esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Model Design Concept and Construction  
Patient digital bilateral femur solid models were constructed using data from computed tomography (CT) scans. All CT cross-section images were processed to identify the contours of various hard tissues (cortical and cancellous bone). The bone contours were extracted and converted into 3D solid model reconstructions of the intact left and fractured right femur using a CAD system (Creo Parametric v5.0, PTC, Needham, MA, USA). The left femoral solid model sites 1 cm proximal and distal to the corresponding right femoral fracture region were mirrored to the right femur to perform the Boolean operation to restore the original size, shape and contours of the affected femur.  
The distal right femur fracture and defect site of the distal right femur can be identified to perform the 3D printing titanium implant design. The implant's 3-dimensional profile was configured to closely match the fracture site (Figure 2a). A central cylindrical tube was designed in the center of the implant to help provide mechanical stability. Proximal and distal shells 1.5 mm in thickness were designed on the implant to provide a contact structure to induce bone ingrowth. A protruding stem was considered to fit into the femoral canal at the proximal bone-implant junction with a transverse implant screw fixed with the proximal host bone. An additional oblique implant screw was also used to fix the implant at the distal host bone. A lattice mesh structure (10 mm \(\times\) 10 mm) 1.5 mm in thickness was projected onto the implant surface to provide space for morselized cancellous bone grafting to enhance the implant's structural strength. An outer lateral-side pre-contoured locking plate was applied at the lateral site with a 0.5 mm gap between the bone and plate to secure the entire construct (Figure 2b).  
Figure 1: A 17 y/o patient with right distal femur fracture with \(>\)15 cm open wound. The X-ray showed severe communication of the fracture with large segmental bone defect.
## 3 Results and Discussion
### Modeling and Prediction by BP-ANN
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Runs** & **A** & **B** (\({}^{\circ}\)**C) & **C (min)** & **D (mg/L)** & **Experimental Value (\%)** & **Predicted Value (\%)** & **Absolute Error (\%)** \\ \hline
23 & 4 & 30 & 90 & 700 & 72.17 & 72.26 & 0.09 \\
24 & 6 & 25 & 90 & 600 & 78.51 & 78.38 & 0.13 \\
25\({}^{*}\) & 5 & 25 & 90 & 700 & 63.12 & 63.07 & 0.05 \\
26\({}^{*}\) & 5 & 25 & 60 & 600 & 55.99 & 45.78 & 10.21 \\
27\({}^{*}\) & 6 & 30 & 90 & 700 & 80.83 & 81.76 & 0.93 \\
28\({}^{*}\) & 5 & 30 & 120 & 700 & 81.68 & 79.82 & 1.86 \\
29\({}^{*}\) & 5 & 25 & 90 & 700 & 63.12 & 63.07 & 0.05 \\  & & & & & & & 0.54 \\ \hline \hline \end{tabular} \({}^{*}\) representation test set.  
\end{table} 
Table 3: _Cont_. Figure 12: Regression plot of the actual and predicted results. Figure 13: Mean square error (MSE) of neurons in the BP-ANN model.
