esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
The first descriptor (T) assesses the characteristics of the primary tumor such as its size, local invasion, and the presence of satellite tumor nodules. The CT modality performs T-staging well, because information about the tumor can be easily obtained from the CT scans. The second descriptor (N) assesses the involvement of the regional lymph nodes. On the CT scans, lymph nodes appear as small opacities with unclear silhouettes; thus, the CT modality performs weakly for N-staging [8; 9]. The last descriptor (M) assesses the invasion of the tumor to the extra-thoracic organs, especially the brain, adrenal glands, liver and bones, and so forth [9]. Staging of metastatic tumors usually requires additional examinations of the invaded body regions. For this reason, we focus only on the T-staging of tumor nodules in this study. T-stages can be further divided into sub-stages depending on the anatomic characteristics of the primary tumors. Table 1 describes the detailed definitions of the T-stages, as determined by the eighth edition of the TNM system. \begin{table}
\begin{tabular}{l c c} \hline \hline \multirow{2}{*}{**Stages**} & \multicolumn{2}{c}{**Sizes (in Greatest**} & \multirow{2}{*}{**Invasion**} \\  & & **Dimension** \\ \hline Tx & & Tumor cannot be assessed \\ \hline T0 & & No evidence of primary tumor \\ \hline Ts & & Carcinoma in situ (pre-cancer) \\ \hline T1 & \(\leq\)3 cm & Surrounded by lung or visceral pleura. Invades the lobar \\  & T1a & \(\leq\)2 cm & bronchus but not the main bronchus. \\  & T1b & \(>\)2 cm and \(\leq\)3 cm & Invades the main bronchus, regardless of distance from the \\ \hline T2 & \(>\)3 cm and \(\leq\)5 cm & & \\  & T2a & \(>\)3 cm and \(\leq\)4 cm & carina but does not invade the carina. Invades the visceral \\  & T2b & \(>\)4 cm and \(\leq\)5 cm & pleura. Invades the hilar region. \\ \hline T3 & \(>\)5 cm and \(\leq\)7 cm & \
\begin{tabular}{c} Invades the chest wall, phrenic nerve, and parietal \\ pericardium. Presents satellite nodules in the same lobe. \\ \end{tabular} \\ \hline T4 & \(>\)7 cm &
\begin{tabular}{c} Invades the diaphragm, mediastum, heart, great vessels, \\ and trachea. Presents satellite nodules in the different lobes. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: T-staging of lung tumors.
## 7 Discussion  
### Clinical Applicability  
These findings support the integrative role of clinical variables such as smoking history, walking ability, lung function, and quality of life in the context of COPD assessment. The use of both correlation matrices and chi-square tests provides a comprehensive statistical foundation, which is visually and numerically presented in Figures 7 and 8 and Table 10, directly supporting the analytical conclusions of the study.  
The overarching objective of this study was to develop a predictive framework for stratifying COPD severity that is not only methodologically sound but also translatable to routine clinical practice. The use of GLMs was motivated by their inherent interpretability, statistical robustness, and compatibility with structured clinical data. Unlike more opaque machine learning algorithms, GLMs yield coefficients that are directly interpretable, facilitating their integration into evidence-based decision-making processes.  
To operationalize this framework in clinical environments, the model is envisioned to be embedded within a digital decision-support tool--such as a web-based platform or a mobile application. Such an interface would enable clinicians to input patient-specific covariates (e.g., age, FEV\({}_{1}\), pack-year history, six-minute walk distance, smoking status) and obtain real-time estimates of COPD severity. This would obviate the need for manual computation and streamline risk assessment workflows in outpatient and primary care settings.  
From a clinical management perspective, identifying patients at elevated risk of transitioning to more severe stages of COPD enables earlier intervention. Physicians can leverage the model's predictions to initiate intensified treatment regimens, optimize pulmonary rehabilitation strategies, or recommend targeted lifestyle modifications. By integrating predictive analytics into routine assessment protocols, healthcare providers are better equipped to anticipate disease progression and tailor management accordingly.
