esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Local Observability Conditions for the Extended Battery ECM
### Observability Analysis for the Extended Sub-Models
Therefore, the local observability condition for extended model (4) is defined by (8), where several interesting concluding remarks can be drawn. \[\det(J)=\left(\frac{\mathrm{d}V_{\mathrm{OC}}}{\mathrm{d}soc}\right)^{2}.( \frac{\eta I_{\mathrm{batt}}(t)}{\mathrm{C}_{n}^{2}(t)})\
eq 0 \tag{8}\]  
1. The term \(\left(\frac{\mathrm{d}V_{\mathrm{OC}}}{\mathrm{d}soc}\right)^{2}\) induces the inherent weaker observability environment for the battery capacity \(\mathrm{C}_{n}\). Because \(\frac{\mathrm{d}V_{\mathrm{OC}}}{\mathrm{d}soc}\) is usually smaller than one, this means its square is always smaller than itself. 2. The input \(I_{\mathrm{batt}}\) appears in the numerator of \(\det(\mathbf{J})\), which means the current value will also affect the observability condition. It has been indicated in [32] that a higher absolute value of the determinant will lead to a better observability. Hence, the larger the current is, the better is the observability condition. However, the selected current level should be adequate, namely it should make a compromise between the convergence speed and the battery high-rate charging current. 3. As \(\mathrm{C}_{n}^{2}\) is in the denominator of \(\det(\mathbf{J})\), then its impact cannot be ignored. A very practical problem in reality is how to choose the initial value for the estimation algorithm, namely an initial value that is larger than the reference one or smaller than the reference one, and the reason why we make that decision. With our demonstration, an initial capacity value that is smaller than the reference value is recommended when the battery capacity is estimated under the extended model framework, because this will enhance the observability condition. To summarize, a schematic resume of the obtained necessary observability conditions for the extended battery ECM is shown in Figure 6.  
Note that these observability conditions are necessary, but not sufficient for battery monitoring, because the observability of the model (3) will definitely be weaker than the observability of each sub-model. However, the proposed indirect observability analysis method can avoid applying the nonlinear observability method directly to the extended battery model, which would be tricky.
## 5 Conclusions  
To facilitate the 3D reconstruction of Warsaw's pre-war architecture, a detailed methodology was devised. This approach combines various sources, including cartographic documents, spatial data, and tachymetric survey results. Key resources such as historic maps, ground-based and aerial photographs, and architectural plans obtained from the National Archives in Warsaw informed the development of three distinct methods for 3D modeling. Among the buildings chosen for reconstruction were significant structures such as the Grand Synagogue, the Kamienica Theater, and the Ttomackie buildings, all of which were either destroyed or heavily damaged during World War II.  
The process of 3D modeling required precise handling of historical imagery, carefully aligning and calibrating photographs, and incorporating data from tachymetric surveys to ensure measurement accuracy. This methodology illustrates that it is possible to digitally reconstruct buildings lost to history by using a combination of archival cartographic information and spatial data sets from multiple origins. By integrating historical maps and spatial data with modern digital technologies, this work offers a novel approach to experiencing and interpreting Warsaw's past, providing a richer understanding for both the city's residents and specialists, including historians, archivists, and cartographers.  
The proposed method for developing 3D models of destroyed architectural objects has universal application value. Not only is this method applicable to buildings that existed before World War II in Warsaw; it can also be applied to any damaged or destroyedstructures, e.g., buildings damaged during armed conflicts, such as those currently in Ukraine and other parts of the world.  
Conceptualization, B.C. and J.S.; methodology, B.C. and J.S.; software, J.S.; validation, B.C. and P.J.; formal analysis, J.S.; investigation, B.C. and P.J.; data curation, J.S.; writing--original draft preparation, B.C. and P.J.; writing--review and editing, B.C. and P.J.; visualization, J.S.; supervision, B.C. and P.J. All authors have read and agreed to the published version of the manuscript.  
This research and publication process was funded within the statutory project No. UGB/22-705/2024, titled \"Acquisition and processing of spatial data for the needs of geospatial reconnaissance\", conducted in 2024 at the Faculty of Civil Engineering and Geodesy, Military University of Technology, Warsaw, Poland.  
The data presented in this study are available on request from the corresponding author due to the privacy restrictions.  
The authors declare no conflicts of interest.