## 2 Materials and Methods
### Chromatin Immunoprecipitation  
Cells were plated in 10 cm dishes at a density of 2 \(\times\) 10\({}^{5}\) cells per mL. For studies using MCF-7 cells, cells were exposed to test ligands 48 h after serum starvation. For assays with overexpressed ER\(\alpha\) and PARP7, HuH-7 cells were transfected with a total of 2.5 \(\upmu\)g DNA consisting of 300 ng of pSGS-ER\(\alpha\) and either 2.2 \(\upmu\)g of pEGFP-PARP7, pEGFP-PARP7H532A or 7.5 ng of pEGFP and 2.2 \(\upmu\)g of p cDNA3.1 using Lipofectamine 3000 (Thermo Fisher Scientific, Waltham, MA, USA). Cells were treated with DMSO or E2 for one hour, and formaldehydehyde was added to a final concentration of 1% and cells were left on a shaker for 10 min. Glycine was added to a final concentration of 0.125 M, and plates were left on the shaker for 5 min. Preparation of the cell extract and ChIP assay was performed essentially as we have previously described using a negative control (no antibody; MCF-7 only) or rabbit IgG (Sigma-Aldrich; HuH-7 only), 3 \(\upmu\)g of anti-GFP (Thermo Fisher Scientific; 3E6) or 3 \(\upmu\)g of anti-ER\(\alpha\) (Santa Cruz Biotechnology, Dallas, TX, USA; HC-20) per immunoprecipitation [28]. One \(\upmu\)L from each sample and the input samples were analyzed by RT-qPCR. The primers used were _PARP7_: forward 5'-TTTGCTCTCCACAGGGTGT-3' and reverse 5'-AGGGTACTTTTGTCCGAGA-3', _GREB1_: forward 5'-CCAGGCTGGCAGCT-3' and reverse 5'-CAAAGGGTCAGAGAACACA-3', and _TFF1_: forward 5'-CCGGCCATCTCACTATGAA-3' and reverse 5'-CCTCCGCCAGGGAATAC-3'.
# On-Orbit Polarization Calibration for Multichannel Polarimetric Camera
###### Abstract  
In this paper, a new on-orbit polarization calibration method for the multichannel polarimetric camera is presented. A polarization calibration model for the polarimetric camera is proposed by taking analysis of the polarization radiation transmission process. In order to get the polarization parameters in the calibration model, an on-orbit measurement scheme is reported, which uses a solar diffuser and a built-in rotatable linear analyzer. The advantages of this scheme are sharing the same calibration assembly with the radiometric calibration and acquiring sufficient polarization accuracy. The influence of the diffuser for the measurement is analyzed. By using a verification experiment, the proposed method can achieve on-orbit polarization calibration. The experimental results show that the relative deviation for the measured degree of linear polarization is 0.8% at 670 nm, which provides a foundation for the accurate application of polarimetric imaging detection.  
polarimetric camera; polarization calibration; polarization parameters; remote sensing and sensors +
Footnote †: journal: Applied
# Variability of Interpolation Errors and Mutual Enhancement of Different Interpolation Methods
Their findings showed that cokriging interpolation, which considered elevation, yielded high accuracy. [PERSON] et al. [16] examined the spatial variability of average rainfall in Texas, USA, utilizing spatial statistics, autocorrelation, and semivariogram functions. Their study revealed a direct correlation between the number of meteorological observation stations and spatial interpolation errors. From the perspective of remote sensing, the interpolation of remote sensing data can provide more comprehensive and accurate information, enhancing our understanding of various Earth system components. [PERSON] et al. [17] compared quaternion interpolation with existing methods for remote sensing images by applying a quaternion interpolation algorithm to fused resampled images. These studies have improved interpolation accuracy within specific ranges. To expand the scope of interpolation methods, it is essential to move beyond traditional frameworks and adopt a more integrative approach. By combining results from different interpolation methods, the accuracy of the interpolated data can be enhanced [18]. Fusion can be achieved through weighted averaging, which maximizes the strengths of various methods, compensates for their weaknesses, and improves overall performance. Different interpolation methods are more suitable for specific spatial or temporal contexts, and through fusion, the characteristics of multiple methods can be effectively harnessed to adapt to regional or data density variations [19; 20; 21]. Fusion methods are increasingly employed across various fields to enhance the accuracy of predictions. In machine learning, for instance, diverse models--such as decision trees, support vector machines, and neural networks--can be combined through weighted fusion to improve overall prediction performance [22; 23; 24]. In image recognition and computer vision, the fusion of outputs from different feature extraction methods, classifiers, or neural networks enhances the accuracy and robustness of tasks, like object and face recognition [25; 26; 27; 28; 29; 30]. Similarly, in finance, the fusion of multiple algorithms or models improves stock price predictions and market trend analysis, thereby increasing both prediction accuracy and risk control capabilities [31; 32; 33; 34]. In medical diagnostics, weighted fusion of various data analysis models can enhance the accuracy of disease diagnosis and precision in patient risk assessments [35; 36; 37]. Thus, applying fusion to improve interpolation accuracy offers a novel approach.