###### Abstract  
The movement and deformation of rock strata and the ground surface is a dynamic deformation process that occurs as underground mining progresses. Therefore, the dynamic prediction of three-dimensional surface deformation caused by underground mining is of great significance for assessing potential geological disasters. Synthetic aperture radar interferometry (InSAR) has been introduced into the field of mine deformation monitoring as a new mapping technology, but it is affected by many factors, and it cannot monitor the surface deformation value over the entire mining period, making it impossible to accurately predict the spatiotemporal evolution characteristics of the surface. To overcome this limitation, we propose a new dynamic prediction method (InSAR-DIB) based on a combination of InSAR and an improved Boltzmann (IB) function model. Theoretically, the InSAR-DIB model can use information on small dynamic deformation during mining to obtain surface prediction parameters and further realize a dynamic prediction of the surface. The method was applied to the 1613 (1) working face in the Huainan mining area. The results showed that the estimated mean error of the predicted surface deformation during mining was between 80.2 and 112.5 mm, and the estimated accuracy met the requirements for mining subsidence monitoring. The relevant research results are of great significance, and they support expanding the application of InSAR in mining areas with large deformation gradients.  
InSAR; mining subsidence; dynamic prediction; model improvement; parameter inversion Article
## 1 Introduction
### Aims and Novelty  
To address these shortcomings, this work has come up with a novel idea that not only alleviates the issue for maritime context, but the extension of work can fundamentally address the same issue for any inherent stochastic process. In this order, a generic geometric shape has been excited by the wave in the model test basin to collect quality and lean input-output data of wave and floating body responses. Later, the obtained time series is segmented into small observation windows. To capture as many features as possible from the data, the continuous wavelet transformation in various scales has been applied to the time series. The resulting coefficients reflect the saliency map. In the next stage, through feature engineering and data processing that will be explained, the data has been reorganized and compressed while preserving the salient features. The engineered matrices are labeled with the respective wave information and proceed for analysis and training. The innovative setup results in a significant reduction in training time and computational power while preserving the data features and reasonable classification accuracy. To better highlight the advantages and effectiveness of the current setup, an identical dataset has been tested for Convolutional Neural Network (CNN) and Neural Network (NN) to compare the performances and showcase the efficiency of the novel approach. The current framework could potentially replace generic ML/DL models for practical applications that require long training, with the goal of integrating these models into onboard systems with minimal computational overhead. The structure of the remaining content is as follows: Section 2 provides an overview of the data generation process, Section 3 outlines the data processing framework, Section 4 evaluates the efficiency of the proposed technique, and finally, Section 5 concludes the paper and discusses future directions.
# Analysis of Equivalent Flexural Stiffness of Steel-Concrete Composite Beams in Frame Structures
## 5 Simplified Design Method  
As discussed above, the rotation restraint stiffness at each beam end is the dominant factor in calculating the equivalent flexural stiffness of the composite frame beams. In the proposed formula, the rotation restraint stiffness of the frame beam is represented by parameter K, which needs to be determined in advance. However, the calculation of the rotation restraint stiffness--the value of parameter K--requires the calibration of the line stiffness for the adjacent structural members, such as the beams and columns connected to the considered frame beam. Thus, it is still complex to use the proposed formula to evaluate the equivalent flexural stiffness of a frame beam. To overcome this drawback in practical design, a simplified method for calculating the rotation constraints at the beam ends and a design formula for evaluating the vertical deflection of the frame beam are proposed in Sections 5.1 and 5.2, respectively.
## 4 Discussion
### Effects of Different Factors on the Capillary Water Absorption of Concrete
#### 4.1.4 Effect of the Preload Level  
According to recent studies, damage occurring under a load is closely related to the capillary water absorption coefficient of cement-based materials. [PERSON] et al. [57,58] found that cracks induced in concrete under preloading substantially affected its capillary water absorption. Using neutron ray imaging technology, [PERSON] et al. [59] found that the crack propagation region had a stronger water absorption capacity than the intact region in cement-based materials. Figure 8 shows the effect of the preload level, \(\lambda_{\text{c}}\), on the capillary water absorption performance of cement-based materials, based on results from the literature [30,39,60,61,62,63,64,65,60] and two sets of UHPC test results; the capillary water absorption coefficient was normalized. The relationship between the capillary water absorption coefficient of cement-based materials and the preload was calculated using Equation (6) (correlation coefficient R\({}^{2}\) = 0.697). When the preload did not exceed 38%, the capillary water absorption coefficient of cement-based materials decreased continuously with an increasing preload level. When it exceeded the critical value of 38%, the capillary water absorption coefficient increased rapidly with increasing preload levels.  
