esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Unconventional Fibers
### Application to a Rectangular Core Fiber  
The method was applied also in the calculation of fundamental modes of a fiber with an rectangular core, Figure 7, with \(aa\) and \(bb\) semi-sides, with refractive index \(n_{1}\) = 1.54, and a cladding of refractive index \(n_{2}\) = 1.47, for various wavelengths, defined by various V factor values \(V=bb\cdot k_{0}\cdot\sqrt{n_{1}^{2}-n_{2}^{2}}\) and four ratios \(a/b\) = 1.1, 1.3, 1.5, and 2.0.  
Birefringence results between the elliptical and rectangular waveguides are compared in tabular form in Tables 5-8 with equal semi axis. The constant component of the refractive index for calculating with each radius \(r\) is defined as  
\[n=\left\{\begin{array}{c}n_{1},r<b\\ \frac{1}{\pi}(n_{1}\varphi_{1}+n_{2}\varphi_{2})\\ n_{2},  
For the results in the Tables 5-8 we have defined birefringence as simply the difference in the orthogonal fundamental modes normalized to \(k_{0}\), for the rectangular and elliptical waveguides. We can see the elliptical waveguide has greater birefringence in all cases of ellipticity and for all wavelengths (represented by the V value).  
To make a direct comparison, the average refractive indexes as functions of \(r\), for an elliptic core fiber and for a rectangular core fiber of the same aa and bb and \(aa/bb=2\), are shown in Figure 8. In Appendix B, the MATLAB codes for the average refractive indexes of an elliptic and a rectangular core fiber are given. In Figure 8 we observe that the average refractive index of the elliptical waveguide is dropping off faster than the rectangular and this facilitates the explanation of the stronger birefringence for this case.  
\begin{table}
\begin{tabular}{c c c} \hline  & **Rectangular Core** & **Elliptic Core** \\ \hline
**a/b = 1.3** & **Fundamental Mode Values** & **Birefringence (TR)** & **Birefringence (TR)** \\ \hline
**V** & & \\ \hline
1.5 & 1.495847758922450 & 0.000344065006270 & 0.000325722889410 \\
1.7 & 1.501459058153440 & 0.000451639480470 & 0.000520210503450 \\
1.9 & 1.5061979273220210 & 0.000480411129310 & 0.000601842058430 \\
2.1 & 1.510134432440230 & 0.000468166987430 & 0.000615857816190 \\
2.3 & 1.513456882268610 & 0.000346878544030 & 0.00059348711210 \\
2.5 & 1.516262818410440 & 0.00039822740380 & 0.000553682765940 \\
2.7 & 1.5168468205192190 & 0.000358904227540 & 0.000507155757960 \\
2.9 & 1.520689537973830 & 0.000321722665480 & 0.000459732120240 \\
3.1 & 1.522448794667530 & 0.000286796641510 & 0.000414381305640 \\
3.3 & 1.523974761485380 & 0.000255871394240 & 0.000372472456950 \\ \hline \end{tabular}
\end{table}
Table 6: Comparison of birefringence for elliptical and orthogonal core fibers.  
\begin{table}
\begin{tabular}{c c c} \hline  & **Rectangular Core** & **Elliptic Core** \\ \hline
**a/b = 1.5** & **Fundamental Mode Values** & **Birefringence (TR)** \\ \hline
**V** & & \\ \hline
1.5 & 1.498120450723510 & 0.000278361237480 & 0.000295449461120 \\
1.7 & 1.503463400911670 & 0.000357573590440 & 0.000442306154260 \\
1.9 & 1.507908503417930 & 0.00037
## 3 Results and Discussion
### Bone Healing and Regeneration Characteristics of the Materials in the Clinical Application
Large bone defects (2-2.5-times larger than the affected bone) would not be ossified naturally [5; 25; 57]. Hence, \(\alpha\)-CSH bioceramic as a substitute bone graft was necessary to treat large bone defects because of its ability to act as a bone formation booster for a short period [18; 40; 58; 59; 60]. The ability of \(\alpha\)-CSH bioceramic to facilitate new bone formation is related to the absorption of this material in the defect area [18; 23; 41; 59]. The absorption time of calcium sulfate varies widely from one-three months after treatment [23; 40; 61]. The rapid absorption of calcium sulfate provides a large supply of calcium ions revealed by a more radiopaque image on the radiograph [19; 41; 59]. In the absorption process, osteoclasts will absorb calcium sulfate as the absorbing bone, preventing osteoclast activity in the bone and promoting osteoblast activity by increasing the concentration of osteropgenitor cells that play a role in the bone-formation process [18; 19; 25; 40; 41; 58; 60; 62]. Several studies have also reported the success of using calcium sulfate bone graft in patients with bone infections [23; 58; 63]. Due to the ability of calcium sulphate to create an acid environment that is unfavorable for bacteria, causes this material to be recommended in cases of bone defect treatments as the consequences of osteomyelitisitis and tumor resection [23; 62; 63]. Another advantage of using \(\alpha\)-CSH bioceramic as the bone graft is a micro/nano-sized powder characteristic that easily mixes with various liquids and adapts well to the various shapes of bone defects [35; 61; 64]. Furthermore, the setting time of this material is acceptable for use in a wide range of surgical and clinical applications, which allows the clinician to place it in the defect area before it hardens [35; 65]. The analysis results that were found above support the \(\alpha\)-CSH bioceramic properties for use as a bone graft in the treatment of bone defects. However, further study is needed for clinical studying with a larger sample size to strengthen the evidence of the effectiveness of the \(\alpha\)-CSH bioceramic as a bone graft substitute in the treatment of bone defects.
