esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Nonstationary Processes in Drill Rigs on the Durability of Structural Elements
## 7 Conclusions  
1. The transient modes of a drilling rig should be analyzed using a continuum-discrete model of a mechanical system of variable structure, based on the joint integration of equations of motion and nonlinear differential equations describing electromagnetic phenomena in electric motors. This makes it possible to ensure the required accuracy of calculations for the strength of structural elements and to predict the durability of heavily loaded parts and assemblies. The efficiency of the analysis is enhanced by the developed finite-difference algorithm for calculating wave phenomena in a stepped drill pipe string.
2. Nonstationary modes of operation of a drilling rig are accompanied by intense mechanical vibrations which significantly affect the operating conditions of the friction device, shaft lines, hoisting rope, drill pipe string, mast, etc. Dynamic phenomena must be taken into account both at the design stage of drilling rigs for a reasonable selection of safety margins and during their operation to justify rational operating modes and increase the reliability of drive systems and support structures.
3. Based on the time dependences of the force in the rope, stress spectra in the material of the crown block axis were constructed for all cases of lifting a pipe column with the number of candles from the maximum to one. Using the AFGROW program, the endurance of the axle made of X46 steel was calculated. It is shown that the number of cycles of complete lifting of the drill string from the well before the destruction of the crown block axis is significantly affected by the maximum length of the string and its running weight. The permissible crack size decreases significantly with an increase in the maximum length and diameter of the string. The number of cycles of complete lifting of the column from the well before the crown block axis is destroyed is quite sufficient, which is explained by the relatively low load of the lifting system when lifting parts of the column of short length.  
Figure 9: Influence of the length of drill string on the number of complete lifts, which causes destruction of the crown block axis.  
**Author Contributions:** Conceptualization, Y.K. and Z.B.; methodology, Y.B., A.B. and J.S.; validation, Y.K., V.V. and J.S.; formal analysis, Z.B., J.S. and Y.B.; investigation, Y.K., Y.B., A.B., V.V., J.S. and Z.B.; supervision, Y.K., Y.B., J.S. and A.B.; writing--original draft preparation, Y.K., Y.B., A.B., V.V., J.S. and Z.B.; writing--review and editing, Y.K., Y.B., A.B. and J.S.; visualization, Y.K., A.B., V.V. and Z.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
### Our Contributions  
This study proposed an intrusion detection and classification system that can detect and classify the zero-day attacks of common IoT malicious traffic using machine learning models utilizing the sovereignty of Nvidia-Quad GPUs. Specifically, our model distinguishes between normal and anomaly network activity. Furthermore, it classifies network traffic into five categories: normal, Mirai attack, DoS attack, Scan attack, and MITM attack. Five supervised machine learning models, named Shallow Neural Networks (SNNs), Decision Trees (DT), Bagged Tree (BT), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM), were implemented to detect and classify network activity in an IoT system. In addition, we have applied different data preprocessing and feature engineering processes to increase the prediction accuracy of the aforementioned machine learning models. As a result, the accuracy rates for all models have scored extremely high ratios rates between 99.40% to 100%. Such accuracy scores have outperformed the performance of all other existing models. The main contributions of this research can be summarized as follows:  
* We present a comprehensive intrusion detection and classification system that can identify and classify the IoT traffic of an IoTID20 dataset into binary classes (normal and anomaly) or five classes (normal, Mirai attack, DoS attack, Scan attack, and MITM attack). In addition, we stipulate an illuminated depiction of our system modules and the machine learning algorithms.  
Figure 2: Typical NIDS architecture.  
* We provide an extensive feature engineering and data preprocessing framework that significantly improves the system performance evaluation. In addition, we provide a thorough development, validation environment, configurations, and extensive simulation results to better perceive the proposed solution methodology. The system has been evaluated using standard performance indicators of machine learning models such as confusion matrix, accuracy, precision, recall, F-score, and specificity.
* We compare our findings with other related state-of-the-art works, machine-learning-based intrusion detection systems (ML-IDSs), and intrusion classification systems (ML-ICSs) employing the same dataset. We show that our proposed system is superior.