Figure 8: Effect of the preload level on \(S_{1}\).  
Figure 7: Effect of the ground granulated blast furnace slag dosage on \(S_{1}\).  
For cement-based materials, when the applied compressive stress did not exceed the critical stress value, the interior was more compact than when the compressive stress exceeded the critical stress value, because excessive stress caused more cracks to form inside the cement-based material, the pore structure inside the specimens was destroyed, the water absorption capacity was increased, and the capillary water absorption coefficient increased. Thus, the preload level exerted a statistically significant effect on the capillary water absorption coefficient.  
\[\left\{\begin{array}{l}S_{\text{rf}}=0.980-0.00824\lambda_{\text{c}},\lambda _{\text{c}}\leq 38.85\%\\ S_{\text{rf}}=0.6599+0.02967(\lambda_{\text{c}}-38.85),\lambda_{\text{c}}>38.8 5\%\end{array}\right., \tag{6}\]  
where \(S_{\text{rf}}\) is the normalized \(S_{1}\) under different preload levels, and \(\lambda_{\text{c}}\) is the preload level (%).  
As described above, we analyzed the individual effects of the W/B, mineral admixtures, and preload damage on the capillary water absorption coefficient. Further studies are needed to determine the effects of additional factors, such as specimen size, ambient temperature, fiber addition, and fiber type, on the capillary water absorption of cement-based materials.
## 4 Effect of Design Parameters on Strength
### Effect of W/B Ratio on Compressive Strength and Split Tensile Strength  
There is a need for mineral admixture in the mix not only for the reduction in the cost of the mix but also to increase the efficiency of the mix. Hence, in the literature, there are different types of mineral admixtures that are added to increase the fresh and hardened property efficiencies of SCC with recycled aggregates. Ingredients that contribute to increasing the binding capacity of concrete are known as binders. In the literature, there are different types of admixtures or binders that are added to the SCC mix in order to counteract the negative impact caused by a recycled aggregate or to improve the specific requirement of SCC itself. Similar to the W/C ratio, the lower the W/B ratio the higher the compressive strength is observed for SCC from Figure 4. The higher W/B ratio lowers in compressive strength. An increase in binder content results in a decrease in compressive strength because it will reduce the formation of the increased amounts of hydration products which is required for the enhancement of compressive strength. Furthermore, an increase in binder content results in rendering the binder content as filler material rather than a reactive ingredient in the mixture.  
From Figure 5, it is observed that an increase in the W/B ratio results in a drop in split tensile strength and it is also observed that the range of split tensile strength is from 2.0 MPa to 4.5 MPa for the W/Br ratio of 0.25 to 0.45. For the lower compressive strength grade of concrete, the split tensile strength is observed to be less when the W/B ratio is higher.  
Figure 4: Effect of the W/B ratio on various compressive strengths of SCC concrete with recycled aggregate according to the literature.  
Figure 3: Effect of W/C ratio on various split tensile strength of SCC concrete with recycled aggregate according to the literature.
## 4 Proposed System Design
### FSO Channel
#### 4.2.2 Dust Storm Weather Conditions  
Dust storms, caused by strong winds that pull the dust from the ground up into the air, have effects on the FSO link. The World Meteorological Organization (WMO) classifies dust storms according to their strength into four categories: dust haze (DH), light dust (LD), medium dust (MD), and heavy dust (HD) [39]. Dust storms also cause attenuation during the transmission between the transmitter and receiver that degrades the received signal. This attenuation, \(D_{a}\), in dB/km, that depends on \(R\) can be expressed as [27]:  
\[D_{a} =52\;\times\;R^{-1.05} \tag{11}\]  
Table 3 shows the visibility and the attenuation corresponding to different levels of dust storms [9; 24].