## 3 Analysis of Data Features
### Optimization of Data Acquisition Unit Placement  
The optimal placement of sensors is critical for acquiring data that is more conducive to stride length estimation across various motion states. Accordingly, we analyzed the feature discriminability across the four body locations based on the discriminability data presented in Figure 3. Specifically, we calculated the average differentiation values of acceleration, angular rate, and magnetometer features for each body part. Furthermore, we computed the overall average differentiation across all feature types for each body part, as summarized in Table 2. As shown in the table, the data collected from the chest exhibit more distinct characteristics compared to those from other body parts.  
Furthermore, the data labeled as S4 and S8 in Figure 2 demonstrate more distinct features for squatting and bending. Specifically, the data collected from the chest demonstrate more pronounced and regular periodicity, with clearer and more comprehensive modal features. This characteristic helps improve the accuracy of motion state classification and aligns better with the practical needs of underground rescue scenarios. Additionally, chest data tend to be smoother, with lower noise levels and fewer interferences, facilitating the extraction of step data. In contrast, foot-mounted sensors are more susceptible to disruptions from complex post-disaster ground conditions, such as standing water, which can cause system malfunctions, and uneven terrain, which introduces instability in foot states and significantly reduces the signal-to-noise ratio.  
During rescue missions, personnel often perform complex movements, such as bending, side-stepping, and crawling, when navigating confined spaces. These actions lead to more complicated motion states for the thighs and calves, reducing the reliability of these data for consistent motion classification. Considering these factors, this study identifies the chest as the optimal placement location for the autonomous localization and orientation data acquisition unit in rescue personnel.
## 3 Results and Discussion
### Effect of Particle Size  
Figure 9 presents the effect of the relative particle size (\(h/d\)) to the ratio of simulated Young's modulus to bond Young's modulus (\(E_{s}/E_{b}\)). The bond Young's modulus and the bond strength were 1.0 GPa and 1.0 MPa, respectively. As \(h/d\) increased, \(E_{s}/E\) tended to increase slightly in the uniaxial compressive tests, while converging in the three-point bending tests. In Figure 9a, \(E_{s}/E_{b}\) converged to 1.5 with \(h/d>7.06\), while it was low with \(h/d<7.06\). Following the results shown in Figure 9b, the simulated Young's modulus was proposed as a function of \(E_{s}/E_{b}\) and \(h/d\) and expressed as  
\[\frac{E_{s}}{E_{b}}=-0.603e^{-0.129}\frac{h}{d}+1.414 \tag{22}\]  
Figure 8: Effect of bond strength on the flexural strength.  
Figure 7: Effect of bond strength on: (**a**) stress–deflection curve; (**b**) stress–strain curve.  
The relative particle size ratio (\(h/d\)) was dependent on the porosity and the number of particles. In other words, smaller particle sizes reduced a void between the particles and increased the number of particles per unit volume, resulting in a stiffer assembly. Therefore, if the relative particle size was large enough, the simulated Young's modulus converged [23; 36].  
Figure 10 shows the effect of the relative particle size (\(h/d\)) to the simulated ice strength. The normalized strength converged to be 1.55 for the flexural strength and 4.04 for the compressive strength at \(h/d>7.06\), indicating that the consistent behavior of ice could be predicted when the relative particle size ratio was higher than 7.06.
