esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Prediction of Underwater Acoustic Communications Based on Channel Impulse Responses
## 5 Results of the CIR-Based Methods
### Conventional Regression Methods with CNN Features  
For certain applications, such as model interpretability, it may be desirable to utilize a traditional model. By using the penultimate layer of the CNN as the output, features are extracted and fed into the previously used conventional models. The results of this method can be seen in Table 4. Results are nearly identical to the CNN results.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & \multicolumn{3}{c}{**KWAUG14**} & \multicolumn{3}{c}{**SPACE08**} \\ \hline  
\end{table}
Table 2: Conventional regression results using CIR-derived features.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Dataset** & **MAE** & \begin{tabular}{c} **Pearson Corr. Coeff.** \\ \end{tabular} \\ \hline KWAUG14 & \begin{tabular}{c} 0.0095 (\(9.9\times 10^{-4}\)) \\ \end{tabular} & \begin{tabular}{c} 0.91 (\(5.0\times 10^{-3}\)) \\ \end{tabular} \\ \hline SPACE08 & \begin{tabular}{c} 0.012 (\(9.3\times 10^{-4}\)) \\ \end{tabular} & \begin{tabular}{c} 0.86 (\(2.1\times 10^{-3}\)) \\ \end{tabular} \\ \hline Combined Datasets & \
\begin{tabular}{c} 0.011 (\(1.0\times 10^{-3}\)) \\ \end{tabular} &
\begin{tabular}{c} 0.89 (\(6.5\times 10^{-4}\)) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 3: Convolutional neural network regression results.
## 3 Results
### Accuracy and Precision  
The integration of images acquired from a GPS/GNSS Drone has allowed placing of the virtual outcrop in its geographical position, with some limitations in terms of absolute accuracy. For most of the structural geology analysis, which did not need very-high accuracy in terms of geographical positioning, the described method can be considered satisfactory.  
It is emphasized that this approach has to be considered as an integration to the \"traditional\" structural geological analysis. From the previous sections, it is evident that, in the two sample areas, the results obtained from aerophotogrammetrical processing and direct field measurements are comparable and that their precision is good. The maximum error measured along the fault scarps was close to 1 cm in the first area (CAB), and around 1-3 cm in the PTP area.  
It is also to be considered that many sites, which may be potentially be studied using the proposed approach, are unreachable with bulky and heavy instrumentation.  
In our case studies, the low performance of the GPS installed on the drone allowed correct positioning in a geographical system, but without a centimetric or millimetric accuracy. In any case, the precision of the height, width, and length of the virtual outcrop was satisfactory, thanks to the  
Figure 12: **(a)** Sample area 2: perspective 3D view extracted from Move Software of the PTP fault scarp in the Piano Grande near Castelluccio di Norcia; arrows show the alignment of the coseismic surface ruptures; in orange, the fault scarp of PTP is highlighted, black rectangles indicate the location of Figure 3f,g of coseismic ruptures measured during the field survey after 30 October 2016 Mw 6.5 earthquake (**b,c**) The plots show dip-azimuth of the coseismic ruptures measured with hand-compass and fieldmove; **(d)** trend of fault scarp obtained from a drone survey; for each rose diagram, the mean value resulting from the statistical analysis of the dataset is reported in the bottom-right rectangle Rose diagrams were extracted through Move 2019 software and redrawn through Corel Draw X4 software.  
correct use of caliber instruments. In conclusion, the results here obtained made it possible to create a valid and reliable model.
