esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Smart Health and ICS as Critical Cyber Infrastructure
In smart health context, it can identify hospitals, manage node networking, and collect related data, such as physician and nurse ID information, patient medical in formation, basic information about the location of pharmaceuticals, medical equipment, an object's geolocation in the hospital infrastructure, and information about the hospital environment [30]. The collected data are transmitted to the core network through various  
Figure 2: Predicted dominant IoT application by 2025.  
communication technologies, for example, Wi-Fi, Bluetooth, RFID, NFC, UWB, BLE, LTE-A, and IEEE 802.15.4. Level 2 (Control/Command): This layer receives the data sent from the field by the sensors (level 1), controls the actuators (level 1) and communicates with the operators via the local Human Machine Interfaces (HMIs) and the supervision room. The main element of this level is the Programmable Logic Controller (PLC), which controls the system in real time. Level 3 (Supervision): The role of this layer is to give an image at a point in the process. Thus, the data acquired by the lower levels are fed back. Operators can also control the system via production orders to adapt the control law and avoid damage. The term Supervisory Control And Data Acquisition (SCADA) can be used to refer to this part. Further, a set of applications dedicated to hospital computerization, management of medical technology, and all other aspects concerning management and supervision of health services [30], such as the computerization of ambulatory care management, visit management, diagnostics, radiology, pathologies, and physical therapy, medication management, material management, patient management, and financial management. Level 4 (Communication Networks): Communication networks allow the different layers to be connected. Originally, ICSs used analog, digital or proprietary communication  
Figure 3: Architecture of an IoT-based CPS infrastructure. protocols to exchange information, however, in recent architectures, TCP/IP is widely used to increase the volume and speed of data transported [36]. The communication networks is the backbone of CPS infrastructure. It supports access to the IoT backbone [37] and facilitates the transmission and reception of medical and industrial data. Furthermore, it facilitates the use of specific communications, including virtualization technologies when migrating to an IaaS Cloud. Thus, this layer is a platform of services that provides an open interface [38] for the various services related to end users. However, cyber vulnerabilities in critical cyber infrastructure dramatically expose patients, medical staff, monitoring medical devices, industrial equipment, PLC and the entire ICS to a variety of serious cyber threats. Therefore, a CPS in cyber health, smart grid and industrial control environment are critical cyber infrastructures that must have maximum protection against malicious cybercriminals.
## 2 Materials and Methods
### DWI and DCE-PWI  
MRI examinations for tumor staging and follow-up were performed with a 1.5 T MR device (Magnetom Aera, Siemens Healthcare, Erlangen, Germany) with a devoted head and neck coil. The MR acquisition protocol included pre- and post-contrast sequences (Table S2 in Supplementary Materials). An axial fat-saturated echo-planar imaging-based DWI with two different b-values (b50 - 800 s/mm\({}^{2}\)) was acquired. ADC values of primitive tumors and residual tissues after CRT were calculated by positioning three regions of interest (ROI) with an average intratumoral area of 0.30-0.40 cm\({}^{2}\) each on three contiguous axial sections. DCE-PWI was obtained through two volumetric interpolated breath-hold examination (VIBE) T1-w sequences characterized by 3.5 mm slice thickness, 0.7 interslice gap, FOV 250 \(\times\) 226 mm, matrix 139 \(\times\) 192, flip angles 5\({}^{\circ}\) and 15\({}^{\circ}\), and acceleration factor 3 for baseline T1-mapping acquisitions. After contrast agent administration, one VIBE T1-w lasting 350 s and with a temporal resolution of 5 s was acquired as follows: TR 4.65 ms, TE 1.66 ms, 3.5 mm slice thickness, FOV 250 \(\times\) 226.6 mm, matrix 139 \(\times\) 192, flip angle 30\({}^{\circ}\), acceleration factor 3, and peripheral K space sampling with time to center 2.2 s. Time/intensity curve, area under the curve (AUC), and K(trans) values of primitive tumor and tumor residual/relapse tissues after CRT were generated by using IntelliSpace software version 9.0 (Philips, Amsterdam, The Netherlands) from the native DCE-PWI images by drawing an ROI including at least 50% of the largest lesion diameter. Before lesion sampling, an ROI was placed on the internal carotid artery to obtain the arterial input function curve, defined as the contrast concentration in vessels feeding to tissue at each point in time during the contrast passage. Vessels, cystic areas within solid lesions, and necrotic, hemorrhagic, or proteinaceous areas detected on T1-w and T2-w sequences were excluded in both DWI and DCE-PWI analysis. ADC, AUC, and K(trans) values of the trapezius muscle on the same side of the tumor were also obtained.
