esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Physical and Mathematical Model
### Boundary Conditions
#### 2.4.3 Leak Hole Condition Setting  
The primary factor influencing the flow rate of leakage through a small hole is the flow pattern of the gas as it escapes from the leak point. This flow pattern can be evaluated by utilizing the critical pressure ratio (CPR) [28].  
\[CPR=\left(\frac{2}{k+1}\right)^{\frac{k}{k-1}}=0.528 \tag{12}\]  
where \(k\) is the gas adiabatic index, taken as 1.29.  
China's national standard [15] stipulates that the towns and cities of the sub-high-pressure gas pipeline operating pressure levels were 0.4 MPa, 0.8 MPa, and 1.6 MPa. The calculated CPR values of the gas at the leakage holes in this study are all less than the ratio of the absolute pressure of the pipeline to the outlet pressure (the outlet pressure was set to 0.1 MPa because the HBNG leaks into the atmosphere), so the flow pattern of the gas at the leakage holes is acoustic flow. Therefore, the small hole leakage flow rate in this study is calculated as in Equation (13):  
\[Q=A_{D}C_{D}P_{T}\sqrt{\frac{Mk}{ZRT}\bigg{(}\frac{2}{k+1}\bigg{)}^{\frac{k+1} {k-1}}} \tag{13}\]  
Table 7 presents the mass flow rate of leakage from a sub-high-pressure HBNG pipeline with a DN300 pipe diameter at various sizes of leakage orifices.  
The initial gauge pressure of the leakage hole should be set when the gas velocity reaches the speed of sound. This study sets the initial gauge pressure according to [16], and for details see Table 8.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \begin{tabular}{c} Ventilation \\ Strategies \\ \end{tabular} & 6 Times/h & \begin{tabular}{c} 12 \\ Times/h \\ \end{tabular} & \begin{tabular}{c} 15 \\ Times/h \\ \end{tabular} & \begin{tabular}{c} 18 \\ Times/h \\ \end{tabular} & \
\begin{tabular}{c} 21 \\ Times/h \\ \end{tabular} &
\begin{tabular}{c} 22 \\ Times/h \\ \end{tabular} \\ \hline air velocity & 1.33 m/s & 2.66 m/s & 3.325 m/s & 3.99 m/s & 4.655 m/s & 4.87 m/s \\ \hline \hline \end{tabular}  
The unit time/h indicates air changes per hour.  
\end{table}
Table 6: The air velocity at the air supply outlet for various ventilation strategies.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \begin{tabular}{c} **Pipeline Pressure (MPa)** \\ \end{tabular} & \begin{tabular}{c} **Leakage Hole** \\ **Diameter (mm)** \\ \end{tabular} & \begin{tabular}{c} **Mass Flow Rate (kg/s)** \\ \end{tabular} \\ \hline \hline \begin{tabular}{c} 0.4 \\ 0.4 \\ \end{tabular} & \begin{tabular}{c} 0.0106 \\ 5 \\ 6 \\ \end{tabular} & \begin{tabular}{c} 0.0166 \\ 0.0239 \\ \end{tabular} \\ \hline \begin{tabular}{c} 0.8 \\ 1.6 \\ \end{tabular} & \
\begin{tabular}{c} 5 \\ 5 \\ 5 \\ \end{tabular} &
\begin{tabular}{c} 0.0298 \\ 0.0563 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 7: Leakage hole HBNG mass flow rate.
# Design of Multi-Modal Ship Mobile Ad Hoc Network under the Guidance of an Autonomous Ship
## 3 Ship Ad Hoc Network Construction
### Design of Network Mode
#### 3.2.2 Cluster Mode  
This mode adopts a hybrid layered topology of star-ring, as shown in Figure 3, which is suitable for use in coastal areas and special sea areas such as coasts, waterways, fishing areas, and anchorages. We adopt this network mode when the number of ships within a certain range from the ship reaches a certain number. In this case, the number of ships is large, and the environmental awareness and communication capabilities of each ship are very different. Therefore, the idea of layering is used to construct the network. Clustering is an effective network topology management mode. Its hierarchical structure improves the scalability of mobile ad hoc networks and reduces the difficulty of grid management. We select the node with strong communication ability as the cluster head node, which is responsible for managing the nodes in the cluster and maintaining the stability of the cluster. The cluster heads are connected by loops. In this way, cluster-head ships can communicate with each other, and cluster members can only communicate with their cluster heads.  
