esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Analysis
### Results of Asymmetrical Construction
#### 4.1.2 Deformation of Tunnel Frame Structure  
An analysis of the tunnel frame structure is shown in Figure 9 by taking the tunnel frame of the \"Huayuan-Luzhou\" Crossing Point as an example. Figure 10 shows the deformation curve of the bottom slab and top slab of the tunnel frame structure at the crossing point. The position of the middle wall in tunnel frame structure is set as the origin of the axial coordinate (see Figure 9). The bottom slab is floating during the asymmetric excavation of the connecting section. The maximum vertical deformation of the bottom slab occurs on the side next to the excavation position, and it is approximately 16 mm during the construction. The construction of the tunnel frame structure causes the slab settlement. At the end of construction, the vertical deformation tends to be stable, with a uniform value of about 6 mm. As for the top slab, the deformation behavior is similar to that of the bottom slab, which means there is little relative deformation between these two slabs.  
Figure 11 shows the deformation of the northern wall, middle wall and southern wall of the tunnel frame structure. The deformation behaviors of these structural walls are quite similar. A deformation towards the soil is observed at the structural walls with the maximum value at the bottom during an asymmetric excavation of the soil behind the supporting pile. Generally, the pile-cut-induced deformation of structural walls is slight and negligible. During the construction of the connecting section, the wall moves backward towards the soil and subsequently induces an inclination of the structural wall. The deformation of the structural wall is well controlled, and it turns vertical with an even value within 0.5 mm. The overall deformation trend of the structural wall is generally consistent with that of the supporting pile.  
Figure 10: Vertical deformation of structural slab subjected to asymmetric construction: (**a**) bottom slab and (**b**) top slab (unit: mm).  
Figure 9: Typical analysis section of tunnel frame structure.
## 3 Case Study  
### Engineering Situation  
A hydropower project located in the lower reach of the Jinsha River in Southwest China has a design normal reservoir level of 825 m, a dam crest elevation of 834 m, and a concrete double-curvature arch dam with a height of 289 m. The water cushion pond is located behind the dam, with a cross-sectional profile of a flat-bottomed composite trapezoidal shape. The elevation of the pond's bottom slab is 560 m, its length is 400 m, and the width of the bottom slab is 80 m. The side slopes of the bottom slab are excavated at a 1:1 slope (with a 1:0.6-1:1 slope on the right bank). Two 5 m-wide access roads are set at elevations of 580 m and 601 m. The top elevation of the water cushion pond is 634 m, with a total width of 213.6 m, and a curved waveguide wall is installed at the top. The slopes above the water cushion pond extend from the dam base at 0 m to 900 m. The left bank elevation ranges from 634 m to 950 m, while the right bank elevation ranges from 634 m to over 1100 m.
## 3 Results and Discussion
### Fit-Atr  
Figure 9 shows the FTIR spectra of TMS plasma-modified rayon flock synthetic leather with different flow rates of helium. Figure 9a shows the FTIR spectrum of untreated sample while the TMS plasma-modified samples exhibit features of increase in the peak of 3790 cm\({}^{-1}\) (OH stretch) and 1016 cm\({}^{-1}\) (Si-O-Si antisymmetric stretch), as shown in Figure 9b-e. The FTIR-ATR results confirm the deposition of TMS on sample surface. As the flow discharge increases from 30 W to 50 W, the silicon-related absorbance increases. The greatest intensity of the silicon-related peaks occurs at a discharge power of 50 W, but it drops at 60 W. This indicates that a discharge power of 50 W facilitates the deposition of TMS. In addition, after plasma treatment, the intensity of peaks at 2900-3000 cm\({}^{-1}\) increases significantly, which also indicates that TMS was deposited in the sample surface.  
Figure 8: The change of surface energy of TMS plasma modified rayon flock synthetic leather (treatment parameter: amount of TMS = 0.2 mL; flow rate of helium = 12.5 LPM; jet distance = 10 mm; treatment time = 30 s).  