# Development of a Self-Powered Magnetorheological Damper System for Cable Vibration Control
## 4 Experimental Results and Discussion  
### The Stay Cable with a Passively Operated MR Damper  
Figure 9 shows the modal damping ratios in the first four modes of the test cable controlled by a passively operated MR damper with constant voltage. As expected, there is an optimum voltage corresponding to a maximum modal damping ratio for each mode of the cable. Generally, lower voltage input seems to be better for a higher-order mode except for the first mode of the cable. In this study, 0.5 V was finally selected as the optimal constant voltage for the passively operated MR damper since it can perform quite well for all of the first four modes of the cable.  
Taking the second mode of the cable as an example, the entire time histories of accelerations at the observation point of the cable with a passively operated MR damper attached are shown in Figure 10. It can be seen that acceleration amplitudes continuously increase with steady external excitation at the first stage, and then decay after the release of excitation. Figure 10 illustrates that the MR damper operated in passive-on mode (i.e., 1 V voltage) can only work well when the amplitude of the cable is relatively large. When the amplitude of the cable falls below one threshold, the MR damper may be locked and lose its energy dissipation capacity. As clearly shown in Figure 10c,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Mode Order** & **Natural Frequency (Hz)** & **Modal Damping Ratio (\%)** \\ \hline
1 & 1.434 & 0.59 \\
2 & 1.969 & 0.15 \\
3 & 2.983 & 0.11 \\
4 & 3.912 & 0.12 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Natural frequencies and modal damping ratios in the first four modes of an uncontrolled cable.  
the supplemental modal damping due to the MR damper gets quite small after 20 s. Therefore, the performance with a passive-on operated MR damper will be substantially reduced for the case of cable vibration with low amplitude. For the passive-off mode (i.e., 0 V voltage) of the MR damper, the acceleration amplitudes decay exponentially with a low damping ratio in all stages of the vibration, demonstrating amplitude-independent damping behavior. Hence, the MR damper operated in the optimal passive mode (i.e., 0.5 V voltage) may be a better choice as it has benefits from both the passive-on and passive-off modes.
## 4 Proposed ML Attestation Method and Workflow
### ML Attestation Token  
At present, there are no standardized attestation claims for ML models that define a consistent set of key IDs, along with their types and encoding formats, to ensure seamless interoperability between devices and services. As shown in Figure 2, the proposed ML attestation token includes a collection of general claims that serve as metadata for the model, providing details such as the model version, source, integrity attributes, and other model-specific information. The submodule claims are represented as a Claim-Set, as defined in the EAT specification. This Claim-Set contains the model architecture, formatted according to the framework used for its creation. The claims are part of the authenticated container and encoded in CBOR format, consistent with the other general claims. Various ML frameworks offer methods to retrieve a summary of a model's structure and configuration. For example, TensorFlow or Keras provides the model.summary() function, which generates a tabular overview of the model's layers, their shapes, and parameters. Similarly, scikit-learn enables access to model properties such as coefficients, intercepts, and hyperparameters. The get_params() method in scikit-learn further allows users to obtain the hyperparameters of a model, including regularization strength, the solver used in logistic regression, or the depth of a decision tree. These features provide valuable insights into the model's configuration, facilitating optimization and fine-tuning. For the general claims, we propose a set of claims outlined in Table 2. For the submodule claims, the process involves creating a human-readable and serializable summary of the model in JSON format, converting it into a Python dictionary for further manipulation or storage, and then using the Python color package to serialize the dictionary into a compact CBOR representation. Figure 2: Dual attestation method: ensure device and model trust through PSA and ML-EAT tokens. Due to the lack of standardization, token claim labels can be assigned to IANA private claim keys or encoded as text strings. While mapping general claims to predefined identifiers may be straightforward, the situation becomes more complex for the model architecture subclaims. This is because the model architecture can vary significantly across different machine learning frameworks, each supporting multiple layer types and configurations. In Section 7, we assess both encoding methods. Analogously to the EAT or PSA standards, converting claim labels into integer keys results in a more compact form and ensures cross-device compatibility. Nonetheless, for ML models, claims encoded as strings might be more effective since they naturally match the JSON or other formats frequently
# A New BDS-2 Satellite Clock Bias Prediction Algorithm with an Improved Exponential Smoothing Method
## 2 Theory of Improved Exponential Smoothing Method Prediction
### Optimize the Smoothing Coefficient Using the \"TNTF\" Principle  
The TNTF principle means that the trend of clock bias over the future period depends more on the recent development law in the historical clock bias, while the correlation of the development trend between long-term historical and the future clock bias is weak. Principle TNTF has a variety of specific implementation methods, the method adopted in this contribution is to introduce a fitting error weight coefficient \(\beta\) (varies from 0 to 1), and the fitting error weight coefficient \(\beta\) is divided into 9 equal parts (0.1, 0.2,... 0.9). The fitting errors of the model are determined by employing the weighted mean absolute percentage error (WMAPE) as follows [38].  