## 6 Conduct
Consequently, Traditional Space is rated very low on speed to market. By contrast, given a very high rating, New Space firms substantially compress these timelines by adopting agile development and short development cycles. As a result, satellites can move from design to orbit within a matter of months rather than years, providing greater flexibility, faster feedback loops, and the capacity for more frequent technology upgrades. The approach to the potential offered by LEO illustrates a pronounced difference between Traditional Space and New Space satellite deployments. This results in a relatively low rating for Traditional Space (which is more GEO- and MEO-focused) and a very high rating for New Space, which is focused on LEOs. Historically, large geostationary satellites dominated the industry, reflecting Traditional Space's preference for missions with longer operational lifespans. On the other hand, the constellations of small satellites in LEOs have proved advantageous, including reduced launch costs, lower latency for communications, and more frequent revisit times for Earth observation. Therefore, traditional satellites remain huge, complex, and customized, while New Space focuses on rather small, standardized satellites (including CubeSats). They enable more frequent launches and a constellation-based approach to coverage. This lighter, modular design lowers the barrier to launch and simplifies incremental upgrades, allowing scalability. Finally, the seventh factor, leveraging a short life cycle as an advantage, is absent in Traditional Space, because it emphasizes longevity. However, it could be seen as a differentiating factor for New Space. Rather than viewing short life cycles as a drawback, many new companies use shorter operational windows for frequent hardware upgrades,  
Figure 11: The strategy canvas for satellites: New Space versus Traditional Space. Source: Own. which enable them to integrate the latest technology, iterate designs, and thus sustain competitiveness in a dynamically changing high-tech environment.
# Model-Free Identification of Nonlinear Restoring Force with Modified Observation Equation
## 3 Numerical Application
### Building Structure Equipped with MR Damper  
The building structure equipped with MR damper on the first floor, as shown in Figure 1, was considered as the first example. The modified Dahl model [40], as expressed by Equations (26) and (27), was employed for the description of NRF provided by MR damper:  
\[\mathbf{R}_{\mathrm{Dahl}}^{non}~{}=~{}k_{d}s+c_{d}\dot{s}+f_{d}z+f_{0} \tag{26}\]  
where \(k_{d}\), \(c_{d}\), \(f_{d}\) and \(f_{0}\) are the coefficients of the model; \(s\) and \(\dot{s}\) are the relative displacement and relative velocity of the MR damper, respectively. Since the MR damper is located on the first floor, \(s\) and \(\dot{s}\) are equal to the relative displacement and relative velocity of the first floor, respectively. \(z_{i}\) is a dimensionless hysteretic coefficient using for the description of Coulomb friction:  
\[\dot{z}~{}=~{}\alpha\dot{s}\Big{[}1-z\cdot\mathrm{sgn}(\dot{s})\Big{]} \tag{27}\]  
in which \(\sigma\) is the coefficient controlling the shape of the hysteretic loop; \(\mathrm{sgn}(\cdot)\) denotes signum function.  
The parameters of the aforementioned model were set to be \(k_{d}\) = 25 N/m, \(c_{d}\) = 2000 N-s/m, \(f_{d}\) = 50 N and \(\sigma\) = \(1.0\times 10^{3}\) s/m. The El-Centro seismic excitation with peak ground acceleration (PGA) of 0.34 g was applied to the building structure. The nonlinear structural responses were calculated by using the Runge-Kutta method with a time interval of 0.001 s. It should be noted that after the calculation of structural responses, the NRF provided by MR damper was viewed as 'unknown fictitious input'. No assumption or prior knowledge of the NRF was made. The unknown quantities to be identified include the extended state vector \(\mathbf{Z}~{}=~{}\left[x_{1},\ldots,x_{6},\dot{x}_{1},\ldots,\dot{x}_{6},k_{1 },
# Review of the Research Progress in Soft Robots
## 4 Applications of the Soft Robots  
### Bionic Structures  
Bionic soft robot is a kind of soft robot made of materials with muscle characteristics. It can imitate the movement of various creatures in nature, and actively change its shape, size, and position in a complex environment according to the actual situation. It can adapt to environments that humans cannot reach and expand the scope of human work [126]. Compared with the traditional rigid robot, it has the characteristics of good continuity and strong adaptability.  
Currently, bionic robots can already achieve creeping crawling, leg crawling, jet swimming, fish-like swimming, wriggling, and other bionic movements [15]. Shown in Figure 9a, MIT invented a new telescopic drive system and applied it to a hybrid bio-inspired sea urchin robot. The robot was driven by extending and compressing the spine and was able to move over rocks and overcome obstacles [127]. Shown in Figure 9b, [PERSON] et al. created a compact and maneuverable underwater bionic robot fish that combined 3d-printed functional structures with embedded electronics to enable controlled movement underwater by converting electrical energy into mechanical energy in the tail fin [128]. Shown in Figure 9c, [PERSON] et al. designed a bionic jellyfish robot with multiple degrees of freedom. The Robotic Toolbox in Matlab analysis software was used to establish the model of the jellyfish manipulator, performing the motion simulation. The simulation results demonstrated that the motion curves were continuous and smooth without mutation, and could meet the needs of marine exploration and underwater reconnaissance [129]. Shown in Figure 9d, inspired by slender sharsucker, Professor [PERSON]'s team created a biologically similar multi-material biomimetic remora disc. The discs could attach to different surfaces and generate 340 times their own weight in tension, allowing the design of an underwater robot that could strongly adhere to a variety of surfaces [130].  