Figure 9: FTIR spectra of rayon flock synthetic leather after TMS plasma treatment with different discharge powers: (**a**) untreated, (**b**) 30 W, (**c**) 40 W, (**d**) 50 W, and (**e**) 60 W. (Amount of TMS = 0.2 mL; flow rate of helium = 12.5 LPM; jet distance = 10 mm, treatment time = 30 s.)
## 3 Offline Optimization and Adaptation Under Nonlinear Task Constraints
### Optimization and Adaptation Procedure
#### 3.3.2 Adaptation Procedure
To minimize the norm \(\left\|\mathbf{q_{v}}-\mathbf{J^{\#}x_{v}}\right\|\) in Equation (54), while ensuring that the resulting joint velocity vector adheres to the optimized distribution at each time step, we define the loss function \(L(\mathbf{\delta_{v}})\) as follows:

\[L(\mathbf{\delta_{v}})=\parallel\



where \(\mathbf{\Sigma}_{q_{\mathbf{\
u}}}=\text{diag}([\mathbf{\sigma}_{q_{\mathbf{\
u}}}^{1},\mathbf{\sigma}_{ q_{\mathbf{\
u}}}^{2},\ldots,\mathbf{\sigma}_{q_{\mathbf{\
u}}}^{7}])^{2}\) represents the diagonal covariance matrix composed of the standard deviations \(\mathbf{\sigma}_{q_{\mathbf{\
u}}}\) for the offline-optimized 7 DoF joint velocities.

In addition to preserving the joint distribution, we also need to perform real-time collision detection between the robot's links and obstacles with dynamic modulation to achieve null-space velocity control. Figure 3 demonstrates the null space velocity control strategy. We detect the closest point on the robot non-end-effector to each obstacle and enforce velocity \(\mathbf{x}_{\mathbf{\
u}}^{0}\) at that point if the point is within the obstacle safety margin \(\mathbf{d}_{m}\). The vector \(\mathbf{d}_{0}\) starts from the obstacle surface to the point \(C_{0}\), with the same direction as \(\mathbf{x}_{\mathbf{\
u}}^{0}\), and \(\mathbf{J}_{0}\) is the Jacobian matrix from the base to point \(C_{0}\). We further denote the end-effector velocity \(\mathbf{x}_{\mathbf{\
u}}\) and the point \(C_{0}\) velocity \(\mathbf{x}_{\mathbf{\
u}}^{0}\) with Equation (53):

\[\mathbf{x}_{\mathbf{\
u}}=\mathbf{J}_{\mathbf{q}_{\mathbf{\
u}}}\mathbf{x}_{\mathbf{\
u}}^{0}=\mathbf{J}_{0} \mathbf{q}_{\mathbf{\
u}} \tag{61}\]

We substitute Equation (54) to \(\mathbf{x}_{\mathbf{\
u}}^{0}=\mathbf{J}_{0}\mathbf{q}_{\mathbf{\
u}}\) with \(\mathbf{\delta}_{\mathbf{\
u}}\) derived as folllows:

\[\mathbf{\delta}_{\mathbf{\
u}}=(\mathbf{J}_{0}\mathbf{N})^{\#}\Big{(}\mathbf{x}_{\mathbf{\
u}}^{0}-\bm {J}_{0}\mathbf{J}^{\#}\mathbf{x}_{\mathbf{\
u}}\Big{)} \tag{62}\]

To achieve a trade-off between maintaining consistency with the optimized distribution and ensuring a safe distance from the robot non-end-effector, we define the coefficients \(\gamma>0\) and \(\rho>0\) satisfying \(\gamma+\rho=1\).