For such a network with a hierarchical topology, the nodes have different positions and functions. We need a cluster routing algorithm to solve the problem of cluster establishment and cluster maintenance in this ad hoc network mode, as well as the way of message broadcasting. Cluster establishment includes cluster head election, cluster member selection, cluster maintenance includes cluster head replacement, cluster member  
Figure 3: Star-ring hybrid topology of cluster mode.  
leaving, joining other clusters, cluster splitting and merging, etc. [33]. Typical hierarchical routing protocols mainly include Global State Routing Protocol (GSR), Dial On-Demand Routing (DDR), Host Specific Routing Protocol (HSR), etc. We can choose the appropriate clustering routing protocol according to the characteristics and navigation requirements of the clustering routing protocol.
# Design of an Open Remote Electrocardiogram (ECG) Service
## 5 Experimental Results  
We obtained the trace in Figure 1 with both doctor's and patient's devices in Italy and using a Heroku server in Europe (Ireland, from IP geo-location). Patient's device and doctor's laptop are located in a residential zone of a medium-sized town and are served by a home-grade WiFi router. The ECG is obtained using the simplest placement of electrodes: two bracelets on wrists and the ground electrode held tight on the thumb.  
On the left side of Figure 1, we see a timestamp and the corresponding count of lost samples. The loss rate shown in the figure is 3.4% after more than four minutes of operation; such loss rate is stable in the long term.  
Regarding autonomy, a patient's device consumes circa 200 mA (see Figure 6) with short spikes that reach 350 mA during transmission. In fact, we observed a continuous operation of more than six hours with the (nominal) 1.4 Ah 5 V _power bank_ shown in Figure 3: a ten hours autonomy requires a capacity of at least 2.4 Ah at 5 V, or 12 Wh, well below the threshold value found in Section 3.  
To understand the bottlenecks in our architecture, we individually tested its modules and communication links: the following is a synthesis of the results.
## 3 Results  
The results of this study are explained separately in two main discussions: aspect extraction and sentiment classification. Aspect extraction plays a crucial role in identifying specific attributes or components of a subject within the data, enabling a deeper analysis of  
Figure 3: Confusion matrix.  
opinions or sentiments related to those aspects. This task is challenging as it requires high precision in identifying and categorizing various elements within a diverse dataset.  
Aspect extraction aims to isolate and recognize specific components within reviews, such as services, curriculum, facilities, or lecturers, in the context of student reviews. Accuracy in this identification is essential because each aspect carries a unique context that can influence students' sentiments. Therefore, the success of aspect extraction determines the overall accuracy of the analysis, given that each aspect receives distinct perceptions or feedback from students. The technology used in aspect extraction must handle the complexities of language and context, particularly in Indonesian, which has structures and expressions different from those of other languages. This study relies on the IndoBERT model, optimized through fine-tuning with adjustments to hyperparameters such as batch size, learning rate, and dropout rate to ensure the model accurately recognizes and differentiates aspects.  
Effective aspect extraction holds strategic value, allowing universities or educational institutions to understand students' opinions better. For instance, by identifying specific aspects such as facilities or curriculum, institutions can pinpoint areas of primary concern or those requiring improvement based on direct feedback from students. Challenges in aspect extraction include maintaining a high level of precision, mainly when working with a varied dataset where language use or review context can differ significantly.  