\[WMAPE=\frac{1}{n}\cdot\sum_{t=1}^{n}\frac{\left|\hat{X}_{t}-X_{t}\right|\cdot \beta^{n-t}}{X_{t}} \tag{11}\]  
where the value of \(\beta\) represents the degree of TNTF principle; \(\hat{X}_{t}\) is the analog value of the clock bias; \(X_{t}\) is the observed value of the clock bias; \(n\) is the number of the clock bias participating in modeling.  
The most commonly used methods to search for the optimal smoothing coefficient \(\alpha\) include the theoretical method and the empirical approach. In this contribution, the equipartition method is adopted. Compared with other methods, the equipartition method has the advantages of simplicity and intuition. The search process of \(\alpha\) is as follows.  
(1) Values assigned to \(\beta=0.1,0.2,\cdots,0.9\), respectively.  
2. Divide \(\alpha\) (\(0<\alpha<1\)) into 999 parts evenly, namely \(\alpha_{i}=0.001\cdot i,i=1,2,\cdots,999\) (\(\alpha_{1}=0.001,\alpha_{2}=0.002,\cdots,\alpha_{999}=0.999\)).
3 We substitute \(\alpha_{i}\) into Equations (3), (6), and (7) in turn, and then the corresponding \(S_{t}^{\prime}\), \(S_{t}^{\prime\prime}\), and \(S_{t}^{\prime\prime\prime}\) can be calculated by combining Equation (10).
4 The analog values of the ES method can be obtained by substituting the calculated \(S_{t}^{\prime}\), \(S_{t}^{\prime\prime}\), and \(S_{t}^{\prime\prime\prime}\) into Equations (8) and (9), respectively.
5 By substituting the analog values of the calculated clock bias and the actual observed values of the clock bias and the given \(\beta\) into Equation (11), the values WMAPE corresponding to a set of \(\alpha_{i}\) can be obtained.
6 Repeat operation steps from (2) to (5) for each given \(\beta\) to obtain \(9\times 999\) corresponding WMAPE, and then, compare these values. The \(\alpha_{i}\) corresponding to the smallest WMAPE is the optimal smoothing coefficient searched out. The flow of the specific optimization process is depicted in Figure 1.
# Regeneration of Articular Cartilage Using Membranes of Polyester Scaffolds in a Rabbit Model
## 4 Discussion
Moreover, magnetic resonance imaging (MRI) should be considered when assessing articular cartilage. MRI is relatively non-invasive and can be performed at multiple time points in the same animal, thus enabling long-term follow-up assessments. MRI can show tissue overgrowth and bone edema, which are common complications of cell therapy procedures. Current imaging techniques may indirectly suggest hyaline cartilage formation, but these images are not always directly related to the histological findings [71; 72]. The animal model is different from the clinical situation in humans. Humans are treated sometime after the defect develops, whereas, in our study, healthy animal joints were treated immediately after the defect developed. Cartilage regeneration was also affected by the fact that the animals fully loaded the limb immediately after surgery, which is the opposite of how humans recover. Hence, the evaluation of regeneration in humans in the future may be different. Our work was carried out in accordance with the 3 R-rule (reduction, replacement, refinement). For this reason, we created two defects in one knee joint--one in the medial condyle and one in the lateral condyle, symmetrically (Figure 3). In groups I and II, in each joint, the regenerated defect from the lateral condyle was subjected to a microanalytical examination, the regenerated defect from the medial condyle was subjected to a histopathological examination (Figure 4). In the control group, both defects in each joint were subjected to histopathological examination. The defects in the control group in the same joints differed in terms of assessment, as did the defects in the two different joints. This means that even though the defects were located in the same joint, the regeneration process was different. For this reason, we treated these regenerates as separate statistically accountable values. We believe that since the process of regeneration of two defects in one joint differs, this process is influenced not only by the individual characteristics, the site of the defect (one defect on one condyle symmetrically), and its size (the same in each joint), but also by means that have not yet been recognized. The mechanics themselves and the way rabbits load the joints probably play a major role. According to the International Cartilage Repair Society (ICRS) recommended guidelines for histological endpoints for cartilage repair studies in animal models and clinical trials, we were looking for any implanted (foreign) material. During preparation of the samples, we did not find any pieces of membranes; moreover, we found no evident residues of previously implanted material in any of the histopathological samples. Based on such results and the GPC examination (Table 1), we assume that this is because the entire polyester has been degraded into the form of very short polymer chains that will be easily and completely degraded in a short time period into the products that can be resorbed by the body.