## 2 Data Analysis
### IGS Global Troposphere Products  
With the improvement of the earth observation network and the increase of observation data, the tropospheric delay transitions from a simple closed mathematical model to a correction model that relies on a large amount of external data [13]. At present, many organizations have provided global tropospheric zenith delay products. These products use different data sources and projection functions, so their accuracy and application areas are different. IGS (International GNSS Service) publishes global tropospheric zenith delay products based on the results of multiple analysis centers, with an accuracy of 4 mm and a wide range of applications [14]. In the process of solving ZTD, it is considered as an undetermined coefficient and solved using six different types of data processing software [15]. Because of the high accuracy of the ZTD issued by IGS, it is often used as a reference true value.
## 2 Numerical Modeling of the AGF Process including Phase Transition Conditions
### Simulated Working Conditions  
The freezing scheme shows that the cooling brine circulation from the 1 st day to the 10 th day, the temperature is reduced by 2.8 \({}^{\circ}\)C per day; the active freezing period is from the 10 th to the 35 th days, and the temperature is stable at \(-28\)\({}^{\circ}\)C. The maintenance freezing period is from the 35 th to the 43 rd days, and the temperature is \(-22\)\({}^{\circ}\)C. When the active freezing period is completed, the frozen soil curtain reaches the design standard, the temperature boundary of the shield shell is activated in turn, and the excavated soil is converted into air.  
The first model is based on the normal working condition without considering the heating effect of the shield machine, which can be used to verify the model. Then, the following three working conditions are proposed for the simulation analysis: the shield tunneling considering the heating effect of shield machine (Condition 1), the continuous rotation working condition of tunneling at the same position due to encountering hard rock formations (Condition 2), and the temporary shutdown condition of tunneling at the same position due to construction arrangements, equipment fault, or other reasons (Condition 3). The corresponding model boundary of each extreme working condition is shown in Figure 5.  
\begin{table}
\begin{tabular}{c c} \hline
**Position** & **Temperature/\({}^{\circ}\)C** \\ \hline Ambient temperature & 20 \\ Initial soil temperature & 20 \\ Shield cutterhead & 20 \\ Shield shell & 60 \\ Freezing pipes & According to simulated conditions \\ \hline \end{tabular}
\end{table}
Table 1: The temperature values of the model.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Frozen Soil** & **Unfrozen Soil** & **Air** \\ \hline Thermal conductivity/(W/(m\(\cdot\)K)) & 1.79 & 1.18 & 0.023 \\ \hline Specific heat capacity/(J/(kg\(\cdot\)K)) & 1610 & 1530 & 1003 \\ \hline Density/(kg/m\({}^{3}\)) & 1880 & 1880 & 1.29 \\ \hline \end{tabular}
\end{table}
Table 2: The thermophysical parameters of materials.
## 2 Numerical Methods and Model Setup
### Wall Film Model
#### 2.4.2 Governing Equations for Wall Film  
The wall film model in this study is based on [PERSON] work. The governing equations of mass, momentum, and energy for individual parcels are as follows:  
\[\dot{N}=\frac{Nu_{rs}\cdot\Gamma_{i}}{\sqrt{A_{f}\cdot m_{l}/m_{f}}}\rho_{g}\ln( 1+B_{m}) \tag{13}\]  
\[\frac{d\mathbf{u_{l}}}{dt}=\frac{\tau_{g}\dot{\mathbf{i}}}{\rho_{l}\delta_{f}} +\frac{2\mu_{f}\mathbf{u_{w}}}{\rho_{l}\delta_{f}^{2}}+(\mathbf{g}-\mathbf{\alpha _{w}})-\frac{2\mu_{f}}{\rho_{l}\delta_{f}^{2}}\mathbf{u_{l}} \tag{14}\]  
\[\frac{d}{dt}(m_{l}c_{pJ}T_{l})=\frac{k_{f}(A_{f}\cdot m_{l}/m_{f})}{\delta_{f} /2}(T_{w}-T_{l})+(A_{f}\cdot m_{l}/m_{f})K_{f}(T_{g}-T_{s})-\frac{dm_{l}}{dt}L \tag{15}\]  
with vapor mass flux \(\dot{N}\); Nusselt number calculated from the Reynolds number and the Schmidt number \(Nu_{rs}\); binary diffusivity of \(i\)th species \(\Gamma_{i}\); wall film face area \(A_{f}\); wall film mass \(m_{f}\); shear stress of the gas flow on the wall film surface \(\tau_{g}\); unit vector in the direction of the wall film surface velocity \(\mathbf{i}\); film thickness at the parcel location \(\delta_{f}\); liquid film viscosity \(\mu_{i}\); wall velocity \(\mathbf{u_{w}}\); thermal conductivity of the wall film \(k_{f}\); wall temperature \(T_{w}\); wall film surface temperature \(T_{s}\).