The bionic soft robot is made of super elastic silicone material. Thus, it has the advantages of good impact resistance, simple drive, continuous deformation, unlimited freedom, compatibility with obstacles, being non-toxic and harmless, etc. In the future, it has a wide application prospect in many fields such as military, production and living, rehabilitation, and medical treatment [131].
## 1 Introduction
(2019) proposed an unsupervised method to monitor the wear state of milling cutter based on sparse auto-encoder (SAE). The established SAE model extracted characteristics of the signal and completed the training of the model without supervision of the empirical label. However, the performance of the SAE process had a heavy dependence on the continuity of cutting, which was similar to a dimensionless method of threshold monitoring. This led to the failure of TCM when switching the cutting parameters according to the manufacturing procedure. Based on the above analysis, it can be seen that current TCM systems are often designed for specific cutting conditions, which means that different cutting conditions require different training processes. To the best of our knowledge, current monitoring systems are not competent to complete the TCM process when dealing with complex parts that involve multiple cutting methods and parameters. More flexible and adaptable TCM systems are needed to provide effective performance in these situations. Therefore, to solve the above deficiencies of the TCM system under variable machining processes, a clustering-based method is proposed in this paper. First, time-domain statistical features are extracted to construct the feature space. Then, a novel clustering method based on adjacent grids searching (CAGS) is proposed to find clusters, corresponding to different tool wear conditions, in the feature space. Second, an important statistical correlation between tool wear and the distribution of cutting force features is studied. The cluster density is used to mine sensitive information about tool wear that is not sensitive to cutting conditions. Third, a cluster density factor is proposed to convert the cluster density into the value reflecting actual tool wear and then the TCM process is accomplished.
# Scaling of FAPAR from the Field to the Satellite
## 4 Results and Analysis
### Discussion
However, for the black-sky FAPAR products (e.g., GEOVI\(\backslash\)CYCLOPES), the light interception measurement is preferred or the absorbed PAR from direct radiation should be separated from the measurement by energy balance monitoring.
# Study of the Ionospheric Scintillation Radio Propagation Characteristics with Cosmic Observations
## 3 Experimental Results  
### Temporal Statistical Feature of Amplitude Scintillation Index S4  
Figure 2 shows the histogram of yearly amplitude scintillation occurrence of different intensities in layers E and F. The probability of scintillation for each intensity in layer E changes slightly. Overall, weak scintillation accounts for the largest proportion (approximately 69%), while moderate scintillation ranks second and accounts for approximately 20%. The strong scintillation intensity is only approximately 11%. In the F layer, the weak scintillation intensity occurrence generally increases year by year, accounting for approximately 70% on average. The moderate and strong scintillation occurrences decrease with decreasing solar cycle, taking up 20% and 10%, respectively. In both the E and F layers, the scintillation occurrences decrease with the increasing in intensity. This is consistent with the conclusions of previous studies [25; 26; 27] that the probability of scintillation decreases gradually with increasing scintillation intensity.  
Based on Figure 2, the scintillation occurrence feature observed in the ionospheric E and F layers was summarized; it was found that the occurrence of weak scintillation in the E layer is lower than that in the F layer, indicating that weak scintillation occurs relatively less frequently in E layer. By contrast, for moderate and strong scintillations, the occurrence in the E layer is greater than that in the F layer. In general, with the increase in scintillation intensity, the amplitude scintillation occurrence decreased gradually.  
Figure 3 shows the occurrences of scintillation in the E and F layers at different latitudes from 2010 to 2019. The occurrence of different intensities of scintillation at low, middle and high latitudes in the E layer is almost the same. In the F layer, the weak scintillation occurrences at different latitudes show a similar trend, with a concave trend in 2012, which is the solar maximum in this solar cycle. For moderate and strong scintillation, similar trends are noticed. They first increased from 2010 to 2014 and then gradually decreased corresponding to the variation in solar activities.  
