esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Penetration Recognition Based on Voltage Signal in K-TIG Welding
## 3 Experimental Results and Discussion
### Analysis  
The welding power source employed in this study is an inverter arc welding power source that utilizes high-frequency inverter technology to convert a direct current source into a high-frequency alternating current source. The resulting alternating current is then stepped down through a transformer and rectified to obtain the desired direct current source. During this process, a certain amount of intermediate frequency alternating current component is generated, which coincides with the intermediate frequency alternating current signal collected at 36-37 kHz, owing to the switching frequency of the inverter being typically between 10 kHz and 100 kHz.  
In the event of a transition from complete joint penetration to incomplete joint penetration during welding, the unstable state of the weld pool is attributed to the inability to form a stable keyhole, leading to instability in the weld pool, such as the occurrence of weld pool rolling, which, in turn, causes instability in the arc state. In the inverter arc welding circuit, an electronic switch is utilized as a method of controlling the arc current, which can adjust the duty cycle and frequency of the arc current. When the arc is unstable, the electronic switch needs to adjust the arc current to maintain stability, causing changes in the intensity of the intermediate frequency component in the arc current.  
Existing theories have shown that changes in arc length lead to changes in arc resistance and conductivity, thereby affecting the waveform and spectrum of the arc current and voltage. Consequently, changes in the welding molten state result in changes in the power spectrum intensity of the intermediate frequency alternating current signal at 36-37 kHz.
## 3 The Proposed Intelligent Mixed Approach
### The Mixed Frequency Hopping Spread Spectrum and Smart Clustering Algorithm
#### 3.2.2 The Proposed Energy Efficient and Scalable Clustering and Network Coding Approach
The aggregated (encoded) packet \(A\) is obtained as follows,  
\[A=\sum_{i=1}^{m}C_{i}\times d_{i}. \tag{16}\]  
The key challenge associated with the network coding technique is that the decoding of the encoded packet is performed by Gaussian elimination and, therefore, requires the reception of at least \(m\) packets to occur. The decoding process on the BS side is performed by forming a \(m\times m\) coefficient matrix \(C_{m\times m}\) from the received \(m\) encoded packets and by reducing it using the row-echelon form. Considering that the linear independence of encoded packets, the set of \(m\) linearly independent equations is solved to retrieve each originally encoded packet. One key advantage of this proposed RLNC approach is that the choice of encoding coefficients form a GF, enhances the probability of linear independence among the encoded packets which then translates in high decoding reliability. The combination of the proposed dynamic frequency hoping and the energy-RSSI aware clustering with network coding approaches form the mixed proposed strategy for enhancing the overall network scalability, energy efficiency and latency of the network. A R2019b MATLAB version has been used to implement and evaluate the performance of the proposed NB-IoT Mixed Approach with Network Coding (N-MANC) in comparison the NB-IoT with the adaptive frequency hopping spread spectrum at PHY layer only (N-AFHSS) [55], then with respect to the NB-IoT with LEACH clustering at the network layer (N-LEACH) [56], and finally with the NB-IoT Mixed approach with no network coding (N-MANNC). The main performance comparison metrics considered are network scalability, energy efficiency, network reliability and network lifetime; as described in Section 4.