## 2 Materials and Methods
### Biofilm Formation Assay  
_E. coli_ isolates, after being cultured overnight in Luria Bertani broth (LB, Merck KGaA, Darmstadt, Germany), were diluted 1:100 in M9 minimal medium enriched with 0.2% glycerol and a mineral mixture (1.16 mM MgSO\({}_{4}\), 2 uM FeCl\({}_{3}\), 8 uM CaCl\({}_{2}\), and 16 uM MnCl\({}_{2}\)). These cultures were incubated again overnight at 37 \({}^{\circ}\)C. Subsequently, they were further diluted 1:100 in the same supplemented M9 medium, and 150 uL samples were dispensed in triplicate into a sterile 96-well microtiter plate (Costar\({}^{\text{\textregistered}}\) 3370, Corning, NY, USA). _E. coli_ ATCC 25922 served as a positive control for each experiment, while the M9 medium without any bacteria functioned as the negative control. After a 24 h incubation period at 30 \({}^{\circ}\)C, the wells were thoroughly washed three times with distilled water, dried at 37 \({}^{\circ}\)C for 30 min, and then stained with 0.1% (\(w/v\)) crystal violet for 2 min. Following another set of three washes and drying, the crystal violet was dissolved in 150 uL of an 80:20 (\(v/v\)) ethanol/acetone solution. Absorbance was measured at 595 nm and the biofilm production was categorized as none, weak, moderate, or strong based on previously published criteria. The optical density of the negative control (ODc) served as a baseline. Isolates were classified as non-biofilm producers if their optical density was less than or equal to the ODc (-), weak biofilm producers if their optical density was above the ODc but less than or equal to twice the ODc (+), moderate producers if their optical density was more than twice but less than or equal to four times the ODc (++), and strong biofilm producers if their optical density exceeded four times the ODc (++). This procedure was repeated three times for accuracy [49; 50].
## 2 Background and Related Work
### Feature-Based Methods
Using time-series data of these features and the Sequential Input Selection Algorithm (SISAL), they ranked features according to their importance with respect to their relevance in the blood glucose level prediction. One approach for feature selection consists of clustering that has been mostly used for dimensionality reduction in text classification. But hierarchical agglomerative clustering that organizes features into progressively larger groups [45] have been used in structural classification. [PERSON] and [PERSON] [46] used hierarchical clustering for improving the accuracy of classification on 40 datasets from the UCI Irvine[47]. Their experimental results show that the hierarchical clustering method of feature selection outperforms the ranking methods in terms of accuracy. On the diabetes data, they achieved an accuracy of 77.47% using the Naive Bayes' classifier and 75.26% using the J48 based classifier. There are two limitations of their work. First, the accuracy is not as good as reported by other approaches on the same dataset. Second, the performance reported is only the accuracy of classification, but it is worse in other evaluations. Compared to their approach, we report a much higher performance in accuracy, precision, and recall scores. Considering the above analysis, we conclude that most of the existing approaches (1) use features which are not generalizable, (2) use a large number of features that cannot be obtained in many real-world scenarios, (3) develop specific models that may not be generalizable, and (4) report only a specific metric for evaluation while ignoring other metrics that may have worse performance as an issue of trade-off between the various evaluation metrics. We approach the problem of diabetes prediction while considering these limitations. In our approach, we use a minimum number of features, reducing them further by feature elimination. We apply five different classification models to avoid model-specific performance. We report that all the models performed equally well on the metrics of accuracy, precision, recall, and F1-score, implying that our data or features are not bound to specific models. Finally, our analysis also includes the identification of those factors that can have an indirect impact on the complications of diabetes.