Figure 1: Framework of the proposed method.
## 3 Results and Discussion
### Overall Performance  
Table 5 shows the key performance parameters for the thermal and aerodynamic analysis of the S-CO\({}_{2}\) three-stage axial turbine. It can be found from the table that the flow rate obtained by turbine aerodynamic analysis is 184 kg/s and the output power is 10.37 MW. Compared with the thermal design, the flow rate only differs by about 1 kg/s, and the output power differs by only 0.37 MW. The results are in good agreement. It is worth noting that the total-total efficiency of the turbine obtained by aerodynamic analysis is 91.60%, which is 6.6% higher than the estimated efficiency of 85%. This shows that the S-CO\({}_{2}\) three-stage axial turbine designed in this paper has good aerodynamic performance to meet design requirements.  
Figure 10: Sketch of transcritical region on blade surface.
## 2 Reflection Power Estimation Using Eigenbeam Spatial Correlation Model
### Problem Formulation
Similarly, by examining \(\mathbb{E}\left\{H_{\mathrm{r}}H_{\mathrm{r}}^{*}\right\}\) based on (3), we can write the power of the reflected sound field component incoming from the direction \(\mathbf{\hat{y}}\) as  
\[P_{R}(t,k,\mathbf{\hat{y}}|\mathbf{y}_{o})=\mathbb{E}\left\{|G_{R}(t,k,\mathbf{\hat{y}}|\bm {y}_{o})|^{2}\right\}. \tag{5}\]  
We aim to estimate the reflection power \(P_{R}(t,k,\mathbf{\hat{y}}|\mathbf{y}_{o})\) from the RTFs \(H(x_{q},\mathbf{y}_{o},t,k)\)\(\forall\)\(q\) obtained using a spherical microphone array. Since \(P_{R}(t,k,\mathbf{\hat{y}}|\mathbf{y}_{o})\) is a spherical function, we can simplify its estimation using the spherical harmonic decomposition [35] given by  
\[P_{R}(t,k,\mathbf{\hat{y}}|\mathbf{y}_{o})=\sum_{p=0}^{\infty}\sum_{u=-p}^{p}\gamma_{ cu}(t,k|\mathbf{y}_{o})Y_{nu}(\mathbf{\hat{y}}) \tag{6}\]  
where \(\gamma_{cu}(t,k|\mathbf{y}_{o})\) are the reflection power coefficients and \(Y_{nu}(\cdot)\) is the spherical harmonic function of \(v\)th order and \(u\)th mode. Thus, we can calculate the reflection power for any incoming direction and time-frequency bin once we estimate \(\gamma_{nu}(t,k|\mathbf{y}_{o})\) coefficients.
## 2 Experimental Setup  
An experimental setup is fabricated to disclose the siphon flow characteristics with side air entrainment in the inverted U-tube. The experimental test rig with half scaled dimensions, compared to the CFD model dimensions, is fabricated. The experimental set up comprises two tanks with a water elevation difference of \(\Delta\)Z--see Figure 1. An inverted U-tube of 2.54 cm inner diameter, with its inlets immersed into the two water tanks, is utilized. The water levels in the tanks are changeable by lowering/lifting the right-side tank. The main filling port (2) in the left side tank and a water exit port in the right-side tank (12) is used to maintain the water levels between the two tanks constant during measurements. Bourdon pressure gauges (4) are utilized to measure the flow pressure around the system. The flowing water through the inverted U-tube is measured by using a calibrated tank and stopwatch while the side air is measured by using a variable area rotameter (7). The rotameter has a full-scale accuracy/repeatability of 6/2%. The rotameter has a variable flow area valve (8) to control the inlet air flow rate (9). The siphon flow generation is one of the tricky points before measurements. The pre-filling port (5) is used to fill the whole system with water while the air venting port (6) is used to release the air from the system cavity. The inverted U-tube inlets in the right- and left-side tanks are blocked during this system pre-filling by using manual valves.  
