esa-sceva/satcom-chunk-collection huggingface dataset:
# Palm Date Leaf Clipping: A New Method to Reduce PAPR in OFDM Systems
## 5 Simulation Results
### PAPR Performances
#### 5.1.2 PAPR Performance with Variable CR  
In this subsection, we present some simulations related to the PAPR in order to study the effect of the parameter CR for each modulation type. The obtained results presented by the CCDF are depicted in the 4-QAM modulation (Figure 12), the 16-QAM modulation (Figure 13), and the 64-QAM modulation (Figure 14).  
Figure 11: PC-OFDM 64-QAM with variable CRs and smoothness.  
Figure 10: PC-OFDM 16-QAM with variable CRs and smoothness.  
Figure 12: Complementary Cumulative Distribution Function (CCDF) of PC-OFDM with 4-QAM for different Clipping Ratios.  
Figure 13: CCDF of PC-OFDM with 16-QAM for different Clipping Ratios.  
According to the results of this study, we notice that the value of PAPR degraded when the CR decreased for all modulation schemes, with small differences for a given CR. However, the performance in terms of PAPR reduction was good, and we were able to achieve 2.25 dB at CCDF equal to \(10^{-4}\) when CR = 1 dB.  
Figure 15 depicts the gain in dB compared to normal OFDM; it can be seen that the variations of parameter \(\beta\) do not have a significant influence on PAPR when the CR is greater than 5 dB, and the PAPR decreases when CR decreases. As shown in this figure, we can have a gain of between 1.91 dB (64-QAM) and 2 dB (QAM) when CR = 10 dB. Therefore, it is important to study the impact of \(\beta\) on the signal performance. The obtained value of CCDF at \(10^{-4}\) for variable CR and the modulation scheme with two values of \(\beta\) (0.5 and 5) of the proposed method are summarized in Table 2.  
Figure 14: CCDF of PC-OFDM with 64-QAM for different Clipping Ratios.  
Figure 15: PC-OFDM gain of peak-to-average power ratio (PAPR) at CCDF = \(10^{-4}\) dB for different Clipping Ratios.  
The obtained results from Figure 15 and Table 2 are very interesting and confirm that our proposed Palm Clipping method achieves good PAPR reduction values when CR = 0 dB and \(\beta=0.5\). It is well demonstrated that it is possible to achieve a gain of between 7 and 9 dB depending on the type of modulation scheme and CR. Since we know that all the clipping techniques deform the transmitted signal, it is very important to analyze the BER performances of the proposed method to find the best compromise between PAPR and BER.
# Advantages and Limitations of Current Microgravity Platforms for Space Biology Research
## 4 Non-Orbiting Microgravity Facilities
### Sounding Rockets  
Sounding rockets have been used since the late 1950s for meteorological and upper atmosphere studies and, starting from 1982, they have also been used for biological experiments [62]. Sounding rockets are sub-orbital rockets that carry payloads above the atmosphere, without placing them into orbit around the Earth (Figure 3c). Sounding rockets can be used in a wide range of disciplines, like physics, materials science, and biology. In particular, scientists have used sounding rockets to study gravity-sensing mechanisms in several plants and animals.  
Typically, such rockets reach an altitude of 250-350 km, at which point the payload is released, undergoes stabilized free-fall, and finally lands by parachute (Figure 3c). A free-fall of 6-13 min and 5-20 min is obtained for the rockets used by ESA and NASA, respectively [62,63]. Microgravity levels are usually at or below \(10^{-5}\) g (Table 3). Payloads can be quite large, thus containing several experimental modules. ESA organises sounding rockets campaigns as often as every two years.  
There are several variables that must be taken into consideration when designing experiments for sounding rockets flights. Samples can experience hypergravity phases during the ascent. Moreover, the payload module outer structure can reach temperatures of 300 \({}^{\circ}\)C and above, during the ascent and descent phases, respectively. An external liquid cooling loop is available for those payloads that contain temperature-sensitive experiments.  