## 1 Introduction
The above-mentioned works were confined to the European region due to the use of the IRI UP model for the F2-layer peak anchor point and were based on the assumption of constant \(H\) in the topside [7] and on the topside \(H\) representation used by the NeQuick model [18]. In the present work, we relaxed these constraints in the calculation of the topside effective scale height. The use of IRI to model the F2-layer peak anchor point allows for the global modeling of \(H\) with CSES--01 and Swarm B in situ \(N_{\rm e}\) observations. Later in this paper, we include a linear variation in the topside \(H\) description based on evidence from several works [6; 20; 21; 22; 23; 24; 25; 26]. To include the linear variation in the \(H\) description, we take advantage of COSMIC/FORMOSAT--3 (also known as COSMIC--1 [27]) RO measurements. COSMIC--1 RO \(N_{\rm e}\) topside profiles are used to infer the median behavior of the effective scale height vertical gradient in the topside through the methodology developed by [6; 25]. The linear \(H\) description can be then applied to \(N_{\rm e}\) datasets provided by in situ LEO satellites, along with IRI-modeled F2-layer peak parameters, to derive values of \(H_{0}\). The remainder of this paper is organized as follows: Section 2 includes a description of CSES--01 and Swarm B in situ \(N_{\rm e}\) datasets and the calibration applied to them; the same section describes COSMIC--1 RO \(N_{\rm e}\) data and IRI-modeled data. On one hand, Section 3 describes in detail the topside effective scale height modeling methodology based on the \(H\) linear approximation in the NeQuick topside model and, on the other hand, how \(H_{0}\) values are obtained. Section 4 presents the global behavior of \(H_{0}\) for daytime and nighttime conditions, different seasons, and for the low solar activity years covered by our dataset. Results from CSES--01 observations are compared with those derived from Swarm B observations and validated against those derived from COSMIC--1 RO. Also for comparison, \(H_{0}\) values obtained with non-calibrated CSES--01 and Swarm B \(N_{\rm e}\) observations are calculated and discussed. In Section 4, results obtained through the use of the original NeQuick topside formulation are also presented. Moreover, a comparison between COSMIC--1-measured and IRI-modeled F2-layer peak parameters is provided. Section 5 discusses the results of Section 4, while Section 6 outlines the conclusions that can be drawn from the present study.
# Effects of Post-Activation Performance Enhancement on Jump Performance in Elite Volleyball Players
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & & & & & & \\ \hline \hline \multirow{2}{*}{**Variable**} & \multirow{2}{*}{**Condition**} & \multirow{2}{*}{**Baseline**} & \multirow{2}{*}{**Post**} & \multirow{2}{*}{**Effect Condition F(p)**} & \multirow{2}{*}{**Effect Time F(p)**} & **p: Post Hoc** \\  & & & & & & & & \\ \hline \multirow{2}{*}{JH (cm)} & CNTR & \(44.5\pm 6\) & \(44.6\pm 7\) & \multirow{2}{*}{0.007 (0.93)} & \multirow{2}{*}{0.86 (0.37)} & \multirow{2}{*}{1.20 (0.29)} & NS \\ \cline{2-3} \cline{5-8}  & PAPE & \(45.3\pm 4\) & \(44.3\pm 4.1\) & & & & NS \\ \hline \multirow{2}{*}{FT (s)} & CNTR & \(0.601\pm 0.041\) & \(0.601\pm 0.047\) & \multirow{2}{*}{0.01 (0.90)} & \multirow{2}{*}{1.06 (0.32)} & \multirow{2}{*}{1.17 (0.30)} & NS \\ \cline{2-3} \cline{5-8}  & PAPE & \(0.608\pm 0.027\) & \(0.601\pm 0.028\) & & & NS \\ \hline \multirow{2}{*}{E (J)} & CNTR & \(394\pm 55.6\) & \(393.6\pm 55.6\) & \multirow{2}{*}{0.02 (0.88)} & \multirow{2}{*}{1.06 (0.32)} & \multirow{2}{*}{0.90 (0.36)} & NS \\ \cline{2-3} \cline{5-8}  & PAPE & \(403.5\pm 59.1\) & \(394.7\pm 62.1\) & & & NS \\ \hline \multirow{2}{*}{AP (W)} & CNTR & \(2072.7\pm 303.6\) & \(2076.3\pm 297.2\) & \multirow{2}{*}{0.004 (0.94)} & \multirow{2}{*}{0.86 (0.37)} & \multirow{2}{*}{1.20 (0.29)} & NS \\ \cline{2-3} \cline{5-8}  & PAPE & \(2109.9\pm 317.3\) & \(2065.4\pm 332.6\) & & & NS \\ \hline RAP (W/kg) & CNTR & \(22.9\pm 2.8\) & \(23\pm 3.2\) & \multirow{2}{*}{0.0001 (0.99)} & \multirow{2}{*}{0.71 (0.41)} & \multirow{2}{*}{1.58 (0.23)} & NS \\ \cline{2-3} \cline{5-8}  & PAPE & \(23.2\pm 1.7\) & \(22.7\pm 1.8\) & & & NS \\ \hline \multirow{2}{*}{PP (W)} & CNTR & \(5981.9\pm 599.5\) & \(5988.4\pm 576.2\) & \multirow{2}{*}{0.03 (0.95)} & \multirow{2}{*}{0.86 (0.37)} & \multirow{2}{*}{1.20 (0.29)} & NS \\ \cline{2-3} \cline{5-8}  & PAPE & \(6048.7\pm 637.2\) & \(5968.8\pm 665.3\) & & & NS \\ \hline RPP (W/kg) & CNTR & \(66.2\pm 5.7\) & \(66.4\pm 6.6\) & \multirow{2}{*}{0.00 (0.99)} & \multirow{2}{*}{0.71 (0.41)} & \multirow{2}{*}{1.58 (0.23)} & NS \\ \cline{2-3} \cline{5-8}  & PAPE & \(66.8\pm 3.3\) & \(65.9\pm 3.2\) & & & NS \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of jumping tests for non-responders (six participants in CMJ, three in SJ) at the baseline and after CA (presented as mean \(\pm\) SD).
# Future Sustainable Internet Energy-Defined Networking
## 4 Future Internet Energy-Defined Networking--Design Methods
### Methods for Network Energy Management and Associated Research Challenges
#### 4.4.1 Service-Level Energy Design Methods  
Service operational support functions (~10% of energy consumption); the methods' main goal: accurate service energy consumption models.  
The specific critical methods include the following:  
1. Building energy service consumption models which will assist in predicting and optimizing consumption for various service offerings.
2. Building a service energy-aware digital twin that will consider the relationship of a wide range of operational parameters, such as packet rates, queue lengths, CPU/storage utilization, and system load with energy consumption and running services.
3. User demand, its service characteristics, and the decisions on how these aspects directly impact the energy consumption of the service infrastructure and operations should be accommodated explicitly in the service intents.
4. These digital twin models will subsequently govern the network and service management decisions and drive greener configurations for the infrastructure and services.
5. The fast evolution of underlying instrumentation and tools for energy-defined Development Operations (DevOps).
# A Sustainability Analysis Based on the LCA-Energy-Carbon Emission Approach in the Building System
## 5 A New Type of Energy System Reuse Analysis  
According to the research in this paper, the building operation stage is the main influencing factor, no matter whether this is from the energy perspective or the carbon emission perspective. Among them, the building operation stage is mainly composed of the thermal subsystem and the electrical subsystem. Therefore, the strategy improvement in this paper will focus on the thermal subsystem and the electrical subsystem.  
