esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion  
Corresponding research was carried out on the new docking mechanism and the simulation test bench for degrees of freedom. Although some research results have been achieved, there were still many issues to be further studied and explored:  
1. The buffer mechanics test of the docking mechanism needs to be improved. In order to more effectively ensure the reliability of the docking mechanism, it is necessary to deeply analyze the dynamic characteristics of the buffering process to further verify the correctness of the established attitude kinematics and dynamic models; at the same time, the collision angle only considers the frontal and oblique collisions, and does not consider whether the collision in space, that is, the collision of the three axes of XYZ with a certain inclination angle will affect the stability of the entire mechanism;
2. At present, only the static analysis of the hook claw is carried out, and the dynamic analysis should be carried out in the follow-up work to fully reflect the transient deformation under force. In the low temperature of space and the change of the material in the vacuum environment, the change of the material should also be considered, and the structure will not be affected. It will cause embrittlement or even breakage due to ultra-low temperature. Should the flexible grasping robot hand be taken into consideration?
3. The research on the joint simulation experiment method in the simulated space vacuum environment still needs to be explored. The fact that no one is now operating the docking, signal transmission, whether the space robot will cause attitude deviation  
Figure 12: Vertical docking test (**a**) test with offset of 40 mm; (**b**) test with offset of 30 mm; (**c**) test with offset of 20 mm.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Measured Value**} & \multicolumn{3}{c}{**Test Group**} \\ \cline{2-4}  & **Offset 20 (mm)** & **Offset 30 (mm)** & **Offset 40 (mm)** \\ \hline Time (s) & 120 & 122 & 125 \\ Catching Force (kg) & 11.94 & 10.08 & 11.33 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Four groups of contact force and buffering force.  
due to delay after receiving the signal, and how to correct the deviation are all connected. These are future things to consider.
# Optimum Slot and Pole Design for Vibration Reduction in Permanent Magnet Synchronous Motors
## 3 Results
### Experimental Verification of the Analytical Results  
In order to verify the design rules proposed, experimental measurements of two machines were carried out. The motors measured were a 48 slot and 8 pole pair machine and an 18 slot and 8 pole pair machine (see Table 2). Figure 10 shows the comparison of the analytical calculation of the 48 slot and 8 pole pair machines with the experimental measurements. The results are in good agreement, thus, it is concluded that the main harmonics mentioned in Section 3.2 are clearly determined.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Machine** & **12p8** & **12p10** & **15p10** & **18p8** & **24p8** & **24p10** \\ \hline \(\gcd(Q_{s},2p)\) & 4 & 4 & 5 & 2 & 8 & 4 \\ \(\operatorname{lcm}(Q_{s},2p)\) & 48 & 60 & 60 & 144 & 48 & 120 \\ \(k_{min}\,|_{n=\gcd}\) & 2 & 2 & 2 & 2 & 2 & 2 \\ \(v_{min}\,|_{n=\gcd}\) & 1 & 2 & 1 & 2 & 2 & 1 \\ \(k_{min}\,|_{n=0}\) & 6 & 6 & 6 & 18 & 6 & 12 \\ \(v_{min}\,|_{n=0}\) & 4 & 5 & 4 & 8 & 2 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Parameters of the machines of 8 and 10 pole pairs.  
Figure 9: Comparison of the vibration response of machines of several numbers of poles and slots.  
In addition, the machines of 48 slots and 8 pole pairs, and of 18 slots and 8 pole pairs are compared in Figure 11. The predominant harmonics can be clearly observed in each case: the 6 th harmonic for the 48p8, which corresponds to the mode order 0 due to a low LCM value, and the 2 nd harmonic for the 18p8 due to the low GCD value (see Table 6).
