esa-sceva/satcom-chunk-collection huggingface dataset:
# A Statistical Approach for Modeling Individual Vertical Walking Forces
## 3 Walking Force Models  
The approach proposed in this paper is based on the modeling of the individual walking force as a series of moving footsteps. To account for the intra-subject variability of the walking force, the gait parameters of the single foot force are defined as random variables. Walking parameters of the given step are assumed as independent of those of the previous, and they are randomly extracted from the multivariate distributions described in Section 4. On the one hand, this approach is able to grasp the individual variability of the gait, since the steps of the same person are never identical to each other. On the other hand, the proposed model is memoryless, such that it is not able to describe particular event as a stumble or a collision. This approach takes inspiration from the well-known application of Markov chains called \"random walk\" [30]. In practice, the position of the agent is given by a sequence of steps, where the position of each step depends only on the position of the previous step. In the classical random walk, the step direction is uniformly distributed between \([0,2\pi)\), and the step size depends on the specific phenomenon under investigation. However, in our scenario, the pedestrian is walking towards a given endpoint, so its direction cannot be uniformly distributed.  
The force model proposed in this paper is thoroughly described as follows (Section 3.2). For comparison purposes, a simplified model is also defined (Section 3.1), which differs from the proposed one by the definition of the single foot force. In the proposed model, the typical M shape of the vertical foot force is assumed, while in the simplified model the step force is supposed to be constant over the step duration.  
Figure 2: Experimental forces of the right (blue line) and left (red line) foot and continuous walking force (dashed black line).
## 4 Experiments and Results
### Evaluation Metric  
We use the mean intersection-over-union (mIoU) as the evaluation metric, which is commonly used in semantic segmentation tasks. In our experiments, we calculate the ratio of the intersection and the union of the two sets of the true pixel-by-pixel classification results and the predicted pixel-by-pixel classification results. The ratio indicates the accuracy of the segmentation results. The computation is explained in Formula (10).  
\[mIoU=\frac{1}{k+1}\sum_{i=0}^{k}\frac{p_{ij}}{\sum_{j=0}^{k}p_{ij}+\sum_{j=0}^{ k}p_{ji}-p_{ii}} \tag{10}\]  
where \(k\) represents the number of categories of the prediction task (excluding the background), \(p_{ij}\) represents the pixel whose true value is \(i\) and is predicted to be \(j\), and \(p_{ii}\) is the correctly predicted pixel. \(p_{ij}\) and \(p_{ii}\) represent the pixels predicted to be false positives and false negatives, respectively.
# Feature Selection and Transfer Learning for Alzheimer's Disease Clinical Diagnosis
## 3 Methods
### Transfer Learning Method for Alzheimer's Disease Clinical Diagnosis  
To solve the problem that happened in Alzheimer's Disease clinical diagnosis that is caused by the training and testing data drawn from different distributions, we try to propose a transfer learning method for diagnosis by utilizing important weights. In our method, we first assign a weight for each image from training dataset. Then we try to adjust the weights to filter the images with the large difference from the testing data distribution. In this way, we can train a suitable model on the re-weighted data for the diagnostic tasks from testing data.  
Specifically, we let \(w_{i}\) indicates the importance weights for the \(i\)-th image. Then, we can use a weight vector \(W\) = (\(w_{1}\), \(w_{2}\),..., \(w_{n+m}\)) to indicate the weights for training data. When considering the fact that the training dataset may include few labeled data from target domain, we use weights \(w_{1}\),..., \(w_{n}\) to indicates weights for the source domain, and use weights \(w_{n+1}\),..., \(w_{n+m}\) for the target domain.  