Like drop towers, sounding rockets are a good opportunity for implementing research in preparation for long-duration missions to obtain valuable scientific return at a contained cost. One of the benefits of this type of carrier is that late access to the payload is available up to less than 3 h before launch. This makes the use of sounding rockets compatible with studies involving samples requiring complex preparation, or focused on time-critical biological processes. Sounding rocket missions can be carried out at medium cost and payloads can be developed in as little as three months (Table 3). This rapid experimental turnover allows scientists to quickly plan new experiments, using the latest, most up-to date knowledge and technologies. Moreover, as sounding rockets are unmanned, payload safety requirements are less stringent for sounding rockets compared with parabolic flights.
# Real-Time Prediction of Fuel Consumption Based on Digital Map API
## 4 Development and Verification of Application
### Error Analysis  
According to some existing research, the difference between the fuel consumption data calculated from the OBD data and the measured fuel consumption data is acceptable [24; 25]. Thus, in order to verify the accuracy of the model, three trips are selected randomly in each interval of engine displacement from OBD data. There is a total of 12 trips' fuel consumption data used to verify the proposed application. Table 8 shows the results.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Engine Displacement Interval** & **Speed Interval (km/h)** & **The Rate of Fuel Consumption (mL/s)** \\ \hline \multirow{4}{*}{(0,1.6)} & \(\leq\)20 & 0.532 \\ \cline{2-3}  & 20–40 & 0.618 \\ \cline{2-3}  & 40–60 & 0.732 \\ \cline{2-3}  & 60+ & 0.984 \\ \hline \multirow{4}{*}{[1.6,2.5)} & \(\leq\)20 & 0.474 \\ \cline{2-3}  & 20–40 & 0.694 \\ \cline{2-3}  & 40–60 & 0.858 \\ \cline{2-3}  & 60+ & 1.298 \\ \hline \multirow{4}{*}{[2.5,4)} & \(\leq\)20 & 0.993 \\ \cline{2-3}  & 20–40 & 0.81 \\ \cline{2-3}  & 40–60 & 0.978 \\ \cline{2-3}  & 60+ & 1.423 \\ \hline \multirow{4}{*}{\(\geq\)4} & \(\leq\)20 & 0.988 \\ \cline{2-3}  & 20–40 & 1.181 \\ \cline{2-3}  & 40–60 & 1.359 \\ \cline{1-1} \cline{2-3}  & 60+ & 1.679 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The fuel consumption rates in each speed interval and engine displacement interval.  
Figure 8: The application interface.  
Figure 7: The specific flow chart of the fuel consumption predictions.  
Overall, it can be seen that the average error of the VSP distributed fuel consumption model can be controlled at 20%. The trips with an error higher than 15% are analyzed concretely and results are shown in Table 9. It can be seen that the time proportion of the low-speed intervals is large on these trips. Considering the irregularity of the VSP distribution in the low-speed interval, it is difficult to distinguish the idle state from the low-speed driving state in the prediction, which leads to a large error.  
\begin{table}
\begin{tabular}{c c c} \hline
**Trip ID** & **OBD Data of Fuel Consumption (mL)** & **VSP Distributed Fuel Consumption Model (mL)** \\ \hline Tripid\#1 & 4202.1 & 3970.1 \\ Error & 0\% & 5.52\% \\ \hline Tripid\#2 & 593.7 & 573.6 \\ Error & 0\% & 3.38\% \\ \hline Tripid\#3 & 177 & 195.1 \\ Error & 0\% & 10.19\% \\ \hline \end{tabular}
\end{table}
Table 8: Comparisons of fuel consumption.  
\begin{table}
\begin{tabular}{c c c} \hline
**Trip ID** & **OBD Data of Fuel Consumption (mL)** & **VSP Distributed Fuel Consumption Model (mL)** \\ \hline Tripid\#4 & 4936.1 & 5849 \\ Error & 0\% & 18.49\% \\ \hline Tripid\#5 & 1913.2 & 1858 \\ Error & 0\% & 2.89\% \\ \hline Tripid\#6 & 3652.4 & 4291.4 \\ Error & 0\% & 17.50\% \\ \hline \end{tabular}
\end{table}
Table 9: The comparison of time proportion.