# Re-Optimized Design for the ADR-Based Dual-Loop Controller of 100 m Class Aperture Radio Telescope
## 4 Simulation Results and Analysis
### Position-Loop Controller Simplification Analysis  
In the closed-velocity DLADRC controller case, we analyze the position response performance of the system with the proportional (P) controller and the lead-lag compensator (denoted PD), respectively.  
Figure 5(a) presents the position response curves of the system under the P controller of \(K_{\text{P}}=16\) and the PD of \(G=16\frac{0.05s+1}{1/10s+1}\), respectively. It can be seen that there is not much difference in the system's response curves under these two controllers, except that the PD-controlled system arrives at the steady-state response slightly faster. When the coefficient \(K_{\text{d}}\) of the derivative term in the lead-lag compensator is increased to 0.55, the response curve is shown in Figure 5(b). The response performance of the lead-lag compensator at this time is not as good as that of the P controller. Figure 5(c) presents the motor's input voltages of the PD are convergent, input fluctuate more than those of P-DLADRC, which is terrible for the achievement of high-performance control.  
Table 2 lists four kinds of statistical values for the overall response errors of the two methods; it can be seen that the difference between the correspondence errors is very tiny. Therefore, we can conclude that a pure proportional controller in the position-loop is sufficient under the same speed controller.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **RPID** & **LADRC** & **DLADRC** \\ \hline Mean error & 0.0710 & 0.0076 & 0.0052 \\ Max. error & 1.0987 & 0.3457 & 0.2836 \\ Median error & 0.0002 & 0.0000 & 0.0000 \\ RMS error & 0.2255 & 0.0480 & 0.0362 \\ Response time & 20 & 8.8 & 8.8 \\ \hline \end{tabular}
\end{table}
Table 1: Tracking errors (in deg/s) and response times (in s) of motor speed responses with three control methods in wind disturbances.  
Figure 5: Motor response curves of the system with three controllers. (a) Velocity responses and (b) input voltages.
# Development of a New Pasta Product by the Incorporation of Chestnut Flour and Bee Pollen
## 3 Results and Discussion
### Texture  
In Figure 7, the textural parameters analyzed (firmness, stickiness and adhesiveness) are presented for fresh and dried pasta (both uncooked and cooked). The fresh control sample showed a firmness of 10 \(\pm\) 0.5 N and the partial addition of chestnut flour (FC4-C50) or pollen powder (FC4-C50P10) did not result in a visible change of this textural parameter. In this case, independently of the addition of chestnut flour and pollen powder, the presence of egg (and particularly the egg white) in fresh samples may contribute to the formation of a compact protein network in pasta, giving origin to a firmer product [69]. Even though gluten has been associated with the firmness of pasta, the eventual lower gluten content in samples with the addition of chestnut flour did not seem to have a significant effect on this textural parameter. Moreover, the egg lipids' interaction with amylose, while cooking the pasta, limits its solubility in water, giving the same firmness to the fresh cooked pasta, independently of the addition of chestnut flour or pollen powder.  
However, in the formulation with both ingredients (FC4-C50P10), the firmness was slightly increased to 15.52 \(\pm\) 1.30 N, and the cooking process led to a lower firmness of cooked pasta texture. The decrease of firmness might be attributed to the higher water absorption during the process [55].  
The drying process increased the firmness of the control pasta (FC4) by a factor of five, because the low water content of the dried product originated pasta with a hard and coarse texture (52.70 \(\pm\) 4.94 N). However, the presence of chestnut flour and pollen powder in the dried sample (FC4-C50P10) decreased the firmness of pasta to around 33.35 \(\pm\) 3.52 N. Contrary to the fresh pasta, the cooking of dried pasta decreased significantly the firmness of cooked pasta that results from the uptake of water absorbed during cooking.  
Figure 6: Color difference in relation to control.  