To introduce the used transfer learning method, we present the TrAdaboost method in detail. In TrAdaboost, to obtain a suitable images weights, we initialize weight vector \(W\) by \(W^{1}=(w_{1}^{1},...,w_{n+m}^{1})\), and  
\[w_{i}^{1}=\left\{\begin{array}{ll}1/n&i=1,...,n\\ 1/m&i=n+1,...,n+m\end{array}\right. \tag{4}\]  
In the first step, weight vector \(W^{1}\) is used to re-sample the images from training dataset. In the second step, we utilize traditional method to learn a base model, \(h_{1}\) with re-sampled dataset. After we obtain a base model, in the third step, we calculate the error \(e_{1}\) of \(h_{1}\) on \(S\),  
\[e_{t}=\sum_{i=n+1}^{n+m}\frac{w_{i}^{t}\cdot|h_{t}(x_{i})-c(x_{i})|}{\sum \limits_{i=n+1}^{n+m}w_{i}^{t}} \tag{5}\]  
where, \(c(x_{i})\) indicates real label of image \(x_{i}\). To filter images with a large difference from testing data distribution, in the fourth step, the weight vector is updated, as following,  
\[w_{i}^{t+1}=\left\{\begin{array}{ll}w_{i}^{t}\delta^{|h_{t}(x_{i})-c(x_{i})|},&1\leq i\leq n\\ w_{i}^{t}\delta^{-|h_{t}(x_{i})-c(x_{i})|},&n+1\leq i\leq n+m\end{array}\right. \tag{6}\]  
where \(\beta_{1}=e_{1}/(1-e_{1})\). Then new weight vector is used to repeat step 1 to step 4 until change of \(e\) is less than threshold \(\varepsilon\).  
For a test image \(x\), the output hypothesis is:  
\[h_{f}(x)=\left\{\begin{array}{ll}1,&\prod_{t=N/2}^{N}\beta_{t}^{-h_{t}(x)} \geq\prod_{t=N/2}^{N}\beta_{t}^{-1/2}\\ 0,&\text{otherwise}\end{array}\right. \tag{7}\]  
where \(N\) indicates the maximum number of iterations.
## 4 Atmospheric Correction Results and Analysis
### Validation of Reflectance Through In Situ Measurement Comparison
By applying the spectral response function (\(a(\lambda)\)) of the MSC, the surface reflectance spectrum (\(\rho(\lambda)\)) measured in the field was converted into band-equivalent reflectance (\(\rho_{band}\)), as shown in the following equation:  
\[\rho_{band}=\frac{\int a(\lambda)\rho(\lambda)d\lambda}{\int a(\lambda)d\lambda} \tag{12}\]  
Here, \(\rho_{band}\) represents the band-equivalent reflectance, ensuring compatibility between in situ measurements and satellite-derived reflectance. The band-equivalent reflectance was compared with the atmospheric-corrected surface reflectance (ACSR), and the results are presented in Figure 14. The figure highlights significant differences between the Top-of-Atmosphere (TOA) reflectance and the in situ measured band-equivalent reflectance for various surface types, including the Dunhuang site, high-reflectance site, vegetation, and water bodies. Notably, the TOA reflectance in the blue band is substantially higher than the in situ measured reflectance, and the overall curve shape shows inconsistency. However, after atmospheric correction, the surface reflectance aligns closely with the in situ measured band-equivalent reflectance, demonstrating the effectiveness of the correction process. The atmospheric-corrected surface reflectance derived from the HJ-2A/B satellite's MSC was compared with the in situ measured reflectance for the Dunhuang site, high-reflectance site, wheat field, and river water, which were treated as ground truth values. The calculated deviations are presented in Table 3. For these four surface types, larger reflectance deviations between the ACSR and the measured reflectance were observed in band 1 and band 2, which correspond to the blue and green bands with relatively  
Figure 14: Comparison chart of ground-measured reflectance and atmospheric-corrected SR. (**a**) Dunhuang site (25 January 2021, HJ-2B). (**b**) High-reflectance site (25 January 2021, HJ-2B). (**c**) Wheat field (25 March 2021, HJ-2A). (**d**) River water (25 March 2021, HJ-2A). shorter wavelengths, making them more susceptible to atmospheric influence. The largest reflectance deviation occurred in band 5 for the wheat field, which may be related to the higher aerosol optical depth on that day. However, overall, the deviations between the ACSR and the measured reflectance were mostly below 0.02, with the maximum deviation not exceeding 0.03. These results confirm the effectiveness of the correction in retrieving accurate surface reflectance values for diverse land cover types.
# Application of USCCD on Girth Weld Defect Detection of Oil Pipelines
## 3 USCCD Pull Test Results for GW Circumferential Crack-Like Defects  
### POD (Probability of Detection) and POI (Probability of Identification)  
For determination of POD, 102 defects with open width of 0.8 mm were taken into account. The test defects were categorized with respect to their position relative to the weld into two groups: defects in weld and those at weld. It was observed that the defect signals in weld and at weld show a different behavior regarding their amplitudes. This behavior was considered in the derivation of the depth sizing models for the different defect groups, respectively. Therefore, the POD, POI and sizing accuracies are calculated individually for each group of defects.  