The second part of this study's results focuses on sentiment classification, where the model determines whether the sentiment associated with each aspect is positive, negative, or neutral. Sentiment classification also demands high accuracy, as the model must be able to detect nuances in language that may alter the meaning or perception within reviews. With its capability for in-depth understanding of word context, the BERT model is used to classify sentiment based on the fine-tuned hyperparameters. Successful sentiment classification enables more accurate analysis and helps institutions understand students' satisfaction or dissatisfaction with each aspect. Accurately classified sentiment allows institutions to make quicker and more informed decisions based on processed data, such as improving services that may cause dissatisfaction or maintaining quality in areas that receive positive sentiment.
## 1 Introduction
[PERSON] [19] proposed a pointing calibration error model for a radio telescope, considering the nonlinear error of the azimuth axis. However, there are relatively few studies on the scanning error. Most existing studies focus on reconstructing far-field patterns through interpolation methods [20] to mitigate the influence of the scanning error on the electrical performance of antennas. Nevertheless, the scanning error still influences the accuracy of antenna holographic measurements. Moreover, current studies do not provide detailed analyses of the scanning error in antenna holographic measurements. Specifically, there is still an insufficient number of studies on the mechanisms by which the scanning error influences antenna gain loss and surface shape measurement accuracy. The purpose of this paper is to systematically study the influence of the feed offset error and the scanning error in microwave holographic measurement. The goals are to establish the mechanisms that influence gain loss and surface measurement accuracy and to identify effective compensation methods. Firstly, in Section 2, the relationship between the feed offset error and the scanning error on the far field of the antenna radiation is established by geometric optics and physical optics distribution. Secondly, in Section 3, the influence of the feed offset error and the scanning error on the far-field pattern and gain loss are studied, respectively. Then, in Section 4, through the two-dimensional Fourier transform relationship between the antenna far field and the aperture field, the influence mechanism of the feed offset error and the scanning error on the measurement accuracy of microwave holography is established. Finally, in Section 5, the compensation method and implementation steps of the influence of these two errors on the surface shape measurement accuracy are studied and verified by experimental cases.
## CFD Modeling
### CFD Solver  
The numerical simulations are conducted by using the industrial solver Ansys Fluent, which has been successfully employed for droplet breakup investigations [12; 14], as well as in applied CFD studies of bluff body flows by the present research group [21]. The solver utilizes the finite volume (FV) method to approximate the compressible flow, with the conservation principles being applied over each control volume [37; 38]. To accurately model the two-phase flow, both high-order spatial and time discretization schemes are employed. Specifically, the pressure-velocity coupling is addressed using the pressure implicit with the splitting of operators (PISO) scheme [39], which is commonly adopted for complex transient flows. As to spatial discretization, the pressure staggering option is used for the pressure field, the bounded-central differencing scheme is employed for the momentum equation, while the QUICK scheme [40] is adopted for the continuity and energy equations, as well as for the additional turbulence model equations. The compressive discretization scheme is used to solve the volume fraction advection Equation (3). As far as time integration is concerned, a second-order implicit method is used along with the VOF implicit scheme, while prescribing a low time step size, which is \(\Delta t=2\times 10^{-7}\) s, due to the restrictions of convection, diffusion, and surface tension effects.  
The practical computational domain corresponds to the entire physical domain described above, without imposing any symmetry condition at the midplane, as was performed in [19]. This way, fully three-dimensional simulations are conducted. Pressure inlet and pressure outlet boundary conditions are associated with the sub-domains corresponding to the post-shock and pre-shock air conditions, respectively. The adaptive mesh resolution ranges from 30 to 100 cells per original droplet diameter, with the finest mesh being used in the transition region between the two phases, as the total number of FV cells results in being about 10 million. The choice of this mesh resolution is based on our previous studies [14; 19], where a grid sensitivity analysis was completed. It is worth noting that the finest mesh size is comparable to that one recently used for under-resolved DNS in [15]. Nevertheless, the two approaches are completely different. Therein, a direct solution of the Euler equations was performed, where the numerical scheme inherently included artificial viscosity mimicking the effect of unresolved scales, without introducing any closure model. Very dissimilarly, the present study aims to determine the mean turbulent flow by solving the URANS equations supplied with a suitable turbulence model.