## 2 Materials and Methods
### Study Procedure
#### 2.2.1 Measurements of Anthropometric Parameters and Cardiometabolic Risk Factors  
Height was measured using an extensometer (Sanwa, South Korea), and body composition was measured using an Inbody 720 (Biospace, Seoul, South Korea) machine utilizing the bioelectric impedance analysis (BIA) method. Body weight (kg), BMI (kg/m\({}^{2}\)), fat mass (kg), body fat (%), and fat-free mass (kg) were measured and obtained from BIA method. Skeletal muscle mass corrected by height (kg/m\({}^{2}\)) was used in this study [26,27]. Blood pressure was evaluated using Accuniq BP850 (Jawon, Seoul, South Korea). Participants measured blood pressure twice in a stable state, and the average of values was used in the analysis. For blood analysis, participants were seated in a chair and wiped their index fingers with alcohol cotton to draw whole blood. After placing the capillaries horizontally, 35 \(\mu\)L of whole blood was collected from index finger of each participant. Total cholesterol (Total-C), LDL-C, high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), and fasting glucose (FG) were measured through Cholesterol LDX system (Alere, Oslo, Norway), and hemoglobin A1c (HbA1c) was measured using Afinion AS100 Analyzer (Alere, Oslo, Norway). Anthropometric parameters and cardiometabolic risk factors of all participants were presented in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variables (Unit)** & **Mean \(\pm\) SEM** \\ \hline Age (year) & 23.9 \(\pm\) 0.5 \\ Height (cm) & 176.3 \(\pm\) 1.3 \\ Weight (kg) & 77.7 \(\pm\) 2.0 \\ Body fat (\%) & 16.6 \(\pm\) 1.0 \\ Muscle Mass (kg) & 37.1 \(\pm\) 4.5 \\ Muscle Mass (kg/m\({}^{2}\)) & 11.9 \(\pm\) 0.2 \\ Hand Grip Strength (kg) & 50.30\(\pm\) 0.8 \\ Hand Grip Strength (kg/m\({}^{2}\)) & 16.2 \(\pm\) 0.4 \\ Peak Anaerobic Power (W) & 918.4 \(\pm\) 25.1 \\ Average Anaerobic Power (W) & 634.2 \(\pm\) 20.2 \\ Minimum Anaerobic Power (W) & 395.0 \(\pm\) 21.7 \\ HR\({}_{\text{rest}}\) (beats min\({}^{-1}\)) & 60.3 \(\pm\) 2.5 \\ AlX (\%) & 4.0 \(\pm\) 1.4 \\ AlX/975 (\%) & \(-\)3.1 \(\pm\) 3.0 \\ baPWV (cm/s) & 1173.5 \(\pm\) 30.4 \\ Weekly engaged exercise times (times/week) & 4.4 \(\pm\) 0.2 \\ \hline \hline \end{tabular} Values express means \(\pm\) SEM. Alx, augmentation index; Alx@75, augmentation index corrected for heart rate 75; baPWV, brachial-ankle pulse wave velocity; HR\({}_{\text{rest}}\), heart rate at rest.  
\end{table}
Table 1: Basic clinical characteristics of participants.
# A Double Rate Localization Algorithm with One Anchor for Multi-Hop Underwater Acoustic Networks
## 4 Performance Analysis and Simulation Results
### The Relationship between Code-Width and Transmission Range  
According to the nodes' property and the environment characteristic, we set \(\mathrm{SL}=134\) dB, \(\mathrm{NL}=70\) dB, \(\mathrm{SNR}=4\) dB, the carrier frequency \(\mathrm{fs}=10\) KHz.  
If we increase the bit duration \(T_{s}\) from \(1\) ms to \(25\) ms, the transmission range changes as in Figure 3.  
In Figure 4 we can see that the transmission range increases along with the increase of code-width. When the code-width equals 1 ms, the transmission range is about 400 m. While the code-width changes to 11 ms, the transmission range increases to about 1100 m. Hence, we can choose the proper code-width of signal from the line in Figure 3 for HM and LM transmission modes receptively to make the distance measurement of multi-hop networks more accurate.  
Figure 3: The localization process.