Stickiness and adhesiveness are usually associated with the formation of a surface layer of exudates (amylose and amylopectin) that give a clumping tendency to the strands [70]. Regarding these texture parameters, the addition of chestnut flour to pasta (fresh and dried) or cooking process resulted in stickier and more adhesive pastas [70]. Pasta cooking quality is determined by the physical competition between protein coagulation in a continuous network and starch swelling with exudate losses during cooking. If the later effect prevails, a pasta with a soft and usually sticky texture is obtained. Thus, the stickier nature of the cooked pasta might be due to the amylose and amylopectin solubilization and protein coagulation in discrete masses lacking a continuous framework [54; 70].
## 2 System Description
### Modeling of the Radial Inflow Turbine  
Volute, stator vanes, and rotor blades are the three primary parts of the radial inflow turbine, as depicted in Figure 2. Figure 3 presents a meridional view of the turbine stage. The flow is firstly collected in the volute, accelerated, and further expanded through the stator vanes and directed to the rotor blades at the proper angle. Finally, the fluid rotates the rotor blades, converting the kinetic energy into mechanical power. The turbine is coupled to an electrical generator to convert the mechanical power into electrical power. The design process is depicted in Figure 4 and detailed in the following subsections. Figure 5 presents an enthalpy-entropy diagram of the turbine stage.  
Figure 3: Meridional view of the turbine stage.  
Figure 2: Turbine architecture (**left**) and velocity triangles of the rotor (**right**).
## 3 Results
### Validation
The error is slightly high for the discharge voltage \(V_{d}\) because this value has been assigned, as specified in the previous section, and for the total efficiency \(\eta_{T}\), probably because the function shown in Figure 3 has been obtained through data from conventionally sized thrusters. Overall, the data shown in Tables 2-8 confirm the validity of the model both for normal-sized and miniaturized ion thrusters; the performance and working parameters obtained from the model are quite similar to the parameters related to the ion thrusters chosen for the comparison. Furthermore, for NSTAR, the methodology described in the previous section to define the magnets dimensions and the anode thickness (Figure 8) was applied. As described previously, these parameters could be obtained through an FEMM simulation. For this simulation, obviously, as regards the materials of the magnets and the discharge chamber, the same materials used by the NSTAR ion thruster have been selected. In particular, three samarium-cobalt magnet rings, with alternating polarity, have been used. The first magnet ring has been placed in the region of the discharge cathode, the second at the discharge chamber conic-cylinder intersection, and the last near the ion optics. Titanium has been used as the material for the discharge chamber. The results showed good agreement with the expected values, e.g., the width of the front magnets obtained was about 5 mm and the anode wall thickness 1.5 mm. The magneto-static solution obtained by FEMM is shown in Figure 9.  
Finally, the developed tool has been compared with another tool proposed in the literature, which makes use of a simple macroscopic plasma-source simulation (SMPS) code with and genetic algorithm (GA) [26]. The tool developed in Ref. [26] takes in input the input power \(P_{in}\), the net accelerating voltage \(V_{b}\), grid parameters, beam diameter \(D_{b}\), and the discharge chamber length \(L\); the outputs of the model are the flow rate of the discharge chamber \(\bar{n}_{pd}\), the beam current \(I_{b}\), the discharge current \(I_{d}\), the discharge voltage \(V_{d}\), the thrust \(T\), and the propellant utilization efficiency of the discharge chamber \(\eta_{md}\). In Table 9, the results of the two models in predicting NSTAR ion thruster data in TH15 operating condition are shown. As the input of the developed tool, it was decided to provide the power \(P_{in}\) of NSTAR instead of the thrust \(T\) for a better comparison. The results in Table 9 show that the developed tool makes slightly lower errors and also allows one to obtain more performances and design parameters than the tool in Ref. [26]. Figure 9: NSTAR: rebuilt magnetic field obtain by FEMM software. The discharge chamber diameter in the cylindrical part is 30 cm, and the total length is 23 cm. The width of the front magnets is 5 mm, and the width of the rear one is 10 mm. Titanium is the discharge chamber wall material, and samarium cobalt is the magnet’s material.