## 4 Discussion
These advances are particularly relevant in pediatric endocrinology, where bone age guides treatment decisions, and in forensic medicine, especially for age estimation in unaccompanied minors. Understanding the acceptable range of error, typically <0.8 years, is essential for contextualizing the practical applicability of automated systems, although slightly higher MAEs may still be acceptable depending on the clinical scenario [104,105]. Despite these advances, BA assessment remains subject to population-specific influences (e.g., _genetic, nutritional, environmental, cultural_, etc.) [106,107], for example, _BoneXpert(r)_ exhibited systematic bias in Taiwanese cohorts until local recalibration [30], and the traditional GP and TW3 standards--derived from North American Caucasian and British samples, respectively--yielded errors up to 11.8 months in girls of African descent [14]. To mitigate such biases, AI models must be trained and validated on demographically representative, balanced datasets, explicitly incorporating variables such as biological sex, ethnic background, and socioeconomic status [108]. Practical strategies include dataset diversification (e.g., _oversampling under-represented groups_, _subsampling over-represented ones_) [109] and the deployment of fairness-aware algorithms (e.g., _reweighting schemes_, _adversarial debiasing_, _constrained-optimization methods_) [110]. Several studies illustrate this approach. For example, [PERSON] et al., (2022) [55] trained a DL model on over 12,600 PA-HW X-rays plus clinical covariates such as CA and sex, achieving an MAE of 0.67 years (\(\approx\)8.04 mos.) and robust performance in adolescents [11; 13]. [PERSON] et al., (2024) combined EfficientNetV2S features with age and sex data to reach MAE of 0.41 years (\(\approx\)4.92 mos.) and 91.1% accuracy overall and MAE of 0.267 years (\(\approx\)3.2 mos.) and 95.0% in girls under the age of 11 years [74]. Similarly, [PERSON] et al., (2023) tailored a model to Korean children, reducing MAE from 0.875 to 0.683 years (\(\approx\)10.5 to 8.2 mos.) versus _VlNO Med(r)-BoneAge_[67]. Along the same lines, [PERSON] et al., (2020) used a two-stage method--Faster R-CNN for ossification detection followed by sex- and region-specific regression--to achieve MAE of 0.38 years (\(\approx\)4.56 mos.) on the RSNA test set and produce interpretable outputs for radiologists [31]. Finally, [PERSON] et al., (2022) and [PERSON] et al., (2023) emphasize validating tools like _VlNO Med(r)-BoneAge_ and Gleamer _BoneView(r)_ across varied populations to ensure accuracy, equity, and long-term reliability [45; 93]. Overall, these studies show that combining clinical covariates with optimized DL architectures and validating models on specific populations enables automated BA estimation from left PA-HW radiographs with MAEs below 6 months, yielding more accurate, equitable, and sustainable assessments.
# Study on the Stability and Control of Gob-Side Entry Retaining in Paste Backfill Working Face
## 5 Results of Numerical Simulation
### Deformation Law of Roadway Surrounding Rock with Different Backfill Ratios
This results in larger roof subsidence, more obvious floor heave, and severe deformation of two ribs, which seriously impacts the stability of the roadway. As the backfill ratio increases to 75%, the support provided by the backfill material to the roof gradually increases, reducing the rotation space for the roof in gob and resulting in a decreasing trend in the deformation of surrounding rock. However, roof subsidence remains relatively severe, floor heave is still apparent, and rib deformation is also noticeable, indicating that the stability of the surrounding rock needs further reinforcement. When backfill ratio is increased from 75% to 85%, the deformation of surrounding rock significantly decreases, and the stability improves. When backfill ratio reaches 95%, the deformation of the CT301 headgate decreases significantly, with minimal roof subsidence and floor heave, and rib deformation is not pronounced, resulting in excellent integrity. Therefore, the backfill ratio plays a key role in the stability of roadway surrounding rock. By increasing the backfill ratio in gob, the uneven deformation of roof can be effectively reduced and the deformation of rib and floor can be minimized.