# Improved Algorithm to Detect Clandestine Airstrips in Amazon RainForest
## 2 Materials and Methods
### Modified Algorithm  
Based on the algorithm presented by the authors of [33], it was observed that despite the high recall rate achieved, the number of false positive predictions was relatively high compared to the best result presented. To address this issue, several improvements were introduced in the fourth step of the algorithm. Firstly, the Normalized Difference Water Index (NDWI) [40] is calculated for each pixel of the generated segment using the original image bands, without any prior processing. Since, in production, the algorithm receives the raw satellite image without the preprocessing steps mentioned earlier, it is possible to retrieve these data. This step checks if any pixel in the image represents water bodies, and if so, the cutout is discarded. This is crucial because the Amazon region contains numerous water bodies, which can, depending on the cutout, resemble the shapes of landing strips. Additionally, when the minimum bounding box of the generated segment is calculated, the Circularity Ratio (CR) is also computed. If the CR is greater than 0.1, the cutout is discarded--this threshold was determined empirically through extensive testing. Furthermore, after calculating the segment's centroid, this location is checked for its proximity to any federal or state highway [41]. If the distance is less than 50 m, the location is discarded. This is an important step to eliminate potential false positives, as the cutout has only regional context and not a complete view of the entire scene. Highways often have shapes that closely resemble landing strips. To improve the perform in terms of computational time of the algorithm, the entire process described above was parallelized using 4 threads. This means that 4 scenes areprocessed in parallel, with each scene going through the mentioned steps. The number of threads was chosen to be the highest value that would not cause memory issues on the GPU. Additionally, a new step was added to the algorithm after the generation of the locations. This step involves obtaining images with dimensions of \(2\times 2\) km around all the generated locations. With these images, a new classification step is performed using a different classifier from the previous one. The reason for this new step is that, in the initial stage, due to the nature of the search conducted, the targets were unlikely to be centered, which could confuse the algorithm and result in a high number of false positives. However, with the locations of the predictions, it is now possible to have images with the targets centered, which enhances this classification step. In Algorithm 2, the modified algorithm is presented.
# Forecasting Economy-Related Data Utilizing Weight-Constrained Recurrent Neural Networks
## 4 Experimental Results
### Performance Evaluation of WCRNN against State-of-the-Art Neural Network Training Algorithms
#### 4.1.3 Banknote Authentication Problem  
The data for this classification problem were extracted from images taken from genuine and forged banknote-like specimens. For digitization, an industrial camera usually used for print inspection was used. The final images had \(400\times 400\) pixels. Due to the object lens and distance to the investigated object, gray-scale pictures with a resolution of about 660 dpi were gained. The wavelet transform tool was used to extract features from images. The network architectures consisted of 2 hidden layers with 8 and 4 neurons each and an output layer of 2 neurons, while the error goal was set to \(E_{G}\leq 0.01\) within the limit of 2000 epochs.  
Figure 3 illustrates the performance profiles regarding the banknote authentication classification problem. It is worth noticing that WCRNN\({}_{2}\) exhibited the highest probability of being the optimal solver, significantly outperforming all other training algorithms, followed by WCRNN\({}_{1}\). More specifically, WCRNN\({}_{1}\) and WCRNN\({}_{2}\) reported 30% and 54% of simulations with the highest \(F_{1}\)-score, respectively; while the state-of-the-art training algorithms Rprop, LM, and SCG presented 24%, 8%, and 12%, respectively. Furthermore, WCRNN\({}_{1}\) and WCRNN\({}_{2}\) reported 38% and 60% of simulations with the highest classification accuracy, respectively; while the state-of-the-art training algorithms Rprop, LM, and SCG presented 30%, 10%, and 14%, respectively. Summarizing, we conclude that the application of the bounds on the weights of the RNNs increased the overall classification accuracy; however, in case the bounds are too tight, this will not substantially benefit the classification performance.