## 4 Experiments and Analysis
### Quantitative Comparisons  
We firstly performed a quantitative comparison on 90 images from T90, 60 images from C60, and 45 images from U45. Table 1 gives the quantitative results of different methods. The PSNR, SSIM, and MSE were used as full-reference evaluation metrics and provided only the T90 test set, since T90 has ground-truth images. UCIQE and UIQM were used as non-reference evaluation metrics and provided for all test sets. From Table 1, it can be seen that our method achieved the best results in terms of the PSNR and MSE metrics on the T90 test set and the best results in terms of the UCIQE metric on all three test sets. Specifically, on the T90 test set with reference images, the proposed LPS-Net scored the best PSNR and MSE metrics and obtained the second-best SSIM metric, proving that the proposed network has achieved state-of-the-art UIE performance, and its network architecture is able to restore color and recover image details. Compared with the recently developed MFEF method, which roughly ranked second-best, LPS-Net led by 0.553 dB, 0.005, and 0.005 in terms of the PSNR, SSIM, and MSE. On the other hand, higher UCIQE and UIQM values indicate better performance in terms of sharpness, color correction ability, and contrast in images. Among the evaluated methods, LPS-Net achieved the highest UCIQE values, demonstrating its efficacy in mitigating color shifts and enhancing sharpness. Although FUNIE-GAN and HAAM-GAN slightly outperformed our method in terms of the UIQM, a qualitative comparison revealed that GAN-based approaches suffered from localized exposure issues, leading to significant image distortions. Conversely, the enhanced results obtained by LPS-Net effectively addressed color biases without introducing additional degradation problems. Apart from enhancement performance, efficiency is also very important for UIE methods. Table 2 provides comparison results in terms of various efficiency metrics, including the number of computational operations measured in GFLOPs, the number of parameters, and runtime speed. It can be seen that LPS-Net needed only 0.08 M (80.12 k) parameters, substantially fewer than other methods, which is beneficial for deployment in equipment with limited memory capacity. In terms of GFLOPs, LPS-Net ranked the second-best, after UHD-SFNet, whose UIE performance was much worse compared to LPS-Net, as shown in Table 1. Compared with Shallownet [38], we reduced the parameters by more than 50% and reduced the time complexity by more than 71%, but the PSNR and SSIM were 5.627 dB and 0.06 higher, which fully proved the superiority and feasibility of our method. Although LPS-Net did not have the best runtime metric, taking into consideration its performance and efficiency overall, it can be concluded that LPS-Net achieved state-of-the-art underwater image enhancement performance while enjoying superior model efficiency.
# Estimation of Stability Parameters for Wide Body Aircraft Using Computational Techniques
## 2 US DATCOM
### US DATCOM Results  
After the generation of source code, stability/control analysis was carried out, and results from US DATCOM at low-cruise-flight conditions with variations of the angle of attack and control deflection were obtained. The variation of different stability/control coefficients in both longitudinal and lateral modes can be seen with the angle of attack and control deflection in the plots shown below.  
\begin{table}
\begin{tabular}{c c c c} \hline
**S No.** & **Geometric Parameter** & **Symbol** & **Value** \\ \hline
1 & Reference area & S & 5500 ft\({}^{2}\) \\
2 & MAC & \(\overline{C}\) & 27.3 ft \\
3 & Wingspan & b & 196 ft \\
4 & Mach No (cruise) & M & 0.9 \\
5 & True airspeed & V & 871 ft/s \\
6 & Dynamic pressure & P & 222.8 Psi \\
7 & CoG location & \(\chi_{\text{cg}}/\overline{C}\) & 0.25 \\
8 & Angle of attack (cruise) & \(\alpha\) & 2.4 deg \\
9 & Weight & W & 636,636 lbs \\
10 & Moment of inertia & lxx & 1.82 \(\times\) 10\({}^{7}\) slug-ft\({}^{2}\) \\
11 & Moment of inertia & lyy & 3.31 \(\times\) 10\({}^{7}\) slug-ft\({}^{2}\) \\
12 & Moment of inertia & lzz & 4.97 \(\times\) 10\({}^{7}\) slug-ft\({}^{2}\) \\
13 & Inertia moment product & lxz & 9.7 \(\times\) 10\({}^{5}\) slug-ft\({}^{2}\) \\
14 & Fuselage length & L & 225.17 ft \\
15 & Wing sweep angle & \(\wedge\) & 37.5 deg \\
16 & Aspect Ratio & AR & 7 \\ \hline \end{tabular}
\end{table}
Table 2: Geometric parameters of Boeing 747-200 aircraft [52].
