esa-sceva/satcom-chunk-collection huggingface dataset:
# Steady-State Response Analysis of an Uncertain Rotor Based on Chebyshev Orthogonal Polynomials
## 3 Rotor System  
Figure 3 illustrates the dynamics similarity tester for a turboshaft engine rotor with the discs and shafts constructed from the same material (40 CrMnSiA), which has an elastic modulus of 2.11 \(\times\) 10\({}^{11}\) N/m\({}^{2}\). The shaft has a length of 2.4 m and a diameter of 0.17 m. The masses of Disc 1, Disc 2, and Disc 3 are 3.3 kg, 5.83 kg, and 3.09 kg, respectively, with corresponding moments of inertia of 0.0079 kg\(\cdot\)m\({}^{2}\), 0.0247 kg\(\cdot\)m\({}^{2}\), and 0.0088 kg\(\cdot\)m\({}^{2}\). The damping coefficient is 200 Ns/m. Other relevant model parameters include support stiffness, unbalanced mass on each disc, and installation phase angles. The installation phase angle of the unbalanced mass denotes the angular positions of each disc at the time of installation. The arrangement of these phase angles directly impacts the relative positioning of the unbalanced masses, thereby influencing the system's vibration patterns and stability. Specific values and interpretations of these parameters in the example rotor system are presented in Table 2.  
The steady-state response of the rotor system is computed using the finite element method (FEM). For clarity, the detailed modeling process is presented in Appendix A, ensuring that the organization of this paper remains coherent. Using the established FEM, the first three critical speeds are identified as 1434 rpm, 1964 rpm, and 6923 rpm. The corresponding vibration mode shapes are depicted in Figure 4, in which the first and second modes represent pitching, while the third mode represents bending. Taking the steady-state response associated with Disc 1 as an example, the frequency response function for Disc 1 is obtained, as illustrated in Figure 5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Dimension** & **Description** \\ \hline \(k_{1}\) & 2.46 \(\times\) 10\({}^{5}\) N/m & Stiffness of Support 1 \\ \(k_{2}\) & 3.64 \(\times\) 10\({}^{5}\) N/m & Stiffness of Support 2 \\ \(u_{1}\) & 30 g\(\cdot\)cm & Mass unbalance on Disc 1 \\ \(u_{2}\) & 30 g\(\cdot\)cm & Mass unbalance on Disc 2 \\ \(u_{3}\) & 30 g\(\cdot\)cm & Mass unbalance on Disc 3 \\ \(\phi_{1}\) & 0\({}^{\circ}\) & Phase angle of unbalanced mass on Disc 2 relative to Disc 1 \\ \(\phi_{2}\) & 0\({}^{\circ}\) & Phase angle of unbalanced mass on Disc 3 relative to Disc 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model parameters.  
Figure 4: The first three vibration mode shapes of the example rotor: (**a**) 1 st mode shape, (**b**) 2 nd mode shape, and (**c**) 3 rd mode shape.  
Figure 3: The structure of the rotor system.
# UAV Dynamic Non-Terrestrial Transmission Channel Analysis Based on SSCM-RT Model
## 4 Spatial Consistency of Dynamic Channels
### SSCM-RT Dynamic Channel Simulation  
Figure 12 displays the path loss for the two motion models using the ray tracing model. Accordingly, it can be inferred that the ray tracing model presents spatial consistency in nature. The spatial consistency can be maintained even when the overall path loss is less than 3 dB. Therefore, the introduction of spatial consistency to the linear model, Gauss-Markov model, and random direction model allows for the continuous output of channel state information, which is helpful for the quantitative definition and description of the UAV channel in practical settings.  
Figure 11: Random direction model.  