# Monitoring Students at the University: Design and Application of a Moodle Plugin
## 1 Introduction
### Characteristics of Teaching in Blended Learning Environments
The analysis of the records or logs will allow a detailed and individualized follow up of learning processes and the personal development of each student within a short time [20; 23; 24], aspects which are essential to achieve personalized learning [29]. Along these lines, programmed tutoring has in recent research [30] been shown to increase student motivation towards the subject matter. The benefits of this type of program are focused on the personalization of learning as a tool for the prevention of academic dropout [31]. As previously mentioned, an LMS registers student and teacher interactions and behaviour on the platform. In addition, the application of automatic learning techniques facilitates the detection of both successful and at-risk behavioural patterns. These patterns explain up to 52% of the variance in learning outcomes. The successful behaviours detected on the platform were [16; 32; 33]:  
1. The overall effort made to follow the subject. 2. The time the student spends on practical tasks. 3. The time spent by the student on tasks of a theoretical nature. 4. The effort invested in completing the questionnaires. 5. The time spent on forum discussions. 1. The quality of the discussions in the forums (type and length of messages). 2. The time spent analysing the feedback from the teacher. 3. The number of messages read in the forums. 6. The contribution to the creation of content. 7. How soon or how late the activities are completed and delivered. 8. The number of \"to visit\" links to reference information. Closely related to these results are those from other studies that point to both improved learning outcomes and higher levels of interaction on the platform through the personalized monitoring of students [33]. Likewise, information collected from the monitoring processes of one study predicted student learning outcomes by 61.3% and their patterns of behaviour on the platform by 56.1% [2]. In another, the use of hypermedia smart tutoring systems explained 60.4% of the variance in learning outcomes [34]. Similarly, in various other studies the use of PBL methodology predicted learning outcomes on quizzes by 42.3%, and the use of effective learning patterns by 74.2% (these data refer to the multiple correlation coefficient or determination coefficient (\(R^{2}\)), which represents the proportion of the variability explained by the independent variable with respect to the dependent ones), in addition to increasing student satisfaction with the teaching-learning process [3; 30; 31; 32; 33; 34].
## 6 Influence of Floods on GPS, GLONASS, and BDS CNR
### Influence of floods on BDS with 1 s Data Set  
As is well known, the BDS system consists of three orbital-type satellites, such as MEO, IGSO, and GEO satellites. Considering that the orbital repeat period of these three types is different, thus, they are analyzed separately in this section.
## 5 A Framework for SDN-SDR-Based Maritime Communications
### The SDN Approach
#### 5.2.1 SDN Background
It should be noted that SDN, while offering unprecedented flexibility and efficiency in network management, can introduce challenges to system security. The centralization of control in SDN architectures creates a potential single point of failure, making the network vulnerable to targeted attacks. Additionally, the programmability of SDN controllers opens up new attack vectors, as malicious actors may exploit vulnerabilities in the software-defined infrastructure to manipulate network behavior. Moreover, the separation of the control plane from the data plane in SDN can expose communication channels between these components to security risks, potentially leading to unauthorized access or data breaches. As organizations increasingly adopt SDN to enhance their network capabilities, it becomes imperative to address these security concerns through robust encryption, access controls, and continuous monitoring to safeguard the integrity and confidentiality of network operations.
# CDA-Net for Automatic Prostate Segmentation in MR Images
### Deep-Learning Methods for 3D MR Image Segmentation
#### 1.1.1 Methods with 2D Manners  
The first way treats each slice of the 3D MR images as a separate image and inputs it into the network to achieve pixel-wise segmentation. These methods, headed by FCN [14] and U-Net [15], are famous for their lightweight network architecture, so they are very suitable for medical image datasets with small-scale. For example, [PERSON] et al. [16] proposed a variant of FCN called PSNet, which used knowledge transfer from natural images to medical images to yield accurate segmentation of prostate MRI. [PERSON] et al. [17] introduced a residual jump connection and weighted attention mechanism based on U-Net to realize the segmentation of blood vessels in retinal images. Similarly, [PERSON] et al. [18] designed a bidirectional convolution dense convolution U-Net (BCDU-Net) and achieved excellent performance in retinal vessel segmentation, skin lesion segmentation, and lung segmentation. All the above methods show that pixel-wise semantic segmentation has outstanding advantages in 2D fine-grained segmentation.