## Results
### Calibration of The e-TBM
The following parameters were chosen for the design spectrum of Eurocode 8 [19]: \(S=1.15\), \(a_{g}=0.35\cdot 9.81\) m/s\({}^{2}\), \(\beta=2.5\), \(T_{B}=0.2\) s, \(T_{C}=0.6\) s, \(T_{D}=2\) s, \(k_{1}=1\), \(k_{2}=2\), \(\eta=1\). A total of 10 acceleration time histories are generated with 30 s duration, 10 s stationary interval between 4 s and 14 s, and sampling rate, \(f_{s}=0.005\) s. Figure 4 compares the target spectrum (continuous line) and the mean response spectrum of the generated acceleration time series (dotted line). Figures 5 and 6 compare, respectively, the top level displacement and the base shear time histories of the detailed FE model (left panels) and the e-TBM (right panels) for one acceleration time series in the \(x\) direction. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **e-SBM** & **e-TBM** & **eS [\%]** & **FE Model** & **eF [\%]** \\ \hline \(\omega_{1}\) & 1.3991 & 1.4111 & 0.859 & 1.3003 & 8.520 \\ \(\omega_{2}\) & 1.4097 & 1.4256 & 1.130 & 1.3146 & 8.434 \\ \(\omega_{3}\) & 5.5019 & 5.5019 & 0.000 & 6.1787 & \(-\)10.946 \\ \(\omega_{4}\) & 5.6150 & 5.6152 & 0.004 & 6.3512 & \(-\)11.614 \\ \(\omega_{5}\) & 5.6200 & 5.6200 & 0.000 & 6.3859 & \(-\)11.986 \\ \(u\) & 0.4675 & 0.4675 & 0.000 & 0.4836 & \(-\)3.329 \\ \(v\) & 0.4780 & 0.4780 & 0.000 & 0.4933 & \(-\)3.102 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Natural frequencies of the three models and relative errors: between e-TBM and e-SBM (eS); between e-TBM and FE model (eF). Figure 3: Sensitivity analysis and minimum at the optimal design parameters: second inertia moments, \(I_{x}\) and \(I_{y}\), (**a**), shear areas, \(A_{s,x}\) and \(A_{s,y}\), (**b**) and cross-section area and torsional stiffness, \(A\) and \(I_{t}\) (**c**). and \(y\) direction using the e-TBM and the complete FE model (i.e., with all the beam and column elements in Figure 1) needed 104 s and 10 h, respectively. This result confirms the effectiveness of the proposed procedure to perform both global response structural optimization strategies and reliability analyses.
## 3 Status and Application
### Repeat-Pass Interference
#### 3.1.2 PS-InSAR  
In 2001, [PERSON] et al. [42] introduced the Permanent Scatterers InSAR (PS-InSAR) technique. Using statistical analysis of time-series SAR data over the same area, high-coherence points (i.e., PS points) were detected. By modeling and analyzing discrete PS points, the influence of atmospheric delay could be weakened, and surface deformation and elevation information could be accurately separated. This technique extracts stable and high-Signal-to-Noise-Ratio (SNR) features to eliminate noise and other interferences in the interference map. This method enabled the creation of sub-meter precision surface elevation models and millimeter-level surface deformation monitoring. It has proven  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Measurement** & **Precision Level** & **GNSS** & **D-InSAR** \\ \hline Spatial coverage & Discrete point & Discrete point & Surface covering \\ Accuracy & mm & mm & mm \\ Periodic velocity & Long and slow & Short and fast & Short and fast \\ Operating condition & According to the weather & All-weather & All-weather \\ Cost & High & higher & low \\ \hline \hline \end{tabular}
\end{table}
Table 2: Classification and D-InSAR technology.  
especially effective for monitoring infrastructure such as buildings, roads, and dams, often achieving an observation precision of 1 mm or even better [43].  