To verify the influence of heat and electric energy on the whole building system, a new power and heat supply subsystem has been designed and is displayed in Figure 19. The most obvious highlight of the system is that the energy comes from the waste heat recycling of the glass manufacturing system, which belongs to the reuse of surplus energy. It provides new energy supplies while reducing waste.  
In Figure 19, a glass kiln is the energy source used to provide heat energy through a range of mechanical devices to convert it into electricity, including a heat exchanger, heat-conducting oil, pump, preheater, evaporator, super-heater, expansion tank, exhaust stream heater, condenser, pump, generator, etc. Finally, electricity is produced by the new power generation subsystem, some of which goes to the grid, and some of which goes to the cogeneration machine, where it is used to generate heat for the building system.  
From the energy point of view  
Through the data collection, the total energy of the new power and heat supply subsystem has been calculated. Thus, it provides support for the completion of the calculation of sustainability indicators in Table 12.  
Figure 19: A new power and heat supply subsystem.  
Table 12 and Figure 20 clearly show the change differences between the previous index and the improved indicator. As a whole, with the new system connected, four indexes are increased, including Renewable input (\(\mathrm{R_{i}}\)), Nonrenewable resource (\(\mathrm{N_{s}}\)), Energy feedback input (\(\mathrm{E_{f}}\)) and the Emergency sustainability indicator (ESI). The remaining two indicators are decreased (Emergy yield ratio (EYR) and Environmental loading ratio (ELR)). Although the energy of the whole system is increased, the addition of a renewable energy system leads to an environmental pressure reduction, which enhances the sustainability effect of the whole building system. Taking the ESI as an example, the sustainability effect was significantly boosted (from 0.849 to 2.197), with an increment of 1.58 times.
## 3 System Model and Problem Formulation
### Calculation Model
#### 3.3.3 Cloud Computing Model  
Assume that the communication link between the M-IoT device and the satellite has a fixed transmission rate \(R_{s}\), which depends on the modulation and coding scheme and the bandwidth allocation of the satellite communication system. Let \(f_{i}^{s}\) represent the cloud computing capability allocated to the \(i\)-th M-IoT device. Then, the transmission and processing delay for cloud computing can be expressed as:  
\[T_{i}^{s}=\frac{D_{i}^{s}}{R_{s}}+\frac{D_{i}^{s}C_{i}}{f_{i}^{s}} \tag{12}\]  
Accordingly, the energy consumption for offloading the task of the \(i\)-th M-IoT device to the LEO satellite is:\[E_{i}^{s}=\frac{P_{i}^{s}D_{i}^{s}}{R_{s}} \tag{13}\]  
Here, \(P_{i}^{s}\) is defined as the transmission power for offloading the task of the \(i\)-th maritime IoT device to the LEO satellite.  
In the partial offloading method, the total delay in the computing task is determined by the maximum values of the local execution and offloading execution delays. In addition, the total energy consumption for processing the task should include both local computation and the offloading energy. Therefore, the total delay and energy consumption for executing the task \(D_{i}\) on the \(i\)-th M-IoT device are expressed as:  
\[T_{i}^{\text{total}}=\max\Bigl{\{}T_{i}^{l},T_{i}^{u},T_{i}^{s}\Bigr{\}} \tag{14}\]  
\[E_{i}^{\text{total}}=E_{i}^{l}+E_{i}^{u}+E_{i}^{s} \tag{15}\]  
The weighted sum of the total delay and total energy consumption for the \(i\)-th M-IoT device at the \(t\)-th time slot represents the system cost for the \(i\)-th M-IoT device at time \(t\), which can be defined as:  
\[\text{Cost}_{i}(t)=\omega_{T}T_{i}^{\text{total}}+\omega_{E}E_{i}^{\text{total}} \tag{16}\]  
Here, \(w_{T}\) and \(w_{E}\) are weight parameters, representing the weight factors for the delay and energy consumption, respectively, satisfying the constraints \(0\leqslant w_{T}\leqslant 1\), \(0\leqslant w_{E}\leqslant 1\) and \(w_{T}+w_{E}=1\).