## 3 Results and Discussion
### FEM--Validation
#### 3.1.2 Bar-Shaped Investigations
Due to the delayed closure of the entire system in the tests with the stepped tenon base shape STZ, the FE curve had to be shifted by 0.5 mm. The mean value curves of the test results of the REF 01 and STZ series show equally high ultimate loads (see Figure 19), although those of the stepped tenon base form are only reached at deformations of 15 mm. In the FE model, these series show the same initial stiffnesses \(K_{\text{ser}}\) (see Figure 23). The initial stiffness \(K_{\text{ser}}\) is overestimated in the FE model for  
Figure 21: FEM—Transverse tension and compression stresses (z-direction) in the hole of the main girder (**a**) Reference REF 03 section (**b**) Step shape STZ section. Figure 22: FEM- Validation of the test runs (thin solid lines) (**a**) REF 03 (**b**) STZ. the STZ series, since the problem of closing the individual steps resulting from a lack of fitting accuracy is not covered by the model. Reference form REF 03 shows the lowest initial stiffness, \(K\)ser, in the FE model (see Figure 23). The ultimate loads in the tests of the REF 03 series, shown in Figure 19, are all reached within deformations of 5 mm. Furthermore tests of REF 03 show a higher initial stiffness, \(K_{\text{Ser,mean}}\), as in the FE model because of a positive \"outlier\" in the tests (see Table 6). The individual steps of the tenon base attract different load proportions (see Figure 24). The widest, step 3 at the top, absorbs more than half of the load share of the entire tenon base. In general, the FE model for the STZ geometry shows a distribution of the load-bearing components between the tenon base and the flanks of approximately 88% to 12% (total value \(K_{\text{Ser,FEM}}\) of STZ in Table 6). This distribution thus, corresponds to that of the reference shape REF 01 (see Figure 23). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Series** & \(K_{\text{ser,mean}}\) & \multicolumn{2}{c}{\(K_{\text{ser,FEM}}\)} \\
**[-]** & **[N/mm]** & **[\%]** & **[N/mm]** & **[\%]** \\ \hline REF 01 & 9246 & 100\% & 10,145 & 100\% \\ STZ & 7102 & 77\% & 10,563 & 104\% \\ REF 03 & 9562 & 103\% & 8526 & 84\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: FEM—comparison of stiffnesses with test series. Figure 23: FEM—Comparison of the load-bearing behavior of reference shapes REF 01 and REF 03 with stepped tenon base shape STZ.
## 2 Experimental Setup and Test Sample  
The experimental setup was comprised of heat sources, a thermal camera and a computer that recorded data in real time. The heat sources were two photographic flashes (BOWENS BW-3955 Gemini R & Pro). The duration of the heat pulse at a maximum power of 1500 W was 0.7142 ms. Homogeneous heating of the test sample was achieved by positioning the flashes at an optimal distance of about 35 cm, at an angle of 45\({}^{\circ}\) relative to the normal of the surface being heated. To better direct the heat flux to the test sample surface, 65\({}^{\circ}\) reflectors with a 20 cm aperture were mounted on the flashes. Triggering of the flashes was synchronized via the computer. The cooling process was monitored by using a thermal camera (FLIR SC620), designed for the spectral range 7.5 \(\upmu\)m-13 \(\upmu\)m. The camera can be operated in three modes, so that the duration of a frame can be 8.33 ms, 16.66 ms or 33.33 ms.  
Figure 1 (photograph) shows the arrangement of cylindrical defects (flat bottom holes) simulated in a black acrylic glass plate. The dimensions of the plate are 180 mm \(\times\) 50 mm \(\times\) 4.2 mm. Four series of defects were made and denoted by A, B, C and D. Series A comprised four holes of equal diameters (\(\emptyset\) 5 mm) but different depths \(h\) (2.7 mm, 3.2 mm, 3.4 mm and 3.7 mm, respectively). Depths \(h\) are distances from the damaged surface. Series B, C, D and E included three holes each, whose diameters differed (\(\emptyset\) 3 mm, \(\emptyset\) 8 mm and \(\emptyset\) 15 mm, respectively), but their depth was the same. The depth of series B was \(h\) = 3.9 mm, of C \(h\)= 3.5 mm, of D \(h\) = 3.2 mm, and of E \(h\) = 2.6 mm.  
The dimensions of the defects are shown in Table 1, where \(\emptyset\) is the cylindrical hole diameter and the other dimensions \(h\) are the corresponding depths. The distance between the defects in group A was 10 mm and the distance from the test sample edge to the largest, medium-size and smallest defects in the other groups of defects was 15 mm, 32.5 mm and 42.5 mm, respectively.  
A series of measurements were made after the smooth, defect-free side of the test sample was illuminated.  
Figure 1: Photograph of acrylic glass test sample side on which defects were simulated marked as A, B, C, D and E series.