## 3 Data and Methodologies
### Numerical Ice-Flow Modeling  
We applied an analytical forward model implemented in the ice sheet system model to calculate the spatial-temporal distribution of damage and stress [48]. The ice flow was approximated using [PERSON]'s \"shelf stream approximation\" (SSA) technique within a 2-D model. The observed velocity and rheology assumed as constant were used to calculate the stress field of floating ice according to [PERSON]'s flow law, which revealed an approximately proportional relationship between glacier strain rate and deviatoric stress [49]. An important consequence of this analytical solution is that the damage and effective deviatoric stress can be calculated analytically as the stress balance evolves to quantify and diagnose the glacier instability state. The axes of the principal stress was computed by using the surface stress to compare with the orientation of the crevasses on glacier terminate position [50]. We can write the expression for effective stress and damage in the simplified form [48; 50]:  
\[\dot{\delta}_{e}=\sqrt{\left(\dot{\delta}_{xx}^{2}+\dot{\delta}_{xy}^{2}+\dot {\delta}_{yy}^{2}\right)/2} \tag{2}\]  
\[D=1-\left[\frac{\theta}{\varepsilon_{xx}}\right]^{\frac{1}{h}}\left[\frac{1/2 \rho gH}{B}\right] \tag{3}\]  
where \(\dot{\delta}_{e}\)\(\dot{\delta}_{xx}\), \(\dot{\delta}_{xy}\)_and_\(\dot{\delta}_{yy}\) respectively represent effective stress, transverse stress, shear stress and longitudinal stress; \(D\) is damage scalar; \(\dot{\varepsilon}_{xx}\) is transverse strain rate; \(\theta\) is the contribution of lateral and shear strain rate terms; \(\rho\) is the density of ice; \(g\) is gravitational acceleration; \(H\) is the ice thickness; \(B\) is the ice rigidity.  
The model geometry was obtained from the mass conserving bed product in the Bed-Machine v3 Greenland dataset [51]. This dataset includes the ice thickness and ice base derived from the ice surface by employing the floating condition. We updated the recent ice fronts and set up the boundary conditions, in which a stress-free surface and water pressure were respectively applied at the ice-atmosphere and ice-ocean interface. Along the other boundaries of the model domain, Dirichlet conditions were applied to ensure that the ice velocity equaled the observed velocity, and that the direction of the ice velocity was tangential to the boundary. The modeling domain covered the Petermann Glacier basin. The model calculation was performed on an adaptive finite element grid, with a high resolution of 50 m in fast-flowing regions and a coarser resolution of 2000 m in the slow-moving region. For the fracture of floating ice, the region was refined with a mesh resolution of 20 m. In total, the mesh was made up of about 1,757,389 elements.
## 2 The Role of Machine Learning in Cardiovascular Disease Prediction
### Background
The work in [118] presented a cardiovascular disease classification system employing relief and rough set techniques, which achieved a classification accuracy of 92.32%. In another study [103], a cardiovascular disease identification method using feature selection and classification algorithms was proposed. To this end, the Sequential Backward Selection Algorithm (SBS FS) was utilized for feature selection and tested the KNN classifier on both full and selected feature sets, obtaining high accuracy. The work in [80] designed a cardiovascular disease prediction method using hybrid machine learning techniques and introduced a novel feature selection method for effective machine learning classifier training and testing, achieving a classification accuracy of 88.07%. The work in [119] developed cardiovascular disease identification techniques employing an improved SVM-based duality optimization technique. While the aforementioned techniques have employed various methods to detect cardiovascular disease at early stages, they exhibit limitations in terms of prediction accuracy and computational time. In [120], the researchers developed a classifier utilizing a blend of diverse support vector machines (SVMs) to classify ECG signals, focusing on the extraction of features from intervals between consecutive beats. Additionally, they tackled the challenge of highly imbalanced data by employing both over and under-sampling techniques on the Arrhythmia dataset. The work in [121] proposed a classifier that is capable of identifying 17 distinct types of Arrhythmia, which employs a CNN model specifically designed for
###### Abstract  
The cloud point extraction (CPE) method was successfully used for the isolation and pre-concentration of ultra-low concentration of Pb prior to its determination by flame atomic absorption spectrometry (FAAS). Lead(II) reacts with methyl 4,20-disobutyl-2,5,8,16,19,22- hexaoxo-7,17-dipropyl-3,6,9,15,18,21-hexaaza-1(2,6)-pyridinacyclo-docosaphene-10-carboxylate (DLNL) as chelating agent in the presence of octylphenoxypolyethoxyethanol (Triton X-114) as a nonionic surfactant giving a surfactant-rich phase chelate which could be used for CPE. Factors affecting the CPE such as solution pH, concentrations of the chelating ligand and surfactant, temperature of equilibration, and time were optimized. The efficacy features of the proposed protocol such as linear range, lower limit of detection, pre-concentration, and progress factors were evaluated. The method revealed a wide linear range in the range of 7-250 ng/mL of Pb\({}^{2+}\) with a limit of detection of 5 ng/mL using FAAS. Validation of the presented protocol revealed good performance characteristics including high between-batch repeatability, high precision, wide linear range, low limit of detection, and acceptable accuracy. The presented procedure was successfully introduced for the separation and quantification of lead (II) in wastewater samples with acceptable results.  