Figure 12: _Cont._  
Given the above, the spatial consistency is introduced in the statistical channel part of the SSCM-RT model, which is applied to the Gauss-Markov model and random direction model. By characterizing the dynamic channel of the long-distance non-terrestrial network, the reliability of the SSCM-RT model is confirmed from the standpoint of path loss. Assume that \(d_{1}\) is 1900 m and the initial value of \(d_{2}\) is 100 m; the value of \(d_{2}\) varies with the motion process. The path loss under the Gauss-Markov model and random direction model are shown in Figure 13. The simulation findings show that the SSCM-RT hybrid model can maintain spatial consistency during the whole motion process, making it suited for dynamic scenarios with irregular distance changes. Therefore, in comparing the actual relative motion scenarios of a UAV, the SSCM-RT hybrid model has superior channel characterization than the SSCM model.  
Figure 12: The path loss of two motion models by using RT model. (a) Gauss–Markov model; (b) random direction model.  
Figure 13: _Cont._
# Triaxial Loading and Unloading Tests on Dry and Saturated Sandstone Specimens
## 2 Materials and Methods
### Test Scheme  
The triaxial tests including conventional triaxial compression tests (abbreviate to loading test) and unloading confining pressure tests (abbreviate to unloading test), were conducted by a servo-controlled material testing machine (MTS 815) (MTS Systems Corporation, Minnesota, USA) at laboratory of Central South University. Both of axial and circumferential strains can be recorded by extensometers.  
Each test includes two experimental groups of dry and saturated sandstones respectively. Thus, the specimens are divided into four groups of dry and saturated sandstones in different loading and unloading tests (see in Table 2). The initial confining pressures (\(\sigma_{3}^{0}\)) were set as 10, 20, 30, and 40 MPa, respectively. Meanwhile, the unloading point for the confining pressure in unloading tests was set as the axial stress reaching 80% of the corresponding triaxial compression strength (such as 0.8\(\sigma_{(10)}\) and so on). Then, the axial loading method was changed from displacement control (0.1 mm/min) to loading control (1.5 MPa/min) at unloading point. In general, four sets of specimens were prepared, i.e., TC-D, TC-S, TU-D, TU-S. Specimens of set TC-D and TC-S, with the dry and saturated state respectively, were used in the conventional triaxial compression test. Specimens of set TU-D and TU-S, with the dry and saturated state respectively, were used in the unloading confining pressure test. And the number of specimen set represents for the corresponding confining pressure. For example, as shown in Table 2, TU-D-10 represents the unloading confining pressure test of dry specimen under the confining pressure of 10 MPa.  
The detailed test procedures of the triaxial unloading test are listed as follows:  
Step 1: Apply hydrostatic pressure on the specimen to an initial confining pressure (\(\sigma_{3}^{0}\)).  
Step 2: Keep \(\sigma_{3}^{0}\), and increase axial stress to 80% of the corresponding triaxial compression strength by a displacement control method at 0.1 mm/min.  
Step 3: Reach the unloading point, change the axial stress loading method from displacement control to load control at the unloading point.  
Step 4: Increase the axial stress at 1.5 MPa/min and also reduce the confining pressure simultaneously by load control method at a specified unloading rate (3.0 MPa/min) until the failure of the rock specimen.
# Enhancing IoT Network Security Using Feature Selection for Intrusion Detection Systems
## 5 Discussion
### The Destination Port as a Key Feature in Port Scanning and Reconnaissance Attacks  
The correlation analysis from this study highlights the 'Destination Port (id.resp_p)' as a significant feature associated with port scanning and reconnaissance activities. This finding aligns with the nature of these attacks, in which malicious actors exploit open ports to identify vulnerabilities. By linking this feature to attack types, our results validate its importance in IoT network security.  
The 'Destination Port (id.resp_p)' is one of the key features used for recognizing attempts to access the service or open ports in an unauthorized manner, thereby playing a crucial role in identifying scanning attacks and various reconnaissance-type activities. Using the port scanning technique, a network of systems is systematically explored to identify several active services. The associated information can be collected and leveraged by various malicious actors, thereby locating various vulnerable systems in the network. To troubleshoot various network issues and maintain the security of the system, these methods are often employed by IT professionals [49]. The destination port is connected to the specific service or application that a network packet is trying to access, making it a significant parameter to monitor. Attempts to access the destination port in an unusual and unauthorized manner can be detected by security professionals, based on the analysis of usage patterns.  