## 1 Introduction
### Motivation and Contribution  
From the aforementioned analysis, one can deduce that there is a serious lack of understanding of different aspects of energy harvesting HSTRNs that employ NOMA. To fill this research gap as well as meet the requirements of both performance and energy efficiency (EE) in future satellite communications, in this paper, we provide an in-depth performance evaluation of NOMA-enabled HSTRNs that employ energy harvesting relays. Our key contributions can be summarized as follows:  
1. A dynamic multi-antenna satellite-terrestrial relay communication design was considered. The terrestrial relay is able to harvest energy from the RF signal from the satellite and use it to forward the message to users. As main kind of NOMA, i.e., power splitting multiple access, is studied. The wireless channel between relay and NOMA users experiences Nakagami-\(m\) fading which is more versatile than conventionally used Rayleigh fading.
2. The closed-form expressions of outage probabilities of AF and DF relays are provided once Shadowed-Rician fading model is applied for satellite link. In addition to this, the analytical expressions of ergodic capacities are provided.
3. Extensive simulations are carried out to validate the accuracy of derived expression. The obtained results are also compared with the conventional OMA approach to highlight the performance gains.
## 3 Methods
### Overall Framework
#### 3.2.4 Training and Testing Procedure of the Proposed Fault Diagnosis Framework  
Based on the data preprocessing, data generation, and fault diagnosis modules built above, the training and testing process of the proposed overall framework is shown in Figure 4. It consists of the following steps:  
* The raw vibration signal data are acquired and divided into an S & I dataset for training and a balanced dataset for testing.
* FFT is performed on the two datasets to obtain the preprocessed frequency domain signal.
* The ACGAN-SN model described in Section 3.2.2. is built to initialize the model weight parameters and hyperparameters, and to train the ACGAN-SN until the Nash equilibrium conditions are met. The trained model is used to synthesize the data that require labels.
* Real data and synthetic data are mixed to form a variety of datasets with different imbalance ratios for the training of the fault diagnosis models.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer** & **Parameters Setting** & **Output Size** \\ \hline Embedding1 & Conv1d: 16 \(\times\) 3, 1, ReLU, BN, & \multirow{2}{*}{\(1\times 1024\)} \\  & Maxpool1d: (2,2) & \\ Embedding2 & Conv1d: 32 \(\times\) 3, 1, ReLU, BN, & \multirow{2}{*}{\(16\times 512\)} \\  & AdaptiveMaxpool1d: 25 & \\  & Reshape: 16 \(\times\) 512-32 \(\times\) 25; & \\  & Data replacement between 1 & \multirow{2}{*}{\(25\times 32\)} \\  & and 2 dimensional & \\ BiLSTM & \(32\times 64\times 2\), Tanh & \(25\times 128\) \\ Reshape & \(25\times 128\)-1 \(\times\) 3200 & \(1\times 3200\) \\ FC1 & \(3200\times 256\) & \(1\times 256\) \\ FC2 & \(256\times 10\), Softmax & \(1\times 10\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The structure of the fault diagnosis module.  
Figure 3: The structure of a LSTM cell.  
* The BiLSTM-based fault diagnosis model described in Section 3.2.3 is built, the BiLSTM model is trained with different datasets, the fault state of the test set with the trained model is predicted and compared with the other methods to evaluate the performance of the proposed fault diagnosis method.
# Energy Efficiency and Coverage Trade-Off in 5G for Eco-Friendly and Sustainable Cellular Networks
## 3 System Model and Problem Formulation
### Problem Formulation and Mathematical Modeling
#### 3.2.3 Data Rate  
Using Equation (17), the expected network aggregate data can be estimated:  
\[C=\frac{N_{RB}\times N_{SC}^{RB}\times N_{Symb}^{SC}\times m_{bits}^{Symb} \times CR}{T_{Slot}}, \tag{17}\]  
where \(N_{RB}\) represents the number of resource blocks (RBs); \(N_{SC}^{RB}\) represents the number of subcarriers per RB; \(N_{Symb}^{SC}\) represents the number of modulation symbols per subcarrier; and \(m_{bits}^{Symb}\) represents thenumber of bits per modulation symbol, which depends on the modulation scheme that is adaptively selected. \(CR\) is the code rate; and \(T_{Slot}\) is the slot time.