[PERSON] et al. [42; 44] successively proposed three methods for identifying permanent scatterers, namely the coherence coefficient threshold method, the amplitude deviation index threshold method, and the phase deviation threshold method. [PERSON] et al. [45] proposed the method using Signal-to-Clutter Ratio (SCR). [PERSON] et al. [46] proposed the StaMPS method, which selects permanent scatterers by using the maximum likelihood method and compares the results with those of traditional InSAR processing, as shown in Figure 7. [PERSON] et al. [47] proposed the SqueezeSAR method, which combines PS points and Distributed Scatterers (DS) and solves the problem by using the PS-InSAR processing flow, thereby improving the point density of deformation monitoring and expanding the application field of PS-InSAR technology. These methods are based on large data volumes and select permanent scatterers from a statistical perspective, which are not suitable for data processing with small datasets. Therefore, they limit the application of time series radar interferometry [48; 49; 50].  
Figure 6: InSAR national deformation map by Wuhan University [38].  
Figure 7: Comparison of wrapped multi-looked interferograms from (**top**) conventional InSAR and (**bottom**) the wrapped phase of individual PS, corrected for DEM error [46].
## 5 Performance Analytics  
We perform data writing and querying using the test tool Postman (version 8.0.6), testing shared data from the alliance chain, including text and image information. All the tests are repeated 20 times, each time sending the same type and size of data, and we record the response time of each request, ultimately taking the average of all the test results in order to ensure that the average response time is relatively stable and reliable. The system was tested for 50 rounds through the interface. The performance test results are shown in Figures 8 and 9. Among them, the average upload time for donkey pump and moving beam text data is 51.592 ms and 52.448 ms, respectively; the average download time is 31.64 ms and 32.25 ms, respectively; the average write time for the data is 786.571 ms and 785.896 ms; and the average query time is 65.376 ms and 67.700 ms, respectively, which can meet the needs of consumers and enterprises to obtain product information quickly. At the same time, data on donkey pumps and moving beams, key components of petroleum machinery and equipment, were used for comparisons during the testing process. The results show that the uploading, writing, querying, and downloading times for the two types of parts tend to be the same, and the system is more stable.  
Figure 7: Example of a traceability query.
# A Decision Support System for Melanoma Diagnosis from Dermoscopic Images
## 5 Database Used for CAD System Validation  
The publicly available PH\({}^{2}\) database is the dataset used as a test-bench for the method validation [22]. A total of 200 dermoscopic images divided into 80 atypical nevi, 80 common nevi, and 40 melanomas formed the dataset being tested. Images were 8-bit RGB with a 768 \(\times\) 560 pixel resolution captured by a lens with a 2\(\times\) magnification.  
The PH\({}^{2}\) database includes manual segmentation, clinical and histological diagnosis of the skin lesion, as well as other important dermoscopic criteria, such as the assessment of the lesion asymmetry, and the identification of colors, pigment network, dots, globules, streaks, regression areas, and the blue-whitish veil. All the included dermoscopic images are from skin type II or III according to the Fitzpatrick skin type classification. Accordingly, skin colors in the PH\({}^{2}\) database range from white to cream white. The manual segmentation  
Figure 6: (**a**) Dyschromia image obtained considering the image segmented in coherent color areas. (**b**) Corresponding flattened image.  
of skin lesions is provided by expert dermatologists and is available as a binary mask in which pixels with an intensity value of 1 correspond to the segmented lesion, while pixels with a value of 0 correspond to the background. For each image, the following information is included:  
* A ground truth binary image, manually generated by expert dermatologists, containing the skin lesion area.
* Clinical and histological diagnosis.
* Dermoscopic criteria.  
All the dermoscopic criteria indicated in the database take into account most of the features that need to be considered during diagnosis procedures, such as the ABCD rule, the seven-point checklist, and the Menzies method.