Keywords:lead; cloud point; pre-concentration; flame atomic absorption spectrometry (FAAS) +
Footnote †: journal: Applied Soft. 2019, 9, 4752; doi:10.3390/appr9224752
## 4 Construction of Cutting Force Prediction Model Based on Response Surface Method
### Construction of Cutting Force Prediction Model  
Based on the experimental design method and response value, combined with the analysis software Design-Expert, the regression analysis was used to analyze the data. When the regression equation was established, some items may have no significant effect on the response. In order to obtain an optimal regression equation, these items should be removed without affecting the accuracy of the equation. The following equations are quadratic regression models for the rotational vibration cutting of the Nomex honeycomb-core composite material with disc-cutter.  
\[\left\{\begin{array}{l}F_{x}=+7.215-0.153a_{p}-0.47a_{e}-1.139\times 10^{-3}v_{ f}+0.086\times 10^{-3}a_{p}v_{f}\\ +0.117\times 10^{-3}a_{e}v_{f}+0.012a_{e}^{2}-0.065\times 10^{-6}v_{f}^{2}\\ F_{y}=-5.03+0.536a_{p}+0.253a_{e}+0.994\times 10^{-3}v_{f}-0.014a_{p}a_{e}\\ -0.049\times 10^{-3}a_{p}v_{f}+0.061\times 10^{-3}a_{e}v_{f}-0.077\times 10^{-6}v_{ f}^{2}\\ F_{z}=+2.576-0.225a_{e}+1.004\times 10^{-3}v_{f}-0.086\times 10^{-3}a_{p}v_{f}+0.0 65\times 10^{-3}a_{e}v_{f}\\ +0.015a_{p}^{2}+0.011a_{e}^{2}\end{array}\right. \tag{4}\]  
where \(a_{p}\) is the cutting depth (mm), \(a_{e}\) is the cutting width (mm) and \(v_{f}\) is the feed rate (mm/min).
## 2 System Model
### Clustering of D2D Pairs  
After traversing all RBs, the received SINR of D2D pairs for each RB and the SINR of CUs received at BS are calculated according to (4) and (5). D2D pairs are clustered if the performance of CUs is not disturbed, and the SINR of D2D pairs meet the particular demand. The number of clusters is assumed to be the same as that of RBs, and the clusters are denoted as \(S_{j}^{C}\) (\(j=1,2,\ldots,M\)), which means that one cluster is an intended user set of one RB. All clusters are denoted as \(S^{C}\) and \(S^{C}=\left\{S_{j}^{C}\right\}\), and each D2D pair can be an intended user of all RBs as the conditions presented above are satisfied.  
**Definition 2**.: _All D2D pairs in a cluster are the intended users of one RB, which means that each D2D pair can reuse this RB and \(\bigcup_{j\in X}S_{j}^{C}\subseteq DP\)._Definition 3: Each D2D pair can be an intended user of multiple RBs. Thus, one D2D pair can be found in different clusters indicated as  
\[S_{j}^{C}\cap S_{k}^{C}\
eq\varnothing\text{ for }\exists j,k\in X\text{ and }j\
eq k \tag{15}\]  
After clustering the D2D pairs, the result we identified is that a row vector is the available RBs of one D2D pair and a column vector is an intended user set of one RB if we apply a matrix to represent the allocation with D2D pairs as ordinate and RBs as abscissa. For each intended user set, the D2D pairs compete for each other for the qualification of reusing RB. To fairly compete among D2D pairs, we have the following definition.  
Definition 4: The weight of D2D pairs in each intended user set that expresses the qualification of sharing RBs is calculated by SINR ratio as  
\[\omega\left(i\left|S_{j}^{C}\right)=\frac{\gamma_{D,i}}{\sum_{j}s_{j}^{C} \gamma_{D,i}} \tag{16}\]  
D2D pairs in each intended user set are weighted by their corresponding SINR accordingly. Nevertheless, one D2D pair in different intended user sets may have different weights due to the interference received from CUs are different. The value of weight satisfies \(\omega\left(i\left|\right.S_{j}^{C}\right)\in\left[0,1\right]\), as we consider that the D2D pair with higher weight in an intended user set has prior opportunity to share RB with CUs.