# A Traffic Equilibrium Model for Multi-Modal Networks with Uncertain Demands
## 3 Generalized Travel Cost Function  
In this paper, the equation for the generalized travel cost for physical, transfer, network access, and network departure links is derived. The generalized travel cost function is regarded as a weighted sum of travel time, crowd discomfort, and travel fare:  
\[c_{l}=\lambda_{t}t_{l}+\lambda_{u}u_{l}+\lambda_{m}m_{l} \tag{1}\]  
where \(t_{l}\) (min) is the travel time of link \(l\); \(u_{l}\) (min) is the crowd discomfort of link \(l\); \(m_{l}\) (min) is the fare of link \(l\); \(\lambda_{t}\), \(\lambda_{u}\), and \(\lambda_{m}\), respectively, are the weights of the travel time, crowd discomfort and fare, dimensionless unit, with values of 0.5, 0.3, and 0.2.  
Under the assumption that link travel costs are mutually independent, the travel cost for each route can be calculated analytically as follows:  
\[c_{w}^{p}=\sum_{l}c_{l}\sigma_{w}^{lp},l\in L \tag{2}\]  
where \(\sigma_{lp}\) is incidence relationship between link and route. If the link is on the route, \(\sigma_{lp}=1\), and otherwise it is 0.
## 3 Present
### Radiomics and Personalized Medicine  
Radiomics is a discipline of medicine that is based on the massive extraction of quantitative data from medical images [38]. The fundamental idea is that images are more than pictures and that there is an enormous amount of information that is not visible to the eyes of radiologists which can reveal underlying pathophysiological processes. These quantitative data obtained from medical images will vary depending on whether there is an alteration, thus being able to obtain a \"radiomic phenotype\" of a certain underlying alteration.  
The term radiomics was coined in 2012, and in its early days, its development focused on the field of oncology [38]. It is possible to use it in any disease whose study uses imaging, and in many different imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI) or positron emission tomography (PET). It was designed to be another tool to be used in daily clinical practice at different levels of the healthcare system. Thanks to the information obtained through radiomics, together with the relevant clinical data of a patient, it would be possible to assist in the detection of tumors, the diagnosis of diseases, as well as to provide information on prognosis, response to treatments, or evaluation of the state of a disease.  
The ending \"-omics\" derives from the field of molecular biology, where it is used for the detailed study of biomolecules (genomics, proteomics, etc.). This termination applies to other fields where a large amount of data is handled, as in the case of the quantitative data we obtain from radiological images (radiomics) [38]. The analysis of these data is what allows us to generate hypotheses. In this way, radiological information offers new advantages in the study of patients' diseases but also presents numerous limitations such as the handling of such large amounts of data.  
As mentioned above, this technology may have a key role in the treatment of tumor pathology. One of the great problems in treating cancer is the great heterogeneity that malignant lesions have at the genetic, physiological, and phenotypic levels. This heterogeneity can develop both intratumorally and between metastases of the same cancer, and can even develop over time, so an initially effective treatment can cease to be so in a matter of months due to these alterations in pharmacological targets. In the current context of medicine, with its evolution increasingly towards totally personalized medicine, knowing the mechanisms of tumor resistance to therapy is mandatory to choose a focused and effective treatment [38]. This task requires an early detection method that is reproducible and minimally invasive, in addition to the development of new drugs, to counteract these drawbacks. Radiomics could offer a solution to this challenge, allowing the detection of relevant changes at the genomic level in the tumor (radiogenomics), provided that these are expressed as changes at the radiological level [39].  