## 2 Materials and Methods
### Planetary Gear Train  
A planet gear refers to a gear that transmits power by revolving around a fixed gear. A planetary gear train, which includes multiple planet gears, consists of a sun gear, a ring gear, planet gears, and a planet carrier, as shown in Figure 3: (1) sun gear: a gear with external teeth that serves as the central axis of the planetary gear train; (2) ring gear: a gear with internal teeth that is co-axial with the sun gear; (3) planet gears: gears with external teeth that simultaneously mesh with both the sun gear and the ring gear; (4) planet carrier: a component that supports the planet gears and is co-axial with both the sun gear and the  
Figure 2: Expert interviews on conceptualizing the two-way wheelchair propulsion system.  
ring gear [25]. Planet gears rotate on their own axis while also orbiting around the sun gear, driven by the movement of the planet carrier.  
A planetary gear train is compact and lightweight, making it widely used in various fields [26]. In addition, it can transmit power in seven different ways depending on the input, output, and fixation settings of the sun gear, ring gear, and planet carrier, which are co-axial. Among these, the driving shaft and driven shaft rotate in the same direction when either the ring gear or the sun gear is fixed. In contrast, when the planet carrier is fixed, the driving and driven shafts enter a reversing state, where they rotate in opposite directions [27]. When the planet carrier is fixed, the planet gears cannot orbit around the sun gear and can only rotate on their own axes. At this point, if rotational force is transmitted to the ring gear, the ring gear begins to rotate. The planet gears, which are meshed with the ring gear, also rotate in the same direction as the ring gear. However, since the planet carrier is fixed, the planet gears cannot revolve around the sun gear and can only rotate on their own axes. As a result, the sun gear, which is meshed with the planet gears, rotates in the opposite direction to the planet gears. Consequently, the initial rotational force applied to the ring gear results in the sun gear rotating in the opposite direction. By utilizing this reversing mechanism of the planetary gear train, it is expected that a manual wheelchair can be designed to move forward when the pushrims are either pushed or pulled.
# Fog-Computing-Based Heartbeat Detection and Arrhythmia Classification Using Machine Learning
## 5 Results and Discussion
### Arrhythmia Classification  
After the heartbeat is detected by the previous module, the sequence is transformed into a tridimensional stream and passed to the RNN classifier. Figure 9a depicts the confusion matrix of the classifier achieved for each classification category considered and Figure 9b provides the normalized version of the matrix.  
The overall performance of the system we presented here (ACM) indicates a very high accuracy, reaching 96% for VEBs and 79% for SVEBs. To better understand the overall performance, we compare the results with those achieved by the classification system based on morphology and interval features (MIF) presented by [PERSON] et al. [55] and the classifier using a mixture of experts (MOE) approach presented by [PERSON] et al. [56]. The results of the evaluation of the ACM module are compared to the results of the evaluation of MOE and MIF as reported in Reference [55,56]. The comparison of the results are presented in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Precision** & **Recall** & **F1 Score** \\ \hline
**ACM** & **0.805** & **0.891** & **0.845** \\ MIF & 0.760 & 0.789 & 0.774 \\ MOE & 0.768 & 0.602 & 0.674 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance of the arrhythmia-classification module (ACM), the classification system based on morphology and interval features (MIF), and the classifier using a mixture of experts (MOE) in terms of average precision, average recall, and average F1 score.  
Figure 9: The actual beats included in the MIT-BIH database for each category and the classification of the beats produced by the arrhythmia-classification module.  
As in the case of the HDM, the training of this module is conducted following an _intra-patient_ approach. Here as well, this training approach improves the results. Again, this requires the RNN to be trained for each specific patient.  
These results demonstrate that the particular approach to signal processing, heartbeat-detection, and feature generation, based only on the detected beats is an improvement on previously reported results for automated heartbeat-classification systems, especially when considering systems of low capabilities. In particular, the ACM module uses two features whereas the MIF [55] uses 15 features and MOE [56] uses eight features. To further evaluate the performance of the ACM module within an embedded processor, we used an Arduino/Genuino 101 development board ([[https://store.arduino.cc/genuino-101](https://store.arduino.cc/genuino-101)]([https://store.arduino.cc/genuino-101](https://store.arduino.cc/genuino-101))) to assess the performance of the feature generation when executed on the x86 core of the Intel(r) Curie(tm) asymmetric embedded processor. The evaluation indicates that the embedded processor is capable of processing the raw signal and generating the two features in real time. The measured power consumption is 28.84 mW and the total execution time is 6.7 ms.