The security of data transmission is one of the paramount goals in the domain of IoT systems, which frequently rely on power-line communication networks. The physical layers of security of networks can be compromised by the presence of an eavesdropper, but security has been enhanced, based on destination scheduling schemes that optimize the paths for data transmission and reduce the probability of interception. Potential vulnerabilities in IoT devices can be identified by continuously monitoring destination ports, which are most frequently open to exploitation. These destination ports and port scanning activities can be correlated to develop more robust security measures. Secrecy performance can be improved by controlling the impulsive-to-background-noise power ratio and arrival rate at receivers [50].  
In summary, the destination port is a critical feature for recognizing and mitigating several vulnerabilities in IoT systems. It provides insights into unauthorized attempts to access the network and provides guidance for effectively implementing security measures to protect against various port scanning attacks and several other reconnaissance activities.
## 4 Results  
### Performance of the Classification Model  
The RCNN classifier's performance across different categories is presented in Figure 3. The precision values for \"Course quality\", \"Instructor\", and \"Process\" were 76.46%, 69.37%, and 54.93%. Regarding recall, the top included \"Course quality\", \"Instructor\", and \"Learning resources\", with values of 75.41%, 71.73%, and 53.45%. Regarding the F1 score, \"Course quality\", \"Instructor\", and \"Learning resources\" achieved the highest scores of 75.93%, 70.53%, and 54.15%, respectively. For \"Course quality\" and \"Instructor\", 75.41% and 71.73% of records were classified accurately. To sum up, the RCNN classifier could identify course reviews regarding their topical categories such as \"Course quality\" and \"Instructor\". However, it performed relatively poorly in classifying course reviews associated with categories such as \"Platforms and tools\" and \"Relationship\".  
The trained RCNN model was used to predict labels for unannotated sentences. Examples of this automatic prediction process are as follows. For instance, when the sentence \"_the professors made the lessons lightshearted and understandable_\" was input into the RCNN classifier, the classifier analyzed its semantic content and predicted confidence levels for all categories. After the analysis, the label with the highest confidence was identified as the predicted category of the input sentence. Thus, the category \"Instructor\" achieved a probability of 1.0, reflecting its relevance to the category \"Instructor\". Similarly, the prediction result for the sentence \"_a lot of the exercises in this course are about mathematical induction, which is an extremely important skill in university mathematics_\" indicates its relevance to the category \"Assessment\" with a probability of 1.0. The prediction result for another sentence, \"_the only thing that I could not use was the voice recorder_\", shows its relevance to the category \"Platforms and tools\" with a probability of 1.0. By using the RCNN classifier, a final data corpus with complete labels for each sentence was obtained. The distribution of different topics in the data corpus is presented in Table 4. The prevalence of different topics varied a lot. For example, the category \"Course quality\" had the most sentences (i.e., 105,130, with a proportion of 56.30%), whereas \"Process\" had the least (i.e., 3165, 1.69%).
## 4 Literature Review on DAS+PRS
### Feature Extraction
#### 4.1.2 Frequency Domain-Based Feature Extraction Methods  
Most of the works that employed frequency domain-based feature extractors generate estimations related to the signal energy calculated from the FFT or estimations from the FFT itself. For example, in [15; 16] the authors first computed the FFT of the acquired signals, and then the power spectral density (PSD) was calculated (the first 12 values of the PSD form each feature vector).  
In [46], the FFT of the recorded signals is first computed. Then, the total energy, the energy of low frequency, the peak value, and the mean value of the spectrum were computed. Finally, the energy ratio of a low frequency to total energy, the total energy, and the ratio of peak value to mean value were calculated to build a 3-dimensional feature vector for each recorded signal.  