# Stability and Heat Input Controllability of Two Different Modulations for Double-Pulse MIG Welding
## 3 Comparison of Stabilities of DP-MIG Welding and TP-MIG Welding  
The stabilities of TP-MIG welding and DP-MIG welding were compared by analyzing the sound of their arcs, their transient welding current waveforms, and their weld morphologies.  
An experiment was conducted using an automated welding system and a 1.2-mm (ER4043) aluminum alloy welding wire. The weld was formed on an AA6061 alloy plate (with thicknesses in the range of 1-5 mm) at a constant wire feed speed. The standardized current levels in this experiment were 60 A, 80 A, 100 A, 120 A, 140 A, 160 A, 180 A, and 200 A. The DP-GMAW long-step calibration curve for 1.2 mm ER4043 aluminum alloy welding wires (with \(t_{bs}\) and \(t_{bw}\) being 8 ms, \(n_{s}\) and \(n_{w}\) being 15, \(I_{ps}\) being 470 A, \(I_{pw}\) being 330 A, and \(I_{bs}\) and \(I_{pw}\) being 45 A) was constructed using expert data is shown in Figure 7.  
Figure 7: 1.2-mm calibration curve step distance of aluminum alloy welding wire.
# Deep Learning-Based Network Intrusion Detection Using Multiple Image Transformers
## 3 Proposed Algorithm
### 2D Image-Based DL Classifier  
The generated image is used to train the four-layer dual CNN model shown in Figure 4. This CNN model has two four-layer CNN models with different filter sizes in parallel to improve intrusion detection performance. The selected filter sizes are 3 \(\times\) 3 and 5 \(\times\) 5, and for each layer of the CNN model, the padding option is set to be valid, and ReLU is applied as an activation function. MaxPooling is constantly applied to the convolution layer output. The outputs of the two four-layer CNNs applied Global Average Pooling, and are combined through the concatenate layer, and the final classification is performed through the fully connected network.  
It is a well-known fact through research on existing inception network that exploiting various kernel sizes in CNN is of great help in improving classification performance [inception]. However, since inception network is far more complicated and slower than other deep learning models such as CNN and RNN, it is difficult to apply them to high performance NIDSs. Therefore, in order to minimize the degradation of the classification speed while having the advantage of multiple kernel sizes, the proposed method uses only two kernel sizes, concatenates the results, and uses them for final classification.  
Figure 3: Final 3-channel RGB color image created using grayscale images generated by t-SNE, kPCA (cosine), and UMAP, which are the image transformers with the highest F1-score values.
## 4 Preparation of HTS Tape Samples
### HTS Tape Sample Parameters  
For the microstructural tests, HTS tape samples were prepared which, as a result of the multiple transitions from the superconducting state with a test current impulse, showed a decrease in the critical current value (Table 3). Tests of the SF12100-CF tapes with 4 \(\upmu\)m and 2 \(\upmu\)m thick silver layers were performed for one value of the prospective short-circuit current. The critical current \(I_{C}\) was measured for the unused HTS tapes, and then the samples were subjected to 25 series of test current impulses with a duration of 0.2 s, and the \(I_{C}\) value was measured again. The duration of the test current impulse was selected in such a way that it corresponded to the typical operating times of the short-circuit protection of the MV power system protection. The values of the surge current \(I_{max}\), the released energy \(E\) during the impulse, and the voltage drop \(U_{drop}\) on the sample are presented in Table 3. In order to determine the possible causes of the changes in the critical current value, comparative microstructural studies were conducted using a scanning electron microscope for the samples subjected to surge currents and control (unused) samples.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**SF12100-CF**} & **Estimated** & \multirow{2}{*}{\
\begin{tabular}{c} **Surface Current** \\ **Current** \\ **\end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Energy Released** \\ **on the HTS Tape** \\ **\end{tabular} } & **Voltage Drop** & **Initial Critical** & **Critical Current** & **Percentage Change** \\  & & & **Aeros the** \\  & & & **Aeros the** \\  & & & **During the Surge** & **Sample** & **Current** & **Transitions** & **25 Transitions** \\  & & & & **_E (_)_** & \(U_{drop}\) (V) & \(I_{C\text{ON}}\) (A) & \(I_{C\text{ON}}\) (A) \\ \hline
4 \(\upmu\)m Ag & 950 & 617 & 146 & 0.61 & 318 & 276 & 13.21 \\
2 \(\upmu\)m Ag & 950 & 535 & 119 & 0.65 & 294 & 274 & 6.80 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Critical current values of the SF12100-CF tape samples used for the microstructural tests.  