# A Pilot Experience with Software Programming Environments as a Service for Teaching Activities
###### Abstract
These benefits include: (i) the ability of the cloud to provide automated deployments of specific instruments, software and hardware orchestrated and configured ad-hoc for an educational activity [9]; and (ii) the possibility for students to access the infrastructure and software in an ubiquitous way with 24/7 accessibility from their own devices, since the cloud provides the computing and storage capacity, and not the devices themselves [10]. Therefore, our hypothesis is that cloud technology is appropriate to develop the aforementioned educational tool. The main contribution of this paper is **ACTaaS (Assessment of Computational Thinking as a Service)**, a cloud-based educational tool that eases and reduces the workload to deploy and configure a set of professional instruments for software programming on the cloud (private, public or federated) in an economic, simply and quickly way. These instruments are automatically configured and orchestrated to be used in teaching environments, and students can access them in a ubiquitous way. The paper presents the ACTaaS architecture, how all its functionalities have been implemented and what cloud-based tools have been employed to develop the educational tool. Moreover, we include the results of an initial experience in a pilot group of 28 students where all the functionalities of ACTaaS have been tested. Currently, as far as the authors know, there is no similar tool like ACTaaS that allows such a high grade of automation for the deployment of software production environments focused on educational activities supporting a wide range of cloud providers. The fact that the academic community has openly accessible an educational tool like ACTaaS will allow an improvement in the learning processes related to the acquisition of CT skills, since it will let students to use professional software production environments identical to the ones used in the industry. Numerous studies, such as fluid mechanics [6], electrical engineering [5] or higher education in general [11] have demonstrated that combining real scenarios and appropriate teaching methods improves the learning process. The structure of this paper starts with this introduction. Next, we review the related works in the area of education, and also analyze the state of the art related to cloud orchestration, because tools in the area will be of the key components in ACTaaS. Then, we present and analyze the architecture and utilities offered inside the ACTaaS solution. We pay special attention to the actions that both the teacher and the students have to do to integrate ACTaaS in the class. Finally, we present the preliminary results of its adoption in a real class environment with a pilot use case, to finish the paper with the conclusions and future plans of this work.
###### Abstract  
Today, most newspapers utilize social media to disseminate news. On the one hand, this results in an overload of related articles for social media users. On the other hand, since social media tends to form echo chambers around their users, different opinions and information may be hidden. Enabling users to access different information (possibly outside of their echo chambers, without the burden of reading entire articles, often containing redundant information) may be a step forward in allowing them to form their own opinions. To address this challenge, we propose a system that integrates Transformer neural models and text summarization models along with decision rules. Given a reference article already read by the user, our system first collects articles related to the same topic from a configurable number of different sources. Then, it identifies and summarizes the information that differs from the reference article and outputs the summary to the user. The core of the system is the sentence classification algorithm, which classifies sentences in the collected articles into three classes based on similarity with the reference article: sentences classified as dissimilar are summarized by using a pre-trained abstractive summarization model. We evaluated the proposed system in two steps. First, we assessed its effectiveness in identifying content differences between the reference article and the related articles by using human judgments obtained through crowdsourcing as ground truth. We obtained an average F1 score of 0.772 against average F1 scores of 0.797 and 0.676 achieved by two state-of-the-art approaches based, respectively, on model tuning and prompt tuning, which require an appropriate tuning phase and, therefore, greater computational effort. Second, we asked a sample of people to evaluate how well the summary generated by the system represents the information that is not present in the article read by the user. The results are extremely encouraging. Finally, we present a use case.  