# Augmented Lagrangian-Based Reinforcement Learning for Network Slicing in IIoT
## 5 Conclusions  
In this paper, we investigated the network slicing problem in IIoT, where the device association and bandwidth allocation for devices in different slices are jointly optimized. By formulating it as a constraint mixed integer nonlinear programming problem with continuous and discrete variables, a Lagrangian-based SAC algorithm is proposed to solve it using DRL. Aiming to maximize the total weighted utility under limited bandwidth resources, cost neural networks and Lagrangian multiplier networks are introduced to update the Lagrangian multipliers and the penalty term is introduced also to the reward function. Moreover, specifically, a novel two-stage actions selection network is presented based on DRL to handle the hybrid actions and decrease the action space simultaneously. Our results verify that the proposed AL-SAC algorithm can effectively meet the constraint and achieve better performance than other benchmark algorithms in terms of average reward and fairness.  
Conceptualization, Q.Q.; methodology, W.L., B.G., and Y.C.; software, J.C.; validation, X.S.; formal analysis, C.D.; investigation, G.L.; writing--original draft preparation, B.G.; writing--review and editing, Q.Q., B.G. and Y.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the technical project of State Grid Fujian (No. 521304210003).  
The authors declare no conflict of interest.  
Figure 9: Network service constraints for devices in different network slices with various weights combination. (**a**) The achieved transmission rate of devices in eMBB network slice; (**b**) the achieved delay of devices in URLLC network slice; (**c**) the achieved bandwidth of devices in mMTC network slice.
## 2 Analytical Modeling  
This study presents an analytical strategy to predict the temperature profiles and estimate the molten pool dimensions in the powder bed fusion process, with the consideration of process conditions-dependent absorptivity. An empirical model was employed to correlate the process conditions with the laser absorptivity of powder materials. A closed-form heat source model was used to compute the temperature distribution, based on the information on material properties, process conditions, and absorptivity. The molten pool geometries were obtained by comparing the melting temperature of the material with the calculated temperature profiles.  
The empirical model for the calculation of absorptivity was proposed by [PERSON] et al. in [20], which was obtained by scaling law and curve fitting of plenty of experimental measurements of absorptivity of various materials, including Ti6 Al4V, 316L stainless steel, and Inconel 625. This model captures the change of laser absorptivity in the transition-mode regime and keyhole melting regime [20]. It can be expressed as:  
\[A_{e}=0.70\left(1-e^{-0.66\beta_{Am}t_{th}^{*}}\right) \tag{1}\]  
where, \(A_{e}\) represents the effective absorptivity, which will be used as the input absorptivity for the thermal model. \(\beta_{Am}=\frac{A_{m}P}{\pi\sigma cT_{m}\sqrt{Du^{3}}}\) represents the normalized enthalpy when the absorptivity is the minimum of absorptivity \((A_{m})\) measured in the transition regime. In this equation, \(P\) is the laser power, \(u\) is the scanning speed, \(a\) denotes the laser beam radius,\(\rho\), \(c\)\(T_{m}\) are the material properties, density, specific heat capacity and melting temperature, respectively. \(L_{th}^{*}=\left(\sqrt{Da/u}\right)/a\) represents the normalized thermal diffusion length [20].  
The analytical thermal model to calculate the temperature distribution is a point moving heat source model, which was derived by [PERSON] et al. in [41] based on the assumptions of semi-infinite medium, thermal conduction, and temperature-independent material properties. It should be noted that there will be heat loss at part boundaries in LPBF through thermal conduction, convection, and radiation [42]. In this thermal model, the boundary heat loss is not considered. The thermal model can be expressed as:  
\[\theta_{laser}(x,y,z)=\frac{PA}{2\pi kR(T_{m}-T_{0})}exp\bigg{(}\frac{-u(R+x) }{2\kappa}\bigg{)} \tag{2}\]  
In this equation, \(\theta_{laser}=\frac{T-T_{0}}{T_{m}-T_{0}}\) is the dimensionless temperature, \(T_{0}\) and \(T_{m}\) denote the melting temperature and initial temperature (room temperature or preheating temperature), respectively. \(P\) and \(u\) represent the laser power and moving speed. \(A\) represents the laser absorptivity of the material. \(\kappa=\frac{k}{\rho c}\) is the thermal diffusivity obtained from thermal conductivity \(k\), density \(\rho\) and heat capacity \(c\). \(R=\sqrt{x^{2}+y^{2}+z^{2}}\) is the distance of the calculated point from the heat source, \((x,y,z)\) represent the coordinate in the calculation domain.