# 3D-printed super-Wideband Spidron Fractal Cube Antenna with Laminated Copper
## 4 Conclusions  
A 3D-printed SWB Spidron fractal cube antenna is designed using 3D printing technology. To ensure the SWB characteristics, a self-complementary structure constructed using a Spidron fractal configuration is implemented on each face of a 3D frame, with the conductor part created using copper tape. The proposed antenna is excited by a tapered microstrip balun. The measured \(-10\) dB reflection ratio bandwidth is 34.9:1 (0.44-15.38 GHz). The measured peak gain ranges from 3.42 to 9.29 dBi within the operating frequency band. In addition, the radiation patterns are nearly omnidirectional at every operating frequency. Therefore, the proposed antenna can be feasibly applied in areas which require SWB characteristics.  
This work was supported by the research fund of Signal Intelligence Research Center supervised by Defense Acquisition Program Administration and Agency for Defense Development of Korea.  
The presented work was carried out in collaboration of all authors. [PERSON] performed the simulations. [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] participated to the conception, fabrication and experiment. [PERSON] wrote the paper which was edited by all co-authors.  
The authors declare no conflict of interest.
# Experimental Characterization of the Capacitively Coupled RF-Plasma Thruster
## 2 Materials and Methods
### Experimental Setup
#### 2.2.1 RF Generator  
For powering the thruster an in-house made radiofrequency generator based on the electron valve SRS4453 was used. The generator was operated at constant power delivering 20 W to the thruster. The ignition threshold for the discharge was 7 W. The generator was able to perform a frequency self-adjustment within a certain range. All tests were conducted at approximately 80 MHz of oscillation frequency. To match the radiofrequency generator to the load, an MFJ-989D load tuner was used. Both the RF generator and the matching network were installed outside the vacuum chamber. The overall electrical scheme of thruster connection is depicted in Figure 2.  
Power transmitted to the thruster was measured by a power meter built into the matching network, and crosschecked by Rogowski coil current measurements as well as direct voltage measurements at the thruster input. Similar power levels should be provided by a flight-ready device (RFG) as well, but such a device does currently not exist for a CCP and needs to be carefully designed.  
Typically, current flight-ready radiofrequency generators generate power-matching to the plasma of a thruster by adjusting the frequency. Unfortunately, most of them are designed for inductively coupled discharge, and have embedded capacities to \"prematch\" it to the thruster it was built for. That means, that in our case of a capacitive thruster, a proper RFG would require additional inductances but those are also a source of heat losses,  
Figure 2: Electrical scheme of thruster.  
and their installation close to the RFG on the spacecraft could lead to overheating of crucial components. Hence more work is needed in this field to enable the C-STAR to become a space-qualified device.
# DNS-IdM: A Blockchain Identity Management System to Secure Personal Data Sharing in a Network
## 8 Conclusions  
Centralized identity management has long served as a secure entry point for digital services. However, as modern-day digital services tend to bank on identity attributes for user authentication and authorization, conventional identity management systems lose their efficacy and practicality in leveraging service providers to make service provisioning decisions and helping users to make validated identity claims.  
In this paper, we critically analysed blockchain identity services, namely uPort, Sovrin, and ShoCard, revealing drawbacks to meet the needs of modern-day digital services. To overcome the limitations and weaknesses of identity attributes: persistence, request, and verification, in existing identity management systems, we proposed DNS-IdM. The proposed system follows a DNS-like approach that enables users and service providers to make identity attribute claims and verification using real-world identity attribute benefactors. The use of permissioned and permissionless blockchains, along with smart contracts enabled the secure and trustworthy management of identities. The experiment demonstrated the efficacy of DNS-IdM for real working applications with amicable overhead and security. The experiments illustrated that service providers can request identity attributes directly from benefactors, through smart contracts, and using blockchain transactions, users can manage and trace their identities.  
For future work, we will explore the application of seamless attribute requests and service-based attribute validation. This will enable users to make use of all possible real-world identity attribute benefactors and also ensure the service provider can only request from those attributes that are needed for provisioning a specific service. In addition to this, we will also identify the facilitators and barriers for blockchain-based identity management services in developing compliance with digital standards.  
Writing--original draft, J.A.K., S.S., H.M.-G., Z.P. and K.D.  