## 4 Results Analysis
### Summary and Outlook
#### 4.1.1 Optimize Interaction Design to Improve Usability  
By adopting intuitive interfaces, clear navigation cues, and multimodal interaction methods (e.g., voice recognition, gesture control), the learning curve for visitors can be minimized, ensuring accessibility across age groups and technical proficiencies. Additionally, ensuring system stability and smooth operation--avoiding delays or lags--will enhance user experience, thereby boosting perceived usefulness and satisfaction.
## 6 Parameter Dependence of Solutions
### Dependence on Prandtl Number, Pr  
The dependence of solutions on the Prandtl number Pr is illustrated in Figures 22-24. Unlike the Grashof number, the Prandtl number represents the driving force for the fluid temperature. In particular, higher Prandtl number values lead to lower fluid temperatures and vice versa, as illustrated in Figure 22. Given that the fluid velocity, in the setting of natural convection flow, is directly influenced by the prevailing temperature conditions, the behaviour of the fluid velocity, therefore, mirrors that of the temperature as shown in Figures 23 and 24.  
Figure 23: Axial velocity dependence on Pr at \(t=5\).  
Figure 22: Temperature dependence on Pr at \(t=5\).
# Hybrid Uncertainty Metrics-Based Privacy-Preserving Alternating Multimodal Representation Learning
## 2 Related Work
### Privacy Protection in Multimodal Learning  
With growing privacy concerns, privacy-preserving techniques have gained prominence in machine learning. Differential Privacy (DP) [13] offers a robust solution by injecting noise into the training process to protect individual data privacy. DP-SGD, for instance, implements DP in deep learning through gradient clipping and Gaussian noise. In the context of multimodal learning, [PERSON] [5] introduced a multimodal differential privacy framework that applies local differential privacy to fused representations, safeguarding user privacy. Complementary approaches include federated learning, which enables privacy-preserving training on decentralized datasets, and homomorphic encryption, which supports computations on encrypted data. The urgency of such measures is underscored by [PERSON] [14], who demonstrated the vulnerability of machine learning models to membership inference attacks, highlighting the need for robust privacy protections. Additionally, [PERSON] [15] provides a practical, secure two-party inference solution, while [16] explores horizontal multi-party data publishing with adaptive noise under differential privacy, and [17] enhances truth discovery using noise-aware fusion under local differential privacy.  
Recent advances in adaptive differential privacy have provided new insights for multimodal learning. [PERSON] et al. [18] proposed a multi-stage adaptive differential privacy algorithm in asynchronous federated learning, which dynamically adjusts the privacy budget to improve the trade-off between privacy and utility (PubMed). Although focused on federated learning, this adaptive mechanism offers inspiration for privacy protection in multimodal learning, particularly when handling heterogeneous data. Similarly, [PERSON] et al. (2019) conducted a comprehensive survey on differential privacy in deep learning, emphasizing the role of adaptive parameter tuning in enhancing model utility with potential applications to multimodal learning scenarios (ScienceDirect). These studies highlight the potential of adaptive differential privacy to optimize noise levels dynamically, thereby improving the applicability of multimodal models in privacy-sensitive domains.  
However, applying these techniques to multimodal learning poses challenges. Federated learning, while privacy-friendly, struggles to coordinate across heterogeneous data distributions typical of multimodal settings, often leading to slow convergence. Methods like MLA, lacking inherent privacy safeguards, are ill-suited for sensitive domains such as medical or financial applications. Moreover, uniformly applying DP across multimodal models can degrade performance, particularly in critical components like shared heads responsible for integrating cross-modal information. This trade-off between privacy and utility remains a significant hurdle in deploying multimodal systems in real-world, privacy-critical scenarios.