From the 36 defects present in weld, 35 were detected, which corresponds to a detection rate of 97%. For the defects present at weld, the detection rate is 92% (61 defects were detected from 66). See Table 1.  
All of the six defects that were missed are 1 mm deep, in which four are external. Hence, shallow cracks are more difficult to detect by USCCD. Because of the irregular shape of weld, external defects at weld tend to be a little more difficult to figure out.  
At this stage of development, reliable guidelines for circumferential crack detection are not established to the extent to be able to distinguish between different types of defects in the pipeline (i.e., cracks and notches have similar reflection characteristics). Therefore, a \"linear indication\" was defined as a reportable defect type for circumferential crack detection.  
All detected defects were classified as linear indications. The radial position (interior or exterior) was correctly classified for all defects detected in the test data.
## 3 Results and Discussion
### Study of Emulsions' Stability  
Emulsions are known to be thermodynamically unstable due to phase separation, leading to loss of functionality of the system [41]. The instability of emulsions can be explained by migration phenomena (creaming and sedimentation) and size variations. The creaming and sedimentation can be generated by gravitational or centrifugal forces. In terms of droplet size variation, different mechanisms such as flocculation, coalescence, and Oswald ripening were described in the literature [33]. In this work, the stability of the emulsions was assessed by following the changes in droplet size, pH, sedimentation index (SI), and creaming index (CRI) during the 14 days of storage at different temperatures. Table 2 represents the data of the droplet size and the polydispersity index of the emulsions after storage at 5, 20, and 40 \({}^{\circ}\)C for 14 days. The temperature of 40 \({}^{\circ}\)C for the storage of the emulsion was chosen for the accelerated stability testing. When comparing the characteristics of freshly prepared emulsions (Table 1) and those after 14 days of storage (Table 2), the droplet size of all emulsions increased during the storage period. Furthermore, storage conditions had an impact on changes in droplet size. When the emulsions were kept at a temperature of 5 \({}^{\circ}\)C, they were more stable. For instance, the droplet size of the EM-CL-10 emulsion was 463, 628, and 891 nm when the samples were stored at temperatures of 5, 20, and 40 \({}^{\circ}\)C, respectively. Figure 1 shows the droplet size of the emulsions during different time intervals (0, 2, 7, 10, and 14 days) when the samples were kept at a temperature of 5 \({}^{\circ}\)C. The droplet size of all emulsions gradually increased; however, samples containing 2.5% of essential oil or active components were more stable compared to those with the higher concentration of bioactive compounds. According to the literature, when the droplet size of the emulsion does not change significantly during a period of observation, it is considered that the emulsion system is stable [42]. In addition, prepared emulsions with droplets of smaller size usually remain quite stable for a certain time. This is explained by the fact that, with a smaller droplet size, destabilization processes such as coalescence or flocculation of droplets occur more slowly [34]. Regarding long-term stability, various strategies could be used to increase emulsion stability. For instance, thickening agents and stabilizers provide additional viscosity and steric stabilization, respectively. Also, the addition of electrolytes or salts can improve stability by increasing the repulsive forces between the droplets [33; 41].
# Direct Selective Oxidation of Hydrogen Sulfide: Laboratory, Pilot and Industrial Tests
## 7 PROClaus Process  
In the proposed concept of the modification of the Claus process, the first and second stages are standard: the combustion of hydrogen sulfide of acid gas in a flare furnace with further catalytic conversion of a mixture of hydrogen sulfide and sulfur dioxide in the first catalytic reactor. The main distinguishing feature of the PROClaus process is the use of a specially developed catalyst for sulfur dioxide reduction comprising the oxides of Fe, Co, Ni, Cr, Mo, Mn, Se, Cu, and Zn, in the presence of which, in a temperature range of 200-380 \({}^{\circ}\)C, there is almost complete sulfur dioxide conversion into elemental sulfur. Additional reducing agents are the products of side reactions proceeding in the high-temperature Claus furnace, i.e., CO and H\({}_{2}\)[33; 34; 35].  
Furthermore, sulfur is separated from the gas stream containing hydrogen sulfide as the primary reagent, and the gas flows to the direct catalytic oxidation reactor which is filled with a Hi-Activity catalyst [36]. The Hi-Activity catalyst is a modified form of the KS-1 catalyst previously developed in the Azerbaijan Institute of Oil and Chemistry containing iron, zinc, and chromium oxides as the main components [37].  
Figure 3: COPE® Technology (Kingswinford, UK) flow sheet diagram (adapted from [28]).  