n ARTL language processing; text similarity; Transformers; neural language models; newspaper articles +
Footnote †: journal: _Algorithms_
# Ionospheric Narrowband and Wideband HF Soundings for Communications Purposes: A Review
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ADC & Analog to Digital Converter \\ AGC & Automatic Gain Control \\ ARCS & Automatic Radio Control System \\ ASAPS & Advanced Stand Alone Prediction System \\ BDR & Blocking Dynamic Range \\ BLOS & Beyond line-of-sight \\ BPSK & Bi-Phase Shift Keyed \\ CW & Continuous Waveform \\ CRC & Communications Research Centre \\ DAC & Digital to Analog Converter \\ DD & Delay Doppler \\ DDC & Digital Down Converter \\ DERA & Defence Evaluation Research Agency \\ DSP & Digital Signal Processing \\ EO & Ebro Observatory \\ FFI & Forsvarets Forskningsinstitutt \\ FOA & Forsvarets Forskningsanstalt \\ FOT & Frequency of Optimum Transmission \\ FPGA & Field Programmable Gate Array \\ GAMBIT & Global Assimulative Model of Bottomside Ionosphere Timelines \\ GPS & Global Positioning System \\ HF & High Frequency \\ ICEPAC & Ionospheric Communications Enhanced Profile Analysis and Circuit Prediction Program \\ INTERMAGNET & INTErnational Real-time MAGnetic observatory NETwork \\ IONCAP & Ionospheric Communications Analysis and Prediction Program \\ IMAG & Ionosonde Network Advisory Group \\ ITU-R & The Radiocommunications section of International Telecommunications Union \\ IRE & Institute of Electronic Engineers \\ LGDC & Lowell GIRO Data Center \\ LOS & Line-of-Sight \\ MFLOPS & Million Floating Operation Points Per Second \\ NVIS & Near Vertical Incidence Sounding \\ OCXO & Owen Controlled Crystal Oscillator \\ PC & Personal Computer \\ PLD & Programmable Logic Device \\ PPS & Pulse per Second \\ SFDR & Spurious Free Dynamic Range \\ SHARC & Super Harvard Architecture \\ SNR & Signal to Noise Ratio \\ SSB & Single-Sideband \\ TID & Traveling Ionospheric Disturbance \\ TOF & Time of Flight \\ VOACAP & Voice of America Coverage Analysis Program \\ \end{tabular}
## 4 System Performance Discussion
### Parameter Discussion on System Performance
#### 4.2.4 Insertion Loss  
Insertion loss is also an important character of RF devices. If the insertion loss is too large, the error rate will be high and deteriorate system performance. The insertion loss can be expressed as:  
\[IL=-10\text{lg}\frac{P_{out}}{P_{in}} \tag{30}\]  
To explore the effect of insertion loss for single RF link MIMO radar system, the SPDT switch insertion loss ranging from 0 dB to 3 dB with scanning interval of 0.5 dB was investigated. The single RF link MIMO radar system detection probability values that varied with the insertion loss are listed in Table 6, which were obtained from the 1000 simulations performed by Monte Carlo method.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Insertion Loss** & **0 dB** & **0.5 dB** & **1 dB** & **1.5 dB** & **2 dB** & **2.5 dB** & **3 dB** \\ \hline Detection Probability & 0.997 & 0.997 & 0.995 & 0.987 & 0.986 & 0.982 & 0.968 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Insertion loss influence on the single RF MIMO radar performance.  
Figure 13: Normal radar space-time adaptive processing (STAP) in MIMO System.  
It can be concluded that the high insertion loss will result in lower target detection probability. When the insertion loss is less than 1.5 dB, the detection probability can reach more than 99%. With the increase in the insertion loss, the detection probability will decrease rapidly, which is almost below 98% when the insertion loss is higher than 2.5 dB. This is because the insertion loss will cause energy loss at the transmitter antenna, which weakens the receiving signal.
## 3 Key Variables in the Analytical Approximation Model
### True Anomaly  
Equation (21) gives the expression of difference vectors in the velocity vector term in Equation (10). However, the difference vector in the acceleration vector term, i.e., \(\Delta\mathbf{A}_{st}\), is given its expression with the help of true anomaly calculation.  
The accurate acceleration vector term in the orbital plane is given in Equation (14). True anomaly \(\
u\) should be calculated using the onboard-measured satellite's position and velocity vectors. A common way to solve \(\
u\) is given in [24]  
\[\
u=\text{atan2}\left(\sqrt{\frac{p}{\mu}}\left(\mathbf{V}_{sat,ECI}\cdot \mathbf{R}_{sat,ECI}\right),p-\left|\mathbf{R}_{sat,ECI}\right|\right) \tag{27}\]  
where the \(\text{atan2}\left(Y,X\right)\) function returns an unambiguous result of \(\arctan\left(Y/X\right)\) with a range of \(\left(-\pi,\pi\right]\). Obviously, the measured state vectors of the satellite \(\mathbf{P}_{sat,ECI,e}\) and \(\mathbf{V}_{sat,ECI,e}\) will result in a value of \(\
u_{e}\) that is not accurate. The probably distribution parameters of \(\
u_{e}\) should be determined in order to analyze the term \(\Delta\mathbf{A}_{st}\) and its influence on QPE.  