This research received no external funding.  
**Conflicts of Interest:** Authors declare no conflict of interest.  
Figure 9: The scalability of the registration, DNS, and attribute functions with the increasing iterations.
###### Abstract  
Background: Shoulder injuries in judo are common as the falling player (uke) lands. Two throws implicated in shoulder injury are tai-otoshi and morote-seoi-nage. Kinematic investigation can provide insight into possible shoulder injury mechanisms and allow for appropriate preventative measures to be suggested. We used two-dimensional (2D) video analysis to measure and compare: (a) the peak acceleration and (b) the peak velocity of uke's shoulder when tai-otoshi and morote-seoi-nage were performed by elite adult judoka. Methods: Eight male participants were recruited from the Budokwai Judo Club in London, UK. The mean age, height, and weight of participants were 25.4 \(\pm\) 5.2 years (18-34), 1.7 \(\pm\) 0.0 m (1.7-1.8), and 75.0 \(\pm\) 5.2 kg (66-80), respectively. Throws were recorded using an iPhone 65 camera and uploaded onto Kinovea for subsequent processing. Results: The peak acceleration (m/s\({}^{2}\)) was greater in tai-otoshi (71.6 \(\pm\) 12.4) compared to morote-seoi-nage (67.9 \(\pm\) 9.9), although this was statistically insignificant. The peak velocity (m/s) was significantly greater (\(p\) = 0.030) in tai-otoshi (5.1 \(\pm\) 0.8) than in morote-seoi-nage (4.5 \(\pm\) 0.6). Conclusions: A greater peak velocity in tai-otoshi suggests that the shoulder is subjected to increased loads upon impact. This may indicate that tai-otoshi carries a greater risk of shoulder injury. Nage-komi (repetitive throwing) practices in training should follow gradual loading principles--beginning with morote-seoi-nage, before moving to tai-otoshi, for example. We must be aware of any assumptions made in estimations of impact force, and future in vivo research is required to provide more definitive values. Meanwhile, coaches must continue to ensure that correct ukemi (breakfall) technique is displayed by athletes before performing throws.  
Keywords:injury; mechanism; prevention; acceleration; velocity +
Footnote †: journal: _Sci._
## 2 Materials and Methods
### Response Surfaces  
In order to preserve the freedom of adding and or removing constraints without the need of running an optimisation simulation, response surfaces are used in this research. The response surfaces were created using specialised software to perform multiple simulations, named Siemens Heeds. This software is used for design space exploration and therefore combines multiple software packages such as Siemens Simcenter 3D, Python, etc. Multiple simulations are performed using a variety of parameter sets. The output for every simulation is plotted and stored together with the parameter set and a surrogate surface is learned from this data. This surrogate will be used in order to predict the optimal parameter set based on the constraints. In order to create an accurate surrogate, 1000 simulationswere performed in the complete design space. The design exploration software used in this research has the ability to draft the response surface in several ways such as Least squares linear, Least squares quadratic, Kriging gaussian, Radial Basis Function (RBF) thin plate spline and one can even program a new approximation himself. In this research, RBF thin plate spline approximation is used.
# Investigation of Cement and Fly Ash on the Improvement of Fine Sand Soil
## 3 Results  
### Standard Proctor Test  
Standard proctor tests determine the maximum dry unit weight and optimum water content values of the samples. The test results, shown in Figure 2, compare samples with and without additives. The notation S represents the pure sand sample, S3C represents 3% cemented sand, S3C5 FA represents sand soil with 3% cement and 5% fly ash, S3C10 FA represents sand soil with 3% cement and 10% fly ash, and so on.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Code** & **S. Proctor Test** & **Direct Shear Test** & **CBR Test** & **Permeability Test** \\ \hline Sand & S & 6 & 6 & 6 & 2 \\ Sand + 3\% Cement & S3C & 6 & 6 & 4 & 2 \\ Sand + 3\% Cement + 5\% FA & S3C5 FA & 6 & 6 & 4 & 2 \\ Sand + 3\% Cement + 10\% FA & S3C10 FA & 6 & 6 & 4 & 2 \\ Sand + 3\% Cement + 15\% FA & S3C15 FA & 6 & 6 & 4 & 2 \\ Sand + 3\% Cement +20\% FA & S3C20 FA & 6 & 6 & 4 & 2 \\ Sand + 3\% Cement + 25\% FA & S3C25 FA & 6 & 6 & 4 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: A list of soil samples prepared for each experiment set.  