Radiomics definition workflow is independent of the disease under study and consists of five consecutive steps (Figure 3):
## 2 Development of the System for Point-Data Pre-Processing
### Module 2--Extraction of Point Data in Cross-Sections  
Bearing in mind that SyPreF is dedicated to RE modelling based on cross-sectional methodology, prior to pre-processing (filtering, smoothing, and reduction) it is necessary to select and sort the points from the 3D point cloud--by cross-sections. Accordingly, within this module (points belonging to particular 2D cross-sectional curves are selected into a separate set Figure 3(a)), and then sorted within each of the scanned curves. The selection can be performed in all three directions--_i.e._, along \(x\), \(y\), and \(z\) axes.  
However, if the input point cloud is unsorted--_i.e._, point data is not sorted into sections or scanned lines--the system provides a sorting option. Point data is sorted within cross-sections parallel to the selected reference plane, by projecting all the points that pertain to defined tolerance zone. The number of cross-sections is directly related to the defined resolution (\(\eta\)), while the tolerance zone represents the band whose width is defined by the maximum allowed shortest distance (\(d_{max}\)) of the points from both sides of the reference planes (Figure 3(b)). It is obvious that a wider tolerance zone implies lower accuracy of the sorting.  
This module also provides an option to change the resolution of the cross-section, _i.e._, the scanned lines. This option can be very useful in case of very densely scanned lines, when the geometric complexity of the scanned object does not require large cross-sectional resolution.  
Figure 3: **(a)** Algorithm of module for extraction of cross-sectional curves. **(b)** Principle of the integrated approach for cross-sectional point data extraction from unsorted point clouds.
###### Abstract  
As a critical task in underground coal mining, personnel identification and positioning in fully mechanized mining faces are essential for safety. Yet, complex environmental factors--such as narrow tunnels, heavy dust, and uneven lighting--pose significant challenges to accurate detection. In this paper, we propose a personnel detection network, MSS-YOLO, for fully mechanized mining faces based on YOLOv8. By designing a Multi-Scale Edge Enhancement (MSEE) module and fusing it with the C2f module, the performance of the network for personnel feature extraction under high-dust or long-distance conditions is effectively enhanced. Meanwhile, by designing a Spatial Pyramid Shared Conv (SPSC) module, the redundancy of the model is reduced, which effectively compensates for the problem of the max pooling being prone to losing the characteristics of the personnel at long distances. Finally, the lightweight Shared Convolutional Detection Head (SCDH) ensures real-time detection under limited computational resources. The experimental results show that compared to Faster-RCNN, SSD, YOLOv586, YOLOv7-tiny, YOLOv8n, and YOLOv11n, MSS-YOLO achieves AP50 improvements of 4.464%, 10.484%, 3.751%, 4.433%, 3.655%, and 2.188%, respectively, while reducing the inference time by 50.4 ms, 11.9 ms, 3.7 ms, 2.0 ms, 1.2 ms, and 2.3 ms. In addition, MSS-YOLO is combined with the SGBM binocular stereo vision matching algorithm to provide a personnel 3D spatial position solution by using disparity results. The personnel location results show that in the measurement range of 10 m, the position errors in the x-, y-, and z-directions are within 0.170 m, 0.160 m, and 0.200 m, respectively, which proves that MSS-YOLO is able to accurately detect underground personnel in real time and can meet the underground personnel detection and localization requirements. The current limitations lie in the reliance on a calibrated binocular camera and the performance degradation beyond 15 m. Future work will focus on multi-sensor fusion and adaptive distance scaling to enhance practical deployment.  
YOLO; personnel detection; location; stereo vision; underground coal mine licenses/by/4.0/. +
Footnote †: journal: Applied sciences