# A Brief Review of Multipath TCP for Vehicular Networks
## 6 Multipath Routing in Vehicular Networks
### Fault-Tolerance and Security  
Path breakage happens frequently in vehicular networks due to vehicle mobility. Meanwhile, VANETs are vulnerable to misbehaving nodes. Therefore, there are numerous studies focusing on fault-tolerant, fast restoration, or security. [PERSON] et al. [119] proposed a fast restoration on-demand multi path routing (FROMR) scheme. FROMR focuses on rapidly building an alternate path when original path breaks. [PERSON] et al. [120] developed a leaky-path model by evaluating the effect of faults by using statistical and estimation information. In addition, fault-correlation between different paths is also considered in the routing approach. The results demonstrate that the algorithm outperforms the standard flow control approach in terms of network throughput and fairness. Some efforts have been made to handle malicious attack issues. [PERSON] et al. [121] proposed a multipath routing protocol that considers user QoS requirements and the trust of nodes. At first, the trusted nodes are extracted by collecting opinions from neighboring nodes. Each node is assigned with a trust value. Then, a trusted route is generated by taking into account the route cost metric (delay and so on) and trust value of nodes for route selection.
# Progress on the Application of Nanomaterial Expansion in Oil Displacement
###### Abstract  
Research on nanomaterials has opened up new opportunities for enhancing oil recovery. This paper reviews the mechanisms of nanomaterials for enhancing oil recovery, including reducing oil-water interfacial tension, regulating rock wettability, and decreasing crude oil viscosity, with a focus on the expansion of the oil-displacement mechanism of nano-silica. Nanofluids form interfacial nanolayers that diminish tension while altering surface wettability to promote oil stripping. Their structural disjoining pressure creates wedge-shaped films at oil-water-solid interfaces, driving droplet detachment. The synergistic \"roll-up\" and \"diffusion\" mechanisms improve oil mobility through capillary regulation and interfacial disturbance. These findings offer critical insights for optimizing nanomaterial applications in oil displacement systems, advancing fundamental research and technological development for enhanced recovery processes.  
Keywords: nanomaterials; expansion displacement; SiO\({}_{2}\); low-permeability reservoir; oil recovery +
Footnote †: journal: Applied Sciences
# Research Advances and Application Prospect of Low-Temperature Plasma in Tumor Immunotherapy
## 1 Introduction  
Recent years have seen great breakthroughs in tumor immunotherapy, which has become an optional treatment for patients with metastatic and recurring tumors. Immunotherapy improves the body's immune response by adjusting its natural defense mechanism, thereby achieving anti-tumor effects. Low-temperature plasma is an ionized gas close to room temperature, which can generate a large amount of reactive oxygen and nitrogen species (RONS). LTP can directly fight tumors by damaging DNA, inhibiting cell proliferation, and causing cell apoptosis [1]. It can also stimulate anti-tumor immunity by regulating the functions of immune cells [2, 3]. Therefore, LTP has become a new treatment of tumors. This article will review the research advances and application prospect of LTP in cancer immunotherapy.
# Misalignment Fault Prediction of Wind Turbines Based on Combined Forecasting Model
## 3 Signal Acquisition and Feature Extraction
### Feature Extraction
#### 3.2.2 Frequency-Domain Feature Parameters  
Time-domain analysis can only briefly and roughly represent the fault of the device, but it cannot find the inherent cause of the fault. Frequency-domain analysis can transform the signal from the time-domain to the frequency-domain through Fourier transform. Different frequency components are clearly displayed to find the fault frequency. Assume that \(x_{i}\), \((i=1,2,3,\ldots,N)\) is a finite-length discrete time series and the sampling frequency is \(f_{c}\). The calculation equation of commonly used frequency-domain feature parameters are as follows.  
Mean Square Frequency (MSF):  
\[MSF=\frac{1}{4\pi^{2}f_{c}^{2}}.\frac{\int_{0}^{\pi}{\omega^{2}S(\omega)d\omega} }{\int_{0}^{\pi}{S(\omega)d\omega}} \tag{22}\]  
Centroid Frequency (FC):  
\[FC=\frac{1}{2\pi f_{c}}.\frac{\int_{0}^{\pi}{\omega S(\omega)d\omega}}{\int_{ 0}^{\pi}{S(\omega)d\omega}} \tag{23}\]  
Variance Frequency (VF):  
\[VF=\frac{1}{4\pi^{2}f_{c}^{2}}.\frac{\int_{0}^{\pi}{(\omega-2\pi f_{c}FC)S( \omega)d\omega}}{\int_{0}^{\pi}{S(\omega)d\omega}}=MSF-FC^{2} \tag{24}\]  
where \(S(\omega)\) is the power spectrum of the signal. \(S(\omega)=X(\omega)\cdot\overline{X(\omega)}\), \(X(\omega)=\sum\limits_{i=1}^{N}{x_{i}e^{-j\pi\omega}}\). \(\omega\) is the discretized angular frequency.
## 4 Experiments and Results
### Experiments
#### 4.4.4 Comparison with the Other Model  
We compare several other large models using the same parameters and dataset. To validate the effectiveness of the proposed method, we use the model combining Slerp and TIES as the baseline for comparison. Table 7 presents a comparison of RL-Fusion with Other large language models with a similar number of participants using the MMLU and C-eval datasets.  
Table 7 compares RL-Fusion with several large models using the MMLU and C-Eval datasets. RL-Fusion achieves 70.8% on MMLU and 78.6% on C-Eval, outperforming models like Mixtral 8x7B (70.6%, 74.7%) and Qwen 7B (56.7%, 59.6%). Notably, RL-Fusion also surpasses the performance of Slerp (68.7%, 74.1%) and TIES (66.9%, 76.2%), demonstrating the effectiveness of the proposed fusion approach. While it lags behind Llama 3.1-8B (73.0%, 81.2%), it shows significantly better performance than smaller models such as LLaMA 2-7B (45.3%, 75.2%) and GPT-3 6.7B (43.2%, 54.4%). Overall, RL-Fusion demonstrates competitive performance with larger models while being more resource-efficient.
## 4 Materials and Methods
### NanoDSF Experiments  
NanoDSF experiments were performed using a Prometheus NT.48 instrument (Nanotemper Technologies) and the provided software PR.thermocontrol v2.0.4. Up to 48 capillaries containing 10 \(\upmu\)L of sample were sequentially illuminated at 280 nm, and fluorescence intensity at 350 (F350) and 330 (F330) nm was measured at increasing temperatures (20-95 \({}^{\circ}\)C, 1 \({}^{\circ}\)C/min). The F350/F330 ratio and its first derivative were then plotted versus temperature.  
NanoDSF analyses were performed on wild-type and mutated LAIR-1 lg-like domain at a concentration of 80 \(\upmu\)M alone or in presence of 80 \(\upmu\)M of a synthetic triple helical collagen peptide in order to detect the collagen peptide binding to LAIR-1. The synthetic collagen peptide with the sequence Ac-GPOGPOGPOGKPKGEQGPOGPO-NH2 was purchased from GL Biochem (Shanghai, China).  
The following are available online at [[https://www.mdpi.com/article/10.3390/ijms22105125/s1](https://www.mdpi.com/article/10.3390/ijms22105125/s1)]([https://www.mdpi.com/article/10.3390/ijms22105125/s1](https://www.mdpi.com/article/10.3390/ijms22105125/s1)), Figure S1: Comparison of bacterial and mammalian recombinant LAIR-1 extracellular region, Figure S2: Far-UV circular dichroism spectra (from 204 to 260 nm) of LAIR-1 WT (in blue), R59A (in green) and E61A (in red), Figure S3: Alternative models of LAIR-1 lg-like domain in complex with C1q collagen stem, Figure S4: Supplementary GPP triplets introduce additional LAIR-1 binding sites within CLR_nc2, Figure S5: Binding assay of a control collagen peptide on LAIR-1 mutants by NanoDSF experiments.  
**Author Contributions:** [PERSON], [PERSON], [PERSON] and [PERSON] designed the study. [PERSON], [PERSON] and [PERSON] performed the research. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] analyzed the data. [PERSON], [PERSON], [PERSON] and [PERSON] wrote the manuscript draft. All authors contributed to the article and approved the submitted version. All authors have read and agreed to the published version of the manuscript.  
Research reported in this manuscript and the APC was supported by the French National Research Agency (grant ANR-16-CE11-0019).  
Not applicable.  
This work used platforms of the Grenoble Instruct-ERIC center (ISBG; UMS 3518 CNRS-CEA-UGA-EMBL) within the Grenoble Partnership for Structural Biology (PSB), supported by FRISBI (ANR-10-INBS-05-02) and GRAL, financed within the University Grenoble Alpes graduate school (Ecoles Universitaires de Recherche) CBH-EUR-GS (ANR-17-EURE-0003). We acknowledge [PERSON] for access to the NanoDSF device, [PERSON] for the CD experiments and [PERSON] for the MS experiments. We acknowledge [PERSON] and [PERSON] for preparing sC1q and C1r2s2 samples, respectively. IBS acknowledges integration into the Interdisciplinary Research Institute of Grenoble (IRIG, CEA).  
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Classification and Mapping of Paddy Rice by Combining Landsat and SAR Time Series Data
## 4 Results
### Analysis of Temporal and Spectral Characteristics of Paddy Rice
Paddy rice shows a distinct phenological pattern in comparison with other land cover classes because paddy rice is flooded during the planting (transplanting) season, unlike other vegetation classes that have a continuously increased/decreased pattern (Figures 4 and 5). While paddy rice is distinguishable between July and September at site A, vegetation related classes ( paddy rice, field, forest, and grass) show a similar pattern in NIR, MIR2, NDVI, and NDWI at site B. This is because sites A and B have different climatic and environmental characteristics: data availability due to clouds (site B has a high cloud cover rate) and the composition of land cover, especially in vegetation types (site B has more forests and denser vegetation areas than site A). However, the difference between planting and harvest seasons of paddy rice is distinct in both sites, and the characteristics of paddy rice are well depicted, especially in NIR spectral bands and SAR backscattering coefficients.
## 2 Theory
### Algorithm II for Identification of Bearings' Main and Cross-Coupled Coefficients
Equations (8) and (11) are the formulas of Algorithm I, which provides an efficient means for rolling-bearing coefficients identification. Equations (16) and (17) are the formulas of Algorithm II, and can be applied to the identification of rolling-bearing coefficients and oil-journal bearing coefficients. The input is \(\rm m+n+1\) unbalance responses, which can be measured under working conditions. The unbalance responses of the measured bearings must be included. In engineering, the \(\rm m+n\) measuring points can be at the location of the discs and the bearings and the last measuring point can be at any other location on the rotor shaft. There is no need for external excitation and a machine run-down/run-up when using the two algorithms. Moreover, the two proposed methods can be applied to rotors with any discs and any bearings.
## 1 Introduction
It mainly includes two processing stages, referred to as calibration stage and localization stage respectively. In the calibration stage, the BR measurements from the calibration targets are exploited to refine the inaccurate transmitter and receiver positions; in the localization stage, the refined transmitter and receiver positions and BR measurements corresponding to the unknown target are exploited to determine the target position. Both processing stages are closed-form, which brings the proposed method computational efficiency and high robustness. Furthermore, the accuracy of the proposed solution is shown analytically to reach the CRLB when the transmitters/receivers/calibration targets' position errors and the BR measurement noises are sufficiently small. Simulations will be conducted to verify the effectiveness and superiority of the proposed solution over existing methods. _Notations_: There will be a lot of notations throughout this paper. Without exception, vectors (matrices) are denoted by bold lower (upper) case letters, respectively. Also, notations \((\cdot)^{\text{-}},(\cdot)^{\text{T}},\|\cdot\|,\)\((\cdot)^{-1},\text{E}(\cdot),\)\(\mathbf{O}_{\text{pvq}},\)\(\mathbf{I}_{\text{pvp}},\)\(0_{\text{pvx1}},\)\(\text{diag}(\cdot)\) and \(\text{tr}(\cdot)\), represent the true value of a noisy or an estimated variable, transpose operation, Euclidean norm, inverse of matrix, statistical expectation, a \(p\)-by-\(q\) zero matrix, an identity matrix of size \(p\), a \(p\)-by-\(1\) zero vector, diagonal matrix and the trace of a matrix, respectively. The remainder of this paper is organized as follows. Section 2 is about the localization scenario in the presence of transmitter/receiver position error and calibration targets. In Section 3, the corresponding CRLB is evaluated, indicating the potential of calibration targets in improve localization accuracy. In Section 4, a closed-form solution is developed for target localization in the presence of transmitter/receiver position error and calibration targets, and theoretical performance analysis is also given. Section 5 describes the results of Monte Carlo simulations that compare the proposed solution with existing methods. Section 6 is the conclusion.
## 5 Experimental Results and Discussions
### Optimization Setup  
A bound-constrained optimization using MATLAB _pattern search_ with a _GPS Positive basis 2N_ poll method and default stopping criteria was implemented to estimate the dynamic parameters of the mechanism based on the nonlinear form of the inverse dynamics model with a varying nonlinear Stribeck friction model. For simplicity, the masses of the three sliders were assumed to be identical and the friction on the three prismatic joints was assumed to be uniform. Therefore, there was only a single value of each friction parameter instead of three values corresponding to the three sliders. Aside from simplicity concerns, this assumption was justified because all of the sliders moved on the same guideway.  
The iterative optimization algorithm was executed by using the design values as the initial values. Table 1 shows the available design values of the inertial parameters, along with the determined bounds. The bounds of the inertial parameters were determined to be wider toward the upper limits since it was expected that the actual values of the parameters were larger than the design values due to additional parts being attached to the main components. On the other hand, wide non-negativity bounds from zero to infinity were applied to the friction parameters since there was no a priori information on their expected values.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Inertial Parameter** & **Lower Bound** & **Upper Bound** & **Design Value** & **Estimate** \\ \hline \(m_{si}\) & kg & 6.2550 & 9.3825 & 6.2550 & 7.2614 \\ \(m_{L1}\) & kg & 11.1280 & 16.6920 & 11.1280 & 12.1299 \\ \(m_{L2}\) & kg & 9.0800 & 13.6200 & 9.0800 & 10.0817 \\ \(m_{L3}\) & kg & 13.3720 & 20.0580 & 13.3720 & 14.4665 \\ \(m_{p}\) & kg & 30.3510 & 45.5265 & 30.3510 & 31.3359 \\ \(m_{L1}L_{1G}\) & kg\(\cdot\)m & 2.0039 & 4.0077 & 2.6718 & 3.3970 \\ \(m_{L2}L_{2G}\) & kg\(\cdot\)m & 1.4104 & 2.8208 & 1.8805 & 2.4049 \\ \(m_{L3}L_{3G}\) & kg\(\cdot\)m & 2.7355 & 5.4711 & 3.6474 & 3.6484 \\ \(m_{p}x_{PG}\) & kg\(\cdot\)m & 5.4517 & 10.9035 & 7.2690 & 8.2238 \\ \(l_{L1}\) & kg\(\cdot\)m\({}^{2}\) & 0.7323 & 1.4648 & 0.9765 & 1.2206 \\ \(l_{L2}\) & kg\(\cdot\)m\({}^{2}\) & 0.4453 & 0.8906 & 0.5937 & 0.7421 \\ \(l_{L3}\) & kg\(\cdot\)m\({}^{2}\) & 1.0424 & 2.0847 & 1.3898 & 1.5088 \\ \(l_{p}\) & kg\(\cdot\)m\({}^{2}\) & 1.7989 & 3.5979 & 2.3986 & 2.9509 \\ \hline \multicolumn{5}{c}{**Friction Parameter**} \\ \hline \(u_{c}\) & - & 0 & \(\infty\) & & 0.1449 \\ \(f_{0}\) & Ns/m & 0 & \(\infty\) & & 83.1797 \\ \(u_{s}\) & - & 0 & \(\infty\) & & 0.3016 \\ \(v_{s}\) & m/s & 0 & \(\infty\) & & 0.3281 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Estimates of the inertial and friction parameters using a nonlinear model with varying friction.  
Figure 6: Hybrid-kinematics machine composed of lower and upper 3 PRR mechanisms.
# Local Balance and Information Aggregation: A Method for Identifying Central Influencers in Networks
## 5 Experiment
### Experimental Analysis
#### 5.4.5 Correlation Index  
The role of the correlation coefficient is to measure the linear relationship between two variables. Specifically, by calculating the correlation between different algorithms and SIR, we can assess the consistency of different key node identification methods. A value of 1 indicates a perfect positive correlation, \(-1\) indicates a perfect negative correlation, and 0 indicates no linear relationship. By plotting scatter plots, the effectiveness of the correlation between different algorithms can be visualized. Figure 8 shows the correlation coefficient values between different algorithms and SIR in the Soc-hamster network. In this highly interconnected network, the DC, KSGC, and IDME algorithms demonstrated relatively high correlation coefficients. The LBIA algorithm, which integrates node degree, node position, and the aggregation of propagated information, achieved a correlation coefficient of 0.9248. In contrast, the scatter plots of the remaining algorithms exhibit more dispersed distributions. This suggests that the LBIA algorithm exhibits a stronger correlation coefficient in tightly interconnected networks, highlighting its superior capability in identifying important nodes.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & **BC** & **CC** & **DC** & **PageRank** & **KSGC** & **LCH** & **NTPR** & **IDME** & **LBIA** \\ \hline Dolphins & 0.96 & 0.97 & 0.83 & 1.00 & 0.99 & 0.99 & 0.99 & 1.00 & 1.00 \\ USAir97 & 0.70 & 0.99 & 0.86 & 1.00 & 0.99 & 0.99 & 1.00 & 1.00 & 1.00 \\ Bio-celegans & 0.87 & 0.99 & 0.79 & 1.00 & 1.00 & 0.99 & 1.00 & 1.00 & 1.00 \\ Eurroad & 0.94 & 1.00 & 0.44 & 1.00 & 0.91 & 0.94 & 0.99 & 1.00 & 1.00 \\ RT-assad & 0.21 & 0.98 & 0.21 & 0.98 & 0.97 & 0.98 & 1.00 & 0.98 & 1.00 \\ Soc-hamster & 0.71 & 0.98 & 0.90 & 0.95 & 0.99 & 0.98 & 0.99 & 0.99 & 1.00 \\ CiteSeer & 0.59 & 0.95 & 0.56 & 0.94 & 0.93 & 0.93 & 0.98 & 0.95 & 0.95 \\ CA-GrQc & 0.38 & 0.99 & 0.75 & 0.97 & 0.98 & 0.98 & 0.99 & 0.99 & 1.00 \\ CA-Erdos992 & 0.17 & 0.99 & 0.23 & 0.99 & 0.99 & 0.99 & 1.00 & 0.99 & 1.00 \\ \hline \end{tabular}
\end{table}
Table 4: The ability of each algorithm to distinguish between different networks.