The figure shows that the optimum water content values, which range from 11.66% to 14.74%, increase with increasing amounts of fly ash and cement. This is because fly ash and cement require more water due to their finer particle size, than sand soil due to its hydration. [PERSON] and [PERSON] (2018) also indicate that the increase in fly ash content results in an increase in optimum water content. From the figure, the highest maximum dry unit weight value of 17.24 kN/m\({}^{3}\) is obtained in the S3C sample, and the lowest value of 15.84 kN/m\({}^{3}\) is obtained in the S3C25 FA sample. The addition of only 3% cement (S3C) led to a significant increase in the maximum dry unit weight value, whereas the inclusion of fly ash results in a drastic decrease in this value. This can be attributed to the higher specific gravity of cement compared to the lower specific gravity of fly ash, which is lower than that of sand. In their study, [PERSON] et al. (2019) also mentioned that for any particular percentage of fly ash addition in soil, the dry density decreases with an increase in fly ash content.
# Analysis of Electric Vehicles with an Economic Perspective for the Future Electric Market
## 2 Electric Vehicle Market
For example, for increasing the drive range, if we increase the battery capacity then the additional factors that need to be considered include the weight as well as the space required to accommodate the additional battery array:  
\[EV\ Range\propto Weight\ of\ the\ battery \tag{2}\] \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Battery** & **Drive Range** & **Top Speed** & **Price** \\  & **Capacity (kwh)** & **(km)** & **(kmh)** & **USD (EUR)** \\ \hline Audi e-tron & 95 & 402 & 199 & 69,975 \\ BMW i3 & 42.2 & 246 & 149 & 41,582 \\ Chevrolet Bolt EV & 60 & 383 & 144 & 34,258 \\ Fiat 500 e & 24 & 135 & 136 & 30,866 \\ Tesla Model X & 100 & 465 & 249 & 128,163 \\ VW e-Golf & 35.8 & 201 & 149 & 29,837 \\ Nissan LEAF & 40 & 241 & 144 & 28,055 \\ Jaguar iPACE & 90 & 376 & 199 & 65,017 \\ Kia Soul EV & 30 & 176 & - & 31,760 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of electric vehicles’ different parameters. The conclusion is that an optimum rating of the battery needs to be selected. The different parameters that affect the electric vehicle are shown in Figure 3. All of the above _EV_ parameters play crucial roles in the electric vehicles that are considered by customers.
## 1 Introduction
The last controller calculates a voltage control based on \(\mathcal{H}_{\infty}\) synthesis that uses the same information as the other controllers. Each controller's robustness is tested with the WTGs working in different scenarios (islanded no-load, islanded rated-load and DR-connected). A harmonic distortion analysis is carried out to validate the improvement of harmonic rejection and stability margins achieved using \(\mathcal{H}_{\infty}\)-based grid-forming controllers. This paper's main contribution is a methodology to design three different controllers, applying the \(\mathcal{H}_{\infty}\) technique for the WTG control of DR-based WPPs. The controller design mainly aims to operate the WTGs in different grid structures. This means that the system impedance changes due to the number of connected cables, WTGs or the DR. The structure of this paper includes the following sections. The system description used to validate the controllers is described in the second section, while the third section includes the controllers' design procedure. The fourth section shows an analysis of the designed controllers regarding performance, robustness and harmonic rejection. The conclusions are included in the last section.
# Using AI to Improve MIMO Antennas with SRR for 26 GHz by Analyzing Data
## 3 AI-Driven Optimization of MIMO Isolation Using SRRs
This process forms the foundation of our proposed AI-based framework for optimizing intelligent SRR-based isolation in millimeter-wave MIMO antennas.