a systematic investigation on the potential effects played by commercial Food Grade Phospholipids (FGPs) as APs for bitumen have recently been undertaken [54]. In particular, hot bitumen pre-blended with small amounts of FGPs added as a commercial liquid mixture has been made suitable to improve the percentage of bitumen coating retained after boiling and consequently decrease the contact angle between binder and tested stone. Similar results have been obtained for bitumens modified with slightly higher amounts of FGPs added as solid powdered form.