Once the whole system is full of water and free of air, the inverted U-tube inlets in the right- and left-side tanks are unblocked while maintaining the water pre-filling port (5) opened to help initiate the siphon flow for a while. Reaching a steady siphon flow is the main task that should be performed first. Then, the desired water elevation difference between the left and right tanks is obtained and kept constant. The water flow rate is measured before and after side air enters the system at the same \(\Delta\)Z. The air flow rate is changeable by using the variable area valve (8), which is also used to close the side pipe during the siphon flow initiation. The water level difference between the two tanks can be changed by adjusting the side air entrainment and the experiments are repeated for different flow conditions.
# Damage Detection of Insulators in Catenary Based on Deep Learning and Zernike Moment Algorithms
## 3 Insulator Damage Detection Based on Zernike Moment  
After the insulator is identified by the deep learning algorithm, the target range can be narrowed. However, since it is difficult to capture a large number of images of damaged insulators in catenary, and insulators usually account for a small area in the captured images, the accuracy of deep learning in judging insulator damage is limited. Therefore, theuse of non-deep learning methods is considered to complete insulator damage detection after insulators are identified.
# Person Re-Identification Based on Attention Mechanism and Context Information Fusion
## 3 Experiment
### Experimental Results
#### 3.3.1 Ablation Study  
In order to verify the effectiveness of the various modules of the person ReID model in this paper, experiments are carried out on datasets: Market1501 and DukeMTMC-reID. We sequentially add modules for model training. The Baseline is a model with ResNet50 as the backbone network and the loss function as cross entropy. L1 represents the attention module, L2 represents the context information fusion module, and L3 represents the combination of MSML and cross entropy loss. Tables 2 and 3 show the results of the ablation experiment. Rank-1 and mAP increased by 1.3% and 2.9%, respectively, on Market1501 and increased by 2.7% and 3.6%, respectively, on DukeMTMC-reID after adding the attention module into the baseline. Furthermore, rank-1 and mAP increased by 1.4% and 1.4%, respectively, on Market1501 and increased by 1.1% and 2.8%, respectively, on DukeMTMC-reID after adding attention mechanism and context information fusion module. Finally, on the basis of adding attention module and context information fusion module, the MSML and cross entropy loss are used to jointly train the model. On Market1501 dataset, Rank-1 and mAP reach 95.2% and 87.1%, respectively; on DukeMTMC-reID dataset, Rank-1 and mAP reach 88.9% and 78.5%, respectively. Experimental results show that the person ReID model proposed in this paper enhances the representation of pedestrian features and improves the accuracy of identification.
# Diagnosis and Treatment of MODY: An Updated Mini Review
## 2 Diagnosis of MODY  
Misdiagnosis of MODY with type 1 (T1 DM) or type 2 diabetes mellitus (T2 DM) can be avoided if clinicians can establish a correct molecular diagnosis, and with advances in genetic testing, aided by the development of new techniques (e.g., Next Generation Sequencing) and increased access to genetic testing facilities, diagnosis of MODY can be performed accurately [13]. However, in order to ensure an accurate diagnosis, specific criteria must be met before administering genetic tests [2]. MODY can be distinguished from other types of diabetes based on the age at which the disease first manifested. MODYsubtypes with variable age of onset, low penetrance, or atypical presentation may fail to meet the diagnostic criteria for the disease [14; 15; 16; 17]. Additionally, while a family history of diabetes is highly suggestive of MODY, some mutations in MODY-associated genes can occur at high frequencies in individuals without a family history of diabetes, demonstrating the critical nature of genetic testing in individuals without a family history of diabetes [18]. According to the MODY diagnostic guidelines, genetic testing should be performed on individuals diagnosed with diabetes at a young age (25 years), as well as those with a familial history of diabetes, evidence of endogenous insulin secretion, detectable levels of c-peptide, and negative antibody results [19]. Direct sequencing with sensitivity close to 100% and next generation sequencing methods can be successfully used to identify MODY gene mutations [1; 9; 20]. According to a model proposed by [PERSON] et al., a diagnosis before the age of 30 years is a useful discriminator between MODY and T2 DM, whereas a parental history of diabetes produces a 23-fold increased chance that a patient previously diagnosed with T1 DM may be diagnosed with MODY at a later stage [9].