Figure 6: Sample oscillogram of the test current pulse waveform for the SF12100-CF tape with a 4 \(\upmu\)m silver layer illustrating the principle of short-circuit limitation by the SFCL.
## 2 Problem Definition  
### Finite Element Mesh and Boundary Conditions  
Figure 1 depicts the typical soil model utilized in this study, featuring the footing and cavity system. In the scenario examined, a shallow foundation with a width denoted as \(B\) is placed on the ground with a cavity. The foundation is subjected to uniform vertical loads in homogeneous soil under plane strain conditions. The uniform load is applied to the foundation base, and the foundation is not embedded. The soil characteristics include the cohesion \(c\), the shear resistance angle \(\phi\), and the unit weight \(\gamma\). The cavity's shape is square and circular (though it is worth mentioning that the circular configuration is not illustrated here) in the homogeneous soft soil material. To mitigate boundary effects, the model's  
boundaries are selected to be sufficiently large. The dimensions of the model are specified as a width of 40 m and a height of 18 m. Vertical displacements are exclusively permitted at the lateral boundaries. At the bottom horizontal boundary, displacements in the horizontal and vertical directions are strictly restricted. Two crucial distances are defined in the context of the cavity: \(h\) (m), representing the vertical gap between the ground surface and the cavity's center, and \(D\) (m), denoting the diameter of the cavity (applicable for a circular cavity as well). Additionally, \(r\) (m) is the horizontal distance between the centerlines of the cavity and the footing. This study assumes that the cavity remains unsupported and statically stable under its self-weight. For enhanced accuracy in the solution, the mesh sizes are deliberately finer around the footing base and the surrounding area. Notably, the cavity's location varies for each analysis case.
## 2 Related Work
### Research Process
(2016) proposed an online CTC/attention-based end-to-end speech recognition architecture using Transformer, which performed exceptionally well in multiple benchmark tests, particularly with long sequence inputs. [PERSON] et al. (2017) introduced a sequence-to-sequence voice conversion model (ATTS2s-VC) based on attention mechanisms and context retention mechanisms. This model, utilizing the Transformer architecture, achieved high-quality voice conversion, particularly excelling in cross-linguistic speech conversion tasks. [PERSON] et al. (2018) proposed a method for dynamically inducing attention probability sparsity by incorporating a weak attention mechanism into a Transformer-based speech recognition model. This method improved the model's recognition performance in noisy environments by suppressing irrelevant information. [PERSON] et al. introduced the Conformer model, a hybrid architecture that integrates convolutional neural networks (CNNs) and Transformers, aimed at effectively modeling both local and global dependencies in audio sequences ([PERSON] et al., 2018). Experimental results showed that it significantly outperformed traditional RNN and pure Transformer models on the LibriSpeech dataset, achieving state-of-the-art performance at the time. [PERSON] et al. (2019) proposed an improved Conformer-based model for high-precision speech feature extraction and learning. The experimental results indicated that the improved model significantly enhanced feature extraction and recognition accuracy. Speech recognition technology has evolved from traditional acoustic statistical models to neural networks, and then to end-to-end deep learning models. While early systems improved accuracy, they were often complex in structure and computationally expensive. With the emergence of end-to-end architectures, speech recognition has become more streamlined, significantly enhancing both model accuracy and efficiency. Currently, end-to-end methods based on Transformer architectures have become mainstream and have achieved outstanding results across various speech recognition tasks.