The authors of [45] also employed an FFT-based feature extraction. First, the FFT is computed and then, the frequency space is divided into 10 bins to reduce the number of features. Next, the coefficients that fed in each bin are summed and normalized by the sum of all the coefficients. After that, PCA was applied for dimensionality reduction to build each 2-dimensional feature vector.  
Other approaches directly calculate the energy in frequency bands. For example, the authors of [22; 23] employed energy in frequency bands as features in the system. In the latter, two signal normalization methods were applied to deal with the signal degradation issue when the distance between the sensor and the recording scenario significantly varies. In [22], the authors normalize the signal according to the position of the recording, and in [23] the signal is normalized according to the energy of the signal in the high frequencies (from 100 Hz). The authors of [25] employed the energy in frequency bands as features, and applied the normalization based on the energy of the signal in the high frequencies presented in [23]. In [24] feature-level contextual information is added to the feature vector obtained in [23], from a neural network-based approach and employing the normalization based on the energy of the signal in the high frequencies. This approach is based on discriminatively-trained multi-layer perceptrons (MLPs) from feature vectors that spread different temporal contexts (short, medium, and long). These feature vectors, which form the input to the MLPs, comprise the energy in frequency bands. Then, the MLPs produce for each original feature vector a set of posterior probabilities that contain the probability given by the MLP to each class (the so-called _Tandem Features_[70]). This set of posterior probabilities is then added to the original feature vectors to form the contextual feature vectors, leading to significant improvements as compared to the baseline system.  
As an alternative, in [3; 28; 29; 31] the phase of the recorded signal is extracted as a feature, and the authors of [19] employed singular spectrum analysis for feature extraction.
# An Energy Efficient Routing Approach for IoT Enabled Underwater WSNs in Smart Cities
## 2 Literature Review
### Cluster-1 Protocols Holding Homogeneous Energy
In the ACE protocol, the Bit Error Rate (BER) is more than 50%. A packet is dropped when it has no neighbor to send information towards the sink. By having single and double retransmission in ACE results in less packet drop. LEACH [47; 48], is a primary known clustering based protocol. The primary goal of LEACH is to introduce local cluster based communication by reducing the global transformation of data packets. A threshold value is considered and an arbitrary number is generated by all the nodes upon which selection of CHs depends. In the case that the created arbitrary number is less when comparing it to the value of the considered threshold, at that moment the particular node is nominated as CH or vise versa. However, in this way, CHs are randomly selected due to which variation in size of clusters is introduced. These variations directly affect the node(s) energy level harshly and, thus, network lifetime decreases. The author's goal in the proposed protocol TEEN [49] is to detect the environment and react at the exact instant, when the desired information is sensed. It also selects CHs and checks for threshold value as well as the status of previous rounds, like LEACH. However, during the transformation of data packets, unlike LEACH, a condition is required to be satisfied in which hard and soft thresholds are defined. The sensor node forwards for the first time when the sensed value is total up to the hard threshold value. However, in a case to transmit for the second time, the sensed value should be larger than the hard threshold and should be either larger or equal to the soft threshold value. Like LEACH, energy is vigorously depleted here and, as a result, network throughput decreases sharply.
# Detection of Broken Bars in Induction Motors Using Histogram Analysis of Current Signals
## 2 Proposed Methodology
### Experimental Test Bench  
An induction motor is an electromagnetic machine with intertwined electrical and magnetic circuits that converts the electrical current through the stator windings into mechanical energy. This energy is produced due to the flux of current where a rotating magnetic field is produced [23]. The presence of broken bars produces a disturbance in  
Figure 1: Proposed methodology for HLT, 1 BRB, and 2 BRB fault classification.  
the rotor's magnetic field, intensifying torque modulation and vibrations within the motor. The sidebars also suffer a progressive deterioration due to the tension generated by motor bar fractures [24]. Thus, the effect of broken rotor bars can be detected through the current signals emitted by the motor power supply or by vibrational signals. Some approaches for motor fault detection consider both kinds of signals. In this work, only one current phase is processed and analyzed for detecting the motor's 1 BRB, 2 BRB, or HLT condition.  
Figure 2 shows the test bench used to acquire the current signals for validating the proposed methodology. The acquisition module uses a WEG 00136 APE48T two poles three-phase induction motor of 1 hp, 28 bars, and 220 V AC power supply at 60 Hz. Motor initiation is controlled by a relay that synchronizes data acquisition with the ignition switch. The data acquisition module (DAS) uses a phase of the current signal acquired by a Fluke Model i200s AC clamp with a 16-bit analog-to-digital converter (ADS/809) from Texas Instruments Incorporated. The instrumentation system provides a sample rate of 1.5 kHz.  
Broken rotor bars effects are observed in the current signal acquired \(S(n)\) through the harmonics that alter the uniformity. Therefore, the current signal can be statistically analyzed to detect broken bars anomalies. Sampled signals are below the tenth harmonic, allowing variations detection within the signal. Thirty signals were acquired in one of the motor phases for each fault and health state with 4096 samples. Based on the plots shown in Figure 3, the amplitude of the signal increases for fewer samples in 2 BRB like shows in Figure 3c than for the rest of the cases. This increment results from a greater demand for current to the power system. The amplitude variation of the current signal allows statistical analysis for detecting unusual patterns in the motor state. The acquired signal is conditioned by analog-to-digital (A/D) conversion, corresponding to the 16-bit format in the DAS. A bank of 90 tests was obtained for signal conditioning, 30 for each motor state (HLT, 1 BRB, 2 BRB). The broken bars in the motor were artificially produced by making circular holes of 7.938 mm diameter to cause partial fractures of the bar illustrated in Figure 4. The rotor part is shown for extracting the HLT, 1 BRB, and 2 BRB samples.
# The Role of Delays in the Performance of Blasting
## 5 Conclusions  
The present research aimed to study and understand the effect of initiation delays on the outcome of bench blasting at full scale. To achieve this, field tests were conducted, with all variables except the number of delays kept constant, including the rock mass (due to the proximity of the blasts). Operational KPIs were used to quantify the blast results.  
The parameters that primarily characterized the results obtained during this research were the distance between simultaneous holes and specific priming, defined as the density of delays per unit of blasted rock.  
Analysis of the results showed that increasing the number of delays in the initiation sequence per unit of blasted rock led to finer fragmentation, reducing the amount of fines and secondary breaking to the point of eliminating it.  
The analysis suggests that production holes behave similarly to contour holes when detonated simultaneously in proximity: It induced cooperation among charges, creating a shearing effect that directed the explosive energy to work along the inter-axis line between the holes. This produced fines along the shear surface and coarse fragmentation in the rest of the blasted volume. Conversely, a greater distance between simultaneous holes promoted breakage along the line of least resistance (the burden), leading to smaller fragments instead of shearing and consequently reducing the amount of fines. The proximity of simultaneous holes resulted in a form of dust-and-boulder behavior. From a safety perspective, avoiding cooperation among charges also reduces flyrock.  
Conceptualization, J.S.; methodology, J.S. and M.C.; software, J.S.; validation, J.S., S.V., T.M. and M.C.; investigation, J.S., T.M. and M.C.; resources, J.S., T.M. and M.C.; data curation, J.S. and S.V.; writing--original draft preparation, J.S. and S.V.; writing--review and editing, J.S. and S.V.; visualization, J.S. and S.V.; supervision, J.S.; project administration, T.M.; funding acquisition, J.S., T.M. and M.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by CNPQ, grant number 400417/2014-6, and MSCA-RISE, grant number 823948.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
Sociadek Extativa Dolomia Ltd.a., Taubate, Brazil, allowed all the fieldwork.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.