The calculated value of the total extracted sulfur from the gas is 99.2% using the three-reactor scheme and 99.5% for the four-reactor one. There characteristics were confirmed in laboratory studies of the concept of the process. However, the tests at the industrial level ended unsuccessfully: the hypothetical level of sulfur extraction was not observed, as the sulfur dioxide reduction catalyst did not achieve the proposed rate of SO2 conversion into elementary sulfur. According to [PERSON] [38], in the process of laboratory studies, a factor of inhibition of the catalyst activity by sulfur vapor coming with the gas flow after the condenser of the first catalytic stage was not taken into account.  
At the same time, according to the company JACOBS [39], the EUROCLAUS process was implemented on an industrial scale using the concept of catalytic reduction of SO2 with the subsequent oxidation of hydrogen sulfide to elementary sulfur. In the EUROCLAUS process, an additional bed of the reduction catalyst is loaded into the Claus catalytic converter.
## 5 Conclusions  
In this paper, we proposed a new method of storage reliability for AEMR based on belief reliability theory. Under the framework of the theory of assured reliability, the method firstly establishes a margin degradation model to describe the relationship between the underlying parameters and the performance degradation characterization parameters by analyzing the AEMR storage degradation failure mechanism. Then, based on uncertainty theory, uncertainties such as product initial state dispersion, individual heterogeneity, and cognitive incompleteness are quantified to construct the belief reliability model. Finally, in the process of the storage reliability solution, multiple sources of information, such as manufacturing process data, degradation simulation data, and test failure data of AEMR, are used to estimate the model parameters to make the assessment results more objective.  
A case study was used to verify the validity and applicability of the method, and the following conclusions were reached.  
1. With the full use of multiple sources of information, a more accurate result can be obtained by using only five sets of failure data samples for the assessment, significantly reducing the assessment cost. The accuracy of the results is improved, and the MAE is reduced by 29.3% when compared to the evaluation method using only simulation data.
2. By measuring the influence of uncertainties in different model parameters, it was clarified, on the one hand, that the main sensitive factor affecting the storage re-liability of batches is the initial release time, which provides a direction for optimization in the product design phase. On the other hand, the importance of the uncertainty of the failure threshold, for the accuracy of the storage reliability assessment, is pointed out. When the threshold's uncertainty is considered, the assessment results are more accurate.  
As mentioned above, the method proposed in this paper offers a new solution to the problem that AEMRs in the design phase cannot carry out a valid reliability assessment without sufficient degradation data. It can also provide a clear direction for product optimization based on uncertainty analysis. It is also a potential reference for the reliability assessment of other electrical products of the same type.  
Methodology, writing-original draft, visualization, supervision, Q.L.; Conceptualization, methodology, validation, Y.L.; formal analysis, writing--review & editing, S.W. ([PERSON]); investigation, data curation, software, S.W. ([PERSON]); writing--review & editing, resources, project administration, funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Plan Project of Wenzhou Municipal Science and Technology Grant G20210030, and in part by Graduate Scientific Research Foundation of Wenzhou University.
## 3 Results of Magnetic Measurements and Discussion  
### DC Properties  
Quasi-static measurements of the sample presented in Figure 6 shows comparable DC magnetic properties to commercial and 3D printed soft ferromagnetic materials from the literature. The main drawback of the material characterized in this study is the relatively low saturation magnetization of 1.3 T (at 5000 A/m), compared to other commercial grade Non-Oriented Electrical Steels according to Standard EN 10106 of 1.6-1.7 T (at 5000 A/m). The reduced saturation magnetization can most likely be attributed to the pinning effect of intragranular porosities [25; 26] on the large recrystallized grains.  
For SLM-fabricated soft magnetic materials in the literature, DC relative permeability in the range of 25,000-31,000 [11; 12] and coercivity of 16 A/m [11; 12] for printed 6.7-6.9% silicon steel has been obtained. For the material characterized in this study, the slightly lower maximum permeability of  
Figure 5: (**a**) Toroid dimensions and the schematic of the hysteresis measurements. (**b**) Geometry and mesh of the finite element method (FEM) simulation.  
Figure 6: Quasi-static hysteresis curve and permeability of the toroid (annealed). Measured with sinusoidal excitation of 2500 A/m at 25 mHz.  