## 4 SSB Signal Generation and SSBN Mitigation Techniques
### Carrier and SSB Signal-Generation Techniques  
In this subsection, different means of generating SSB signals are discussed. In this context, the four extensively employed methods are considered. As illustrated in Figure 3, they are classified into two fundamental groups based on the adopted generation approach, which can be a bias-induced or carrier-assisted scheme. The considered SSB signal-generation schemes and the associated spectra are depicted in Figure 4.  
Figure 3: Carrier and SSB signal-generation classification. DDMZM: dual-drive Mach–Zehnder modulator.
## 2 Materials and Methods  
In order to address VAAG estimation, a classification approach was utilized. The aim was to predict VAAG based on frequency features derived from the BAPW. To achieve this, different SL models were tested to identify the most suitable approach.  
The evaluation process involved two distinct case studies. In Case Study 1, an in silico dataset [8], derived from a One-dimensional simulated model, was exclusively used for model training, and subsequently, the models were tested on two different in vivo datasets. One in vivo dataset contained information of healthy subjects, while the other one had a combination of healthy and pathological subjects. This approach aimed to assess the model performance when exposed to real-world data obtained from different sources.  
To ensure robustness of the SL models, an alternative strategy to Case Study 1 was proposed. Case Study 2 offered a different perspective which comprised the combination of the in silico and in vivo datasets to train SL models. The in vivo dataset used for training was the one composed exclusively of healthy subjects. Each dataset provided their 80% part for training, and the remaining 20% for testing. Testing subjects were not combined as a whole so as to evaluate metrics from each dataset separately. The resulting models were validated against the healthy and pathological in vivo dataset named \"local clinical in vivo Dataset\". For this study, it was assumed that healthy subjects should have the same VAAG as their CHA group or even lower, while pathological subjects should have a higher VAAG. For that reason, the local clinical in vivo dataset was used as a form of validation since information about each subject's risk factors was provided.
## 4 Routing in Integrated Networks  
In combined land-based and satellite networks, routing is essential for achieving streamlined and trustworthy communication between users and services. By choosing the best network pathways, routing algorithms help balance resource utilization and reduce latency while addressing the specific challenges posed by merging ground and satellite systems. The dynamic nature of LEO satellite networks, such as changing satellite positions and varying link conditions, adds complexity to the routing problem, making it essential to develop adaptive algorithms that can effectively respond to these fluctuations. As the desire for high-quality communication continues to grow, the development of innovative routing strategies for integrated networks becomes increasingly important to ensure optimal performance and user satisfaction. The literature on routing protocols in integrated networks is given in Table 4.  
In 2018, [PERSON] et al. focused on routing in DTN-Nanosatellite networks. Their aim was to alleviate the limitations of nano-satellites, including capacity for storage and energy supply, and to increase their lifetime and performance. At this point, they suggested an innovative energy-sensitive routing algorithm derived from Contact Graph Routing (CGR) named E-CGR. They used the Contact Graph Routing (CGR) algorithm, which is most compatible with the predictable conditions of satellite movements in DTN networks that store data when there is no suitable path. In this algorithm, when the ground networks receive the data to be transmitted, they store the data in their memory before transmitting it. When communication starts, the satellite network sends a bundle to the ground network, which indicates the current energy level, called the energy bundle. With this data,  
Figure 5: LEO satellite network topology. it is understood whether the energy of the satellite network guarantees the completion of the routing process. In their performance analysis in the Network Simulator 3 (NS3) environment, the results using E-CGR and standard CGR were compared based on two evaluation indicators: average time to delivery (ADT) and percent of packages delivered (PDB). The results obtained from the E-CGR algorithm show that the average data transmission time decreases, and the amount of data beams delivered to the destination increases compared to classical CGR. However, the variables that can affect the routing process can be expanded further [121]. \begin{table}
\begin{tabular}{p{11.3 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref.