esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
SEM scores were small, ranging from 1.2\({}^{\circ}\) to 1.5\({}^{\circ}\), supporting the precision of the inter-examiner measurements. Furthermore, the MDC values at 95% confidence were slightly higher but still low, ranging from 3.4\({}^{\circ}\) to 4.0\({}^{\circ}\), indicating the sensitivity of the test to detect meaningful changes. Therefore, these results confirm high inter-examiner reliability for hip flexion angle measurements during the SLR test, regardless of whether single measurements or the mean average of two measurements were used. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Intra-Examiner Reliability**} & \multicolumn{2}{c}{**Total Sample (\(\mathbf{r}\) = 53)**} & \multicolumn{2}{c}{**Patients with LBP (\(\mathbf{r}\) = 38)**} & \multicolumn{2}{c}{**Asymptomatic Controls**} \\  & \multicolumn{2}{c}{**Novel**} & \multicolumn{2}{c}{**Experienced**} & \multicolumn{2}{c}{**Novel**} & \multicolumn{2}{c}{**Experienced**} & \multicolumn{2}{c}{**Novel**} & \multicolumn{2}{c}{**Experienced**} \\  & **Examiner** & **Examiner** & **Examiner** & **Examiner** & **Examiner** & **Examiner** \\ \hline Trial 1 & 68.6 \(\pm\) 15.9 & 67.0 \(\pm\) 15.5 & 62.9 \(\pm\) 14.0 & 61.3 \(\pm\) 13.5 & 83.1 \(\pm\) 10.7 & 81.6 \(\pm\) 9.8 \\ Trial 2 * & 67.8 \(\pm\) 15.9 & 67.1 \(\pm\) 15.9 & 62.0 \(\pm\) 13.9 & 61.5 \(\pm\) 13.6 & 82.6 \(\pm\) 10.7 & 81.4 \(\pm\) 1.8 \\ Mean & 68.2 \(\pm\) 15.9 & 67.1 \(\pm\) 15.5 & 62.4 \(\pm\) 13.9 & 61.4 \(\pm\) 13.6 & 82.9 \(\pm\) 10.7 & 81.5 \(\pm\) 9.8 \\ Absolute error & 1.0 \(\pm\) 0.9 & 0.9 \(\pm\) 0.8 & 1.0 \(\pm\) 1.0 & 0.9 \(\pm\) 0.7 & 0.8 \(\pm\) 0.6 & 0.9 \(\pm\) 0.9 \\ ICC\({}_{3,1}\) & (0.998; 0.999) & (0.998; 0.999) & (0.997; 0.999) & (0.996; 0.999) & (0.990; 0.998) \\ SEM & 0.5 & 0.5 & 0.6 & 0.6 & 0.5 & 0.7 \\ MDC\({}_{55}\) & 1.4 & 1.4 & 1.7 & 1.7 & 1.3 & 1.9 \\ \hline \hline \end{tabular} Abbreviations: ICC: Intraclass Correlation Coefficient; MDC: Minimal Detectable Changes; SEM: Standard Error of Measurement; *: Trial 2 was measured 1 week after Trial 1.  
\end{table} 
Table 2: Intra-examiner reliability estimates for hip flexion angle measurement during the straight leg raise test.
# Performance Evaluation of China's First Ocean Dynamic Environment Satellite Constellation
## 1 Introduction
A comprehensive and systematic evaluation of the altimetry data of the HY-2B/C/D three satellite radar altimeters includes the observation efficiency of the altimeter and the observation accuracy of the observation parameters SLA, SSWS, and SWH. In this study, Section 2 introduces the data acquisition and ocean data screening criteria and the basic methods used for the experiments. Section 3 discusses the efficiency levels of the three HY-2B/C/D satellites and radar altimeter observations of six other satellites are compared and analyzed. The standard deviation of the SLA at the single mission \begin{table}
\begin{tabular}{c c c} \hline \hline
**Satellite** & **HY-2B** & **HY-2C/D** \\ \hline Track height/km & 970.732/972.836 km & 957.583/958.490 km \\ \hline Repeat cycle/day & 14/168 day & 10/400 day \\ \hline \multirow{8}{*}{Instruments} & Radar altimeter, microwave scatterometer, microwave radiometer, calibration radiometer, automatic identification system (AIS) for ships, data collection system (DCS) & Radar altimeter, microwave radiometer, calibration radiometer, automatic ship identification system (AIS), data collection system (DCS) \\ \cline{1-1}  & Non-sun-synchronous orbit with the inclination of 66\({}^{\circ}\), frozen track \\ \hline \multirow{4}{*}{Measurement elements} & Sea surface wind field, sea surface height, SWH, gravity field, ocean circulation, sea surface temperature & Sea surface wind field, sea surface height, SWH, gravity field, ocean circulation, sea surface temperature \\ \cline{1-1}  & & \\ \end{tabular}
\end{table} 
Table 1: Composition of China’s ocean dynamic environment satellite constellation and its main information. crossover points and the dual crossover points for each cycle of the HY-2B/C/D satellites was calculated. Section 4 discusses and summarizes the article.
## 3 Results and Discussions  
We study the evaporation of liquid film by the mixed convection of a humid air. This liquid film is falling down on one of two vertical channel plates, which is heated. The second plate is dry and exchanges heat with the outlet with a certain heat transfer coefficient h = 15w/Km\({}^{2}\). In this study, we present the effect of the nano fluid volume rate on the transfers when the film plate is heated. The simulations are done under the following conditions: (T\({}_{0}\) = 25 \({}^{\circ}\)C, T\({}_{\rm{nf}}\) = 25 \({}^{\circ}\)C, d = 0.015 m, H = 3 m, u\({}_{0}\) = 1 m/s, c\({}_{0}\) = 0 and m\({}_{0L}\) = 0.01 kg\(\cdot\)m\({}^{-1}\)s\({}^{-1}\)).  
At present, the film plate is heated by a uniform heat flux q\({}_{\rm{w}}\) = 1000 W/m\({}^{2}\). Figure 3 shows the effect of the volume rate of the nanoparticle on the channel temperature at the exit. Because of the heating, the effect of the thermal conduction is significant and dominate the effect of density and the specific heat. At seen in Equations (9) and (17), an augmentation of the thermal conductivity induces an increase of the film temperature. Also, it augments interfacial temperature (Figure 4) and consequently the vapor temperature. This augmentation of k exists because of the increasing of x\({}_{\rm{v}}\)[14]. The saturation vapor concentration increases when we elevate the nanoparticle volume rate (Vachon [16]). This is shown in Figure 5. Figure illustrates the effect of an increasing of the nanoparticle volume rate on the evaporation production. As a consequence of interfacial vapor concentration, the gradient of concentration near the interface is elevated. This increases naturally the evaporation rate (Figure 6). The augmentation of the film density, due to the x\({}_{\rm{v}}\) increasing [14], induces a deceleration of the film (Equation (8)) as shown in Figure 7. The thermal conductivity doesn't affect the convection of the film (see the equation of momentum of the nano film--Equation (8)).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
\begin{tabular}{c} **POSITION** \\ **AXIALE** \\ \end{tabular} & **(121,121,81)** & **(121,121,21)** & **(121,51,41)** & **(121,51,21)** & **(71,51,21)** & **(51,41,21)** \\ \hline \hline
**X = 0.2** & 333.73 & 333.65 & 333.45 & 333.42 & 333.60 & 333.39 \\ \hline
**X = 0.4** & 334.96 & 334.92 & 334.81 & 334.73 & 334.91 & 334.69 \\ \hline
**X = 0.6** & 337.43 & 337.39 & 337.29 & 337.22 & 337.12 & 337.36 \\ \hline
**X = 0.8** & 339.06 & 339.02 & 338.88 & 338.77 & 338.71 & 338.98 \\ \hline
**X = 1.0** & 340.35 & 340.31 & 340.18 & 340.11 & 340.02 & 340.28 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Stability of calculation from a mesh variations-interfacial temperature.  
Figure 3: Effect of the nanoparticle volume rate on the exit channel temperature – heated plate case.  
Figure 4: Effect of the nanoparticle volume rate on the interfacial temperature – heated plate case.  
Figure 5: Effect of the nanoparticle volume rate on the interfacial vapor concentration – heated plate case.  
Figure 6: Effect of the nanoparticle volume rate on the total evaporation rate-heated plate case.
# A Novel Cryptography-Based Architecture for Secure Data Asset Sharing and Circulation Systems
## 5 System Architecture  
At present, there are many problems in the construction mode of cryptography security systems [14]. Because of the complex application scenario and many security requirements, different types and a large number of cryptography devices and cryptography technologies are introduced to connect various security application services. The direct consequences of the construction mode of this cryptography security system are as follows.  
* Decentralized deployment of equipment, difficult to manage;
* A wide variety of technologies and heavy management workload;
* Complex integration of cryptography security applications with existing systems;
* Lack of intensive use and scheduling of cryptography security resources;
* Lack of cloud computing and edge computing environment;* Lack of quantification of cryptography security services and lack of supervision of applications.  
Therefore, it is difficult to meet the requirements of security protection in the process of cross-border cross-domain sharing and circulation of massive data assets.  
Based on the above analysis, an architecture design scheme of data asset security sharing and circulation system based on cryptography service technology is proposed. A set of comprehensive security service platform software systems is formed by aggregating and integrating a variety of new cryptography and data security technologies, and a flexible multi-mode service aggregation capability is adopted by way of edge computing, micro-service and capacity deployment. This provides unified management and convenient service for the safe sharing and circulation of data assets in uncontrolled scenarios.
# Advancing Few-Shot Named Entity Recognition with Large Language Model
## 2 Related Work
### Language Model Plug-Ins  
In addition to their strong in-context learning abilities, LLMs can be further enhanced by integrating external tools. Several studies have explored this idea by leveraging the SLM to complement LLM capabilities in natural language understanding (NLU) tasks. For instance, [PERSON] et al. [23] suggest using the SLM as a plug-in to improve the LLM's NLU capabilities and mitigate instability in in-context learning. However, their work does not specifically target named entity recognition, nor does it consider a few-shot or metric-learning-based setting.  
[PERSON] et al. [24] propose an approach that integrates span-based few-shot NER models [25; 26] with LLM-based re-ranking of uncertain entities. Their work primarily focuses on span-based NER, where entity spans are first identified using local models before being refined by the LLM. This approach differs fundamentally from our metric-learning-based framework, where entity recognition is formulated as a similarity computation problem rather than a span-detection task. Similarly, [PERSON] et al. [27] employ uncertainty estimation to facilitate collaboration between LLM and the local NER model, aiming to reduce errors in recognizing unseen entities. While effective, their framework is designed for span-based or fully supervised settings, rather than the few-shot setting we investigate.  
In contrast, our work explores the integration of the SLM and the LLM in a metric learning-based few-shot NER framework, an area that remains underexplored. Metric learning-based NER presents unique challenges, such as sensitivity to distributional shifts between the support and query sets, which span-based or fully supervised models do not explicitly address. Additionally, unlike prior studies that rely heavily on manually designed chain-of-thought (CoT) prompts, our method reduces reliance on handcrafted demonstrations, making it more adaptable to different NER tasks.
## 2 Materials and Methods
### Experimental Settings  
#### 2.4.1 Evaluation Protocols  
For quantitative comparison, we report three widely used metrics, including the mean Dice coefficient (mDice), mean intersection over union (mIoU), and mean absolute error (MAE). mDice and mIoU focus on the internal consistency of objects, while MAE represents the average value of the absolute error between the prediction and ground truth.
# Time-Dependent Alternative Route Planning: Theory and Practice +
## 1 Introduction  
Querying a route planning service is now a common daily-routine activity. The majority of such services, as well as of the underlying route planning algorithms, answer queries by offering a route plan from an origin \(o\) to a destination \(d\)[1]. A typical road network instance is described as a directed graph whose vertices represent junctions and arcs represent one-way road segments. Each arc is accompanied with a positive scalar (a.k.a. weight) representing its usage cost (e.g., distance or traversal-time), with respect to the assumed cost-metric. There is also a given optimization criterion (e.g., the total distance or earliest arrival time) for the selection of routes [1]. Possibly the most typical case for daily commuters assumes that the arc-costs metric consists of traversal-times for the arcs, and the optimization criterion is the earliest arrival time at the destination.  
While a standard Dijkstra run on a continental-sized road network with several millions of nodes and static arc costs would require a few seconds, modern approaches, such as oracles and speed-up schemes, have been developed that exploiting a preprocessing phase to produce a manageable amount of cost metadata and achieve response times to arbitraryroute-planning queries in a few milliseconds, or even microseconds. A comprehensive survey of the subject is provided in [1].
## 3 Methodology
### Nuclear Magnetic Resonance (NMR) Characterization  
NMR characterization was conducted using a Bruker 300 spectrometer. A range of temperatures--from 90 to 130 \({}^{\circ}\)C in 10 \({}^{\circ}\)C increments--was chosen to conduct NMR experiments to determine the self-diffusion coefficient (D) for each binder.  
Using Fourier transform, the acquired NMR spectra were derived from the free induction decay (FID). In Figure 2, a typical \({}^{1}\)H-NMR spectrum of the bitumen is presented. The number of scans used in the pulsed NMR experiment was 8 with pulse width equal to \(\pi\)/2. The measurements of D were conducted using a Diff30 NMR probe. As the transverse relaxation time (\(T_{2}\)) is much shorter than the longitudinal relaxation time (\(T_{1}\)), the Pulsed Gradient Stimulated Spin Echo (PFG-STE) sequence was utilized [46; 47]. This sequence comprises three radiofrequency (RF) pulses of 90 (\(\pi\)/2-\(\tau_{1}\)-\(\pi\)/2-\(\tau_{m}\)-\(\pi\)/2) and two gradient pulses that are performed after the first and third pulse RF. \(\tau_{1}\) and \(\tau_{m}\) are the time intervals between RF pulses (milliseconds). The echo appears at \(2\tau_{1}+\tau_{m}\), and the ECHO amplitude attenuation was derived from the equation below:  
\[I\left(2\tau_{1}+\tau_{m}\right)=I_{0}e^{-[\frac{m}{T_{1}}+\frac{3\tau_{1}}{T_{2 }}+\left(\gamma g\delta\right)^{2}\,\mathrm{D}\left(\Delta-\frac{\delta}{ \delta}\right)]} \tag{2}\]  
where D is the self-diffusion coefficient, and \(I\) and \(I_{0}\) are the signal intensities in the presence and absence of gradient pulses. The NMR characterization parameters applied in the experiments to investigate the samples were as follows: \(\delta\) (2 ms), the gradient length pulse; \(\Delta\), diffusion delay time (30 ms); and \(g\), the gradient amplitude (from 100 to 900 gauss/cm). The number of scans increased due to an increment in the number of repetitions. This NMR has a very low fitting standard deviation and good reproducibility of measurements, where the uncertainty of D is approximately 3%.  
According to the colloidal model, bitumen is generally composed of two principal constituents: asphaltene and maltene. The asphaltene is the rigid and polar part, which is characterized by high melting points. The maltene, in turn, is the soft and oily part that disperses the asphaltenes. Considering the low \(T_{2}\) relaxation times of the asphaltenes [48], the self-diffusion coefficients can be attributed to the oily part of the bitumen; in fact, the NMR signal of asphaltenes relaxes during the pulse's application [49].
# Novel Methods of AI-Based Gait Analysis in Post-Stroke Patients
## 1 Introduction
### Al-Based Approaches to Gait Analysis
#### 1.4.3 Previous Gait Studies Using Fuzzy Numbers  
[PERSON] et al. used fuzzy logic to assess which gait characteristic measurements and combinations best reflected severe gait dysfunction in a group of patients with cerebral palsy. Their retrospective study was conducted on 155 children aged 11 \(\pm\) 5.3 years. In the study, [PERSON]'s Interactive Dichotomizer 3 algorithm predicted the Gait Deviation Index (GDI) value from data concerning range of movement, muscle strength, spasticity level, etc. The establishment of seven rules allowed the algorithm to achieve an accuracy of 90%. Straight thigh muscle strength, spasticity level, and posterior tibialis muscle strength were considered the most important predictors [47]. Fuzzy logic was also used in an intelligent algorithm for (real-time) gait phase detection based on kinetic and kinematic parameters extracted from foot pressure and knee joint angle sensors. The proposed algorithm and graphical user interface have proved useful to clinicians for both physiological and pathological gait assessment [48]. The classification of pathological toe gait patterns using a fuzzy number-based algorithm was the subject of a study by [PERSON] et al. The study included 716 patients, of whom toe gait was observed in 240. Measuring parameters, such as range of movement, degree of spasticity, and muscle strength, as well as the application of 12 rules and the use of a fuzzy logic-based algorithm for their analysis allowed the recognition of toe gait with an accuracy of 81%, and while this increased the objectivity of the result, further research and an increase in the accuracy of the algorithm are required [49]. The results of as truly by [PERSON] et al. are worth noting here: the authors showed the effectiveness of a fuzzy number-based algorithm in detecting spinal ataxia in horses based on their body position (recognition accuracy >70%). A limitation of the above-mentioned study was the small size of the groups (12 horses with spinal ataxia and 12 healthy horses) [50]. This study demonstrates that kinematic findings supplemented with modern analytical methods still have great untapped potential.
## 4 Results and Discussion
### Performance Assessment
In Figure 11a the data are obtained from a borehole with a depth of 5.8 m. Likewise, in Figure 11b the percentage of loam is considered to be 46.1, and Figure 11c addresses soil data their percentage of clay is 26.5. Last but not least, the wet density of 1.68 g/cm\({}^{3}\) is the common variable for the data shown in Figure 11d. Figure 11: \(Cont.\)  
Figure 10: The correlation between the actual and predicted SCC in the training and testing phases of (a,b) ANFIS, (c,d) LCA-ANFIS. Among different existing intelligent models, ANFIS is considered as a leading predictive tool that simultaneously enjoys the learning capability of ANNs and the expert knowledge of FISs [38]. Due to the complexity and non-linearity of engineering issues like the SCC estimation problem, utilizing ANFIS for this aim is a logical task. Similarly, many this model has been successfully used for analyzing the relationship between natural phenomena with complicated conditions (e.g., landslide [39] and flood [40]). The findings of the current paper fall in good agreement with these studies due to the high capability of ANFIS in inferring the non-linear relationship between the compression coefficient and related soil factors for a real-world project. The SCC estimation investigated in this study is classified as a high-dimensional problem due to the presence of several effective factors. It is more highlighted  
Figure 11: Estimated and observed SCCs for data samples when (**a**) depth of sample = 5.8 m, (**b**) loam = 46.1%, (**c**) clay = 26.5%, and (**d**) wet density = 1.68 g/cm\({}^{3}\). when this parameter is not in regular proportion with some effective factors like clay content and plastic index (Figure 5). On the other hand, many studies (e.g., [41]) have shown that conducting feature validity is a reasonable way for reducing the complexity of such problems. Thus, we believe that optimizing the input configuration can be a helpful task for achieving more accurate prediction of the SCC. This idea along with comparing the LCA with other metaheuristic algorithms, can be potent suggestions for future studies in this field.
## 2 Literature Review
### Disease Prediction  
A 2017 study explored disease prediction using big data, categorizing data into structured (e.g., age, gender) and unstructured (e.g., medical records) types. Machine learning algorithms such as naive Bayes, KNN, and decision tree were employed for prediction [17]. Emergency situations necessitate alerting systems in healthcare services.
# Authentication-Based Secure Data Dissemination Protocol and Framework for 5G-Enabled VANET
## 2 Related Work
To the best of our knowledge, an exhaustive secure communication system such as the one presented in this article to provide all five types of interactive levels for the IoV framework has not been designed before. In Table 1, we summarize the security and performance requirements fulfilled by the authentication and privacy schemes discussed above.
## 2 Approach  
The approach compares scene radiances at the top of the atmosphere representing a reference condition, i.e., benthic cover, bottom depth, and water clarity, with test conditions that deviate from the reference condition. Parameters that define a reference condition are highlighted with a prime symbol (\(\cdot\)) while the symbol is omitted from parameters representing test conditions. For example, scene radiance at the top of the atmosphere is written as \(L^{\prime}_{sat}\) for a reference condition and \(L_{sat}\) for a test condition. The prime symbol is also omitted in instances of general reference. In all conditions, the atmosphere was defined using a low optical depth maritime aerosol (Table 1) and was held constant along with solar zenith angle throughout the analysis. The water surface was flat and downwelling sun and sky irradiance reflected from the water surface was not included in the water signal.  
Reference conditions were compared with radiances representing other possible coral reef conditions, generated by randomly and independently adjusting water and benthic properties within realistic ranges. The advantages of this Monte Carlo approach over systematic adjustments of individual parameters are that (1) fewer computations were required to estimate parameter uncertainty, (2) all uncertainties were obtained simultaneously, and (3) all parameter interactions were included in the results. In each case, the randomly generated test spectra were compared with the reference spectrum and determined to be either indistinguishably similar or sufficiently unique based on a spectral difference criterion, \(\varepsilon\), defined by the condition-dependent _SNRs_ of the two signals. A flow chart of the modeling procedure is shown in Figure 1. If the reference and test spectra were indistinguishable, then the test condition was recorded and a new random test condition was generated. Random tests continued until enough indistinguishable conditions were encountered to reasonably resolve the range in parameter values. This typically required between \(10^{5}\) and \(10^{7}\) test conditions. The ensemble of indistinguishable test conditions was then used to identify the range in parameter values as a measure of detection uncertainty.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Quantity** \\ \hline Aerosol model & Maritime \\ Aerosol relative humidity & 90\% \\ Aerosol optical depth (550 nm) & 0.06 \\ Atmospheric model & Tropical \\ Precipitable water vapor & 5.0 cm \\ Ozone & 0.34 atm-cm \\ Solar zenith angle & 30\({}^{\mathrm{o}}\) \\ Sun-earth distance & 149.6 \(\times\) 10\({}^{9}\) km \\ \hline \hline \end{tabular}
\end{table}
Table 1: Atmospheric parameter definitions and quantities representing a clear maritime atmosphere used in all model computations.  
Figure 1: Computation flow chart. SNR, signal to noise ratio.