21,500 (at 2500 A/m) and higher coercivity of 49 A/m can most likely be attributed to impurity content (as described by [PERSON] in 1980 [27]) in the printed material (defect, chromium and carbon content). DC losses expressing the total hysteresis losses of the material printed in this study were calculated at 0.95 W/kg (at 1 T, 50 Hz) and 2.81 W/kg (at 1.5 T, 50 Hz). As a comparison, [PERSON] [12] has previously obtained a total power loss of 2.2 W/kg (1 T, 50 Hz) on annealed printed ring samples with a cross section of \(2\times 4\) mm (including limited eddy current losses component) and [PERSON] [11] has calculated 0.7 W/kg hysteresis (1 T, 50 Hz) and approximately 6.5 W/kg (\(5\times 5\) mm ring sample cross section) of total iron losses in her work with SLM-fabricated 6.7% silicon steel. For commercial grade non-oriented electrical steel sheets, total iron losses have been shown to range from 2.2-2.5 W/kg (Top grade, 2.5-3.2% Si, thickness 0.35-0.5 mm, 1.5 T, 50 Hz) to 7-9 W/kg (Low grade, 0.5-1.5% Si, thickness 0.5-0.65 mm, 1.5 T, 50 Hz) [27; 28]. The total iron losses of 6.5% non-oriented silicon steel laminations are typically in the range of 0.5-0.7 W/kg (thickness 0.5 mm, 1 T, 50 Hz) [21] and Somaloy has been shown to exhibit total iron losses 6.6-10 W/kg (\(5\times 5\) mm cross section, 1.5T, 50 Hz) [29].
# Strain Relaxation Effect on the Peak Wavelength of Blue InGaN/GaN Multi-Quantum Well Micro-LEDs
## 4 Conclusions  
In this work, we prepared a 4-inch GaN blue LED wafer by metal-organic chemical vapor deposition (MOCVD). Then, the wafer was divided into 18 areas and each area was fabricated into micro-LED pillar arrays by ICP etching. The pillar diameter of the 18 areas changes from 3 \(\upmu\)m to 20 \(\upmu\)m. The relationship between the micro-LED pillars' strain relaxation and peak wavelength shift was researched.  
Through our PL mapping measurements, the 3 \(\upmu\)m micro-LED pillars peak wavelength blueshifts up to 3.1166 nm due to the InGaN/GaN multi-quantum well strain release at the side edge of the micro-pillar. Then, confocal Raman microscopy measurements were performed, and the existence of the pillar edge strain relaxation was verified by E2H mode Raman shift. Finally, the above results were simulated using the finite element method (FEM) in COMSOL software.  
This work shows that with the reduction of micro-LED pillar size, the influence on the peak wavelength shift of micro-LEDs increases. The smaller the device size, the more obvious the strain relaxation effect on device peak wavelength. In the future, the high resolution of micro-LED displays could allow the size of pixels and devices to be less than 10 \(\upmu\)m. More attention should be paid to the strain relaxation effect on the LED peak wavelength shift. The results of this paper provide important guidance for the design and application of micro-LED devices.  
Conceptualization, H.L. and P.N.; methodology, P.S.; investigation, F.W. and Z.C.; resources, S.L., Y.W. and D.Z.; data curation, K.G.; writing--original draft preparation, C.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Tianjin Municipal Science and Technology Bureau, Grant 18 JCYBJC85400, Grant 18 ZXCLGX00090, Grant 20 JCQNJC00180 and Grant 19 JCTJPJC48000; in part by the China Scholarship Council (CSC), Grant 201809345004; and in part by the Tianjin Key Laboratory of Optoelectronic Detection Technology and System under Grant TD13-5035 and Grant 2017 ZD06.  
Not applicable.  
The authors declare no conflict of interest.
# Quantum Authentication Evolution: Novel Approaches for Securing Quantum Key Distribution
## 5 QKD Authentication Model Setup and Details
### Compact, Moderate, and Sizable Compartments  
In our exploration of a continuous photon stream within the QKD protocol, we investigated how the authentication time for each post-quantum algorithm is affected by the size of the cryptographic key. Initially, we examined the temporal aspects associated with the authentication process of each algorithm across a range of key sizes, from 0 to 2000 bits. Subsequently, we categorized these outcomes into three distinct bins based on key size ranges. The small bin comprised key sizes between 0 and 100 bits, the medium bin covered the range from 100 to 500 bits, and the large bin encompassed key sizes from 500 to 2000 bits. This systematic categorization facilitated a detailed analysis, allowing us to compare algorithmic performance across scenarios characterized by varying key length requirements, from smaller to medium and larger keys.