The detailed derivation is in Appendix A. Here, the standard deviation and its maximum value of the difference between the calculated \(\
u_{e}\) and its accurate value \(\Delta\
u=\
u_{e}-\
u\) are given.  
\[\sigma\left[\Delta\
u\right]=\left|\cos\
u\right|\cdot\frac{1+e\cos\
u}{\sqrt{ \mu ae^{2}\left(1-e^{2}\right)}}\cdot\sqrt{\frac{\mu}{p}\left(1+e^{2}+2e\cos \
u\right)\sigma_{p}^{2}+\frac{p^{2}}{\left(1+e\cos\
u\right)^{2}}\sigma_{v}^ {2}} \tag{28}\]  
\[\sigma\left[\Delta\
u\right]_{\text{max}}=\frac{1}{e}\sqrt{\frac{1}{a^{2}} \sigma_{p}^{2}+\frac{a}{\mu}\sigma_{v}^{2}} \tag{29}\]  
We assume that the variances of the position and velocity errors are the same standard deviation in the \(x\)-, \(y\)-, and \(z\)-direction in the ECI system, i.e., \(\sigma_{p,x}=\sigma_{p,y}=\sigma_{p,z}=\sigma_{p}\) and \(\sigma_{v,x}=\sigma_{v,y}=\sigma_{v,z}=\sigma_{v}\). Equation (29) can provide a good approximation when orbit eccentricity \(e\) is rather small.  
It is important to point out that \(\sigma\left[\
u\right]_{\text{max}}\) is not equal to the exact maximum value of \(\sigma\left[\
u\right]\) for \(\
u\in\left[0,2\pi\right]\). This approximate maximum value is subsequently used to calculate the maximum value of QPE. With this assumption, it would be clearer to identify the core error terms of each variable. A more general expression without the assumption can be found in Appendix A.
# Impact of Environmental Factors on Passenger Ride Comfort
## 7 Results of Experimental Studies
### Visual Comfort Results  
Figure 16 shows the results of the average illuminance values in the metro vehicles. In each vehicle, illuminance was measured at four measurement points. The measurement points were located at a height of 0.8 m from the floor near the passenger seats, with two in the front and two in the rear of the carriage (one on the left and one on the right side).  
The average illuminance for the new metro train was 205.4 lux. This value is in the range of 200-400 lux, which corresponds to visual comfort level 5. This means that passengers in the new train experience a high level of visual comfort, which should promote a better travel experience. In the case of the old metro train, the average illuminance value was 170.8 lux, resulting in a visual comfort level rated at level 4 on the adopted comfort scale. In summary, the results show that the new metro train provides high visual comfort, while the old train requires improved lighting to meet the needs of passengers. The results for visual comfort are presented in Table 12.  
The present results for visual comfort in metro trains show notable differences compared to the observations in Montreal's metro system. As mentioned before, in the present study, the average illuminance in the new metro train falls within the range of 200-400 lux, corresponding to visual comfort level 5. This indicates that passengers in the new train experience a high level of visual comfort, giving a better travel experience. On the other hand, in the old metro train, the average illuminance was 170.8 lux, resulting in a visual comfort level rated at 4 on the adopted comfort scale. In contrast, in Montreal's metro system [30], old cars consistently suffer from low lighting, with illuminance levels falling below 200 lux, leading to a visual comfort rating of 0.60 on the Montreal metro scale. In the new cars, lighting easily surpasses the 300-lux threshold, achieving a comfort level of 1.00. The results indicate that the newer metro trains in both studies provide the highest level of  
Figure 16: Illumination intensity measured on metro trains.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Car** & **Metro, New Train** & **Metro, Old Train** \\ \hline E [ls] & Not applicable & 205.4 & 170.8 \\ Comfort level C4 & 5 & 5 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Illuminance and index of visual comfort C4 in passenger car and metro trains.  
visual comfort, whereas the older trains continue to fall behind, highlighting the need for improving lighting conditions.