esa-sceva/satcom-chunk-collection huggingface dataset:
# Arc Modeling and Kurtosis Detection of Fault with Arc in Power Distribution Networks
## 2 Experimental Work and Arc Waveform Analysis  
### Problem Statement  
Several mathematical models have been used for describing the arcs. Many models are used for circuit breaker arcs [16; 17] and several of these models have been applied to long arcs or arcing faults [18; 19]. The most popular rules depend on thermal equilibrium. Hence, the thermal model has the longest history of dynamic arc models since the introduction of the first description of arc conductivity in the form of a first order differential equation by [PERSON] 1939 and [PERSON] 1943 [1].  
Even if the thermal model increases a model's validity and reduces the computation burden, two concerns still remain for an arc model. First, given that in the previous methods the time varying arc conductance was calculated using the phase current, the variation in phase current is inevitably sensitive to the customer load. Second, phase voltage and phase current can potentially not be in-phase because there are various types of loads such as inductive loads and capacitive loads. Consequently, when the phase voltage and phase current are not in-phase, it is difficult to calculate negative arc conductance in the previous methods by using the phase current as an input of the model.
## 3 Results
### Economic Analysis
As shown in Figure 11, the annual cost of an OAR system, and project revenue, both increase with the decrease in \(AT_{sahcx}\) of SAHEXs, while the increasing tendency of \(Pr\) is larger than that of \(C_{acs}\). Thus, it can be speculated that the smallest \(DPP\) occurs in the largest \(Pr\), as well as the largest \(ESR\) working condition. Table 7 lists the investment cost of components of OAR system and conventional cooling system. Indicators for economic analysis are also listed in Table 7. The direct capital cost of an OAR system (Cdcc-oar) is USD 0.71 M while conventional cooling system costs USD 0.31 M. Thus, the net capital cost for the replacement of conventional cooling system by OAR (\(Cdcc\)-_ar-ccs_) is USD 0.40 M. Annual operation, maintenance, and management cost for the OAR system are calculated as USD 0.07 M. Based on the Cdcc-oar, an \(NPV\) of USD 5.42 M can be obtained during the life cycle of the OAR system, with an estimated 1.32 year dynamic payback period. Based on \(Cdcc\)-_ar-ccs_, the \(NPV\) is calculated as USD 5.71 M, and the \(DPP\) is less than one  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Month**} & \multirow{2}{*}{**T (\({}^{\circ}\)C)**} & \multirow{2}{*}{**\(RH\) (\%)**} & \multirow{2}{*}{**Original Electricity**} & \multicolumn{2}{c}{\(AT_{\text{sahcx}}=5\)**} & \multirow{2}{*}{**7**} & \multirow{2}{*}{**9**} & **11** & **13** & **15** & **17** & **19** & **20** \\  & & & & & & & & & & & & & \\ \hline
1 & \(-\)1.7 & 0.3 & 0.0001 & 19,872 & 7.01 & 6.  
year (only nine months). From the above economic indicators, we can deduce that the OAR heat recovery system applied in the air compression section of the CASU shows great economic potential.
## 3 Numerical Implementation
### COMSOL Implementation  
In the numerical implementation of the TM-PFM, a segregated solution strategy is employed to enhance computational efficiency. The physics modules of choice in COMSOL Multiphysics are the Heat Transfer in Solids Module, Solid Mechanics Module, State Variables, and Helmholtz Equation. The temperature field is solved within the Heat Transfer in Solids Module, while the displacement field is computed in the Solid Mechanics Module. State variables are employed to update the historical strain field during computation. Concurrently, the phase-field variable is obtained by solving the Helmholtz equation. Temporal integration employs the implicit backward difference formula (BDF). The complete solution procedure is illustrated in Figure 2.
# Open-Source Magnetometer for Characterizing Magnetic Fields in Ultracold Experiments
## 2 General Considerations
### Field Measurement with Internal Offset Cancellation
The acquisition procedure is maximally sped up if the measurements are performed in the following order:  
1. while the coil current is off, measure the sensor's response at location \(y_{i}\) along the planned measurement path for two polarizations of the sensor, \(\vec{B}_{\text{ns},+}^{i}=\vec{B}_{\text{stray}}^{i}+\vec{B}_{\text{offset}}^{i}\) and \(\vec{B}_{\text{ns},-}^{i}=-\vec{B}_{\text{stray}}^{i}+B_{\text{offset}}^{i}\),
2. turn on the coil current and repeat the measurement for the same locations \(y_{i}\) as before for two polarizations of the sensor, \(\vec{B}_{\text{ws},+}^{i}=\vec{B}_{\text{source}}^{i}+\vec{B}_{\text{stray}}^{ i}+\vec{B}_{offset}^{i}\) and \(\vec{B}_{\text{ws},-}^{i}=-(\vec{B}_{\text{source}}^{i}+\vec{B}_{\text{stray}}^{ i})+\vec{B}_{\text{offset}}^{i}\). 3. while keeping the coil current on, repeat the measurement for a new location \(y_{i+1}\), for two polarizations of the sensor, \(\vec{B}_{\text{ws},+}^{(i+1)}=\vec{B}_{\text{source}}^{(i+1)}+\vec{B}_{\text{ stray}}^{(i+1)}+\vec{B}_{\text{offset}}^{(i+1)}\) and \(\vec{B}_{\text{ws},-}^{(i+1)}=-(\vec{B}_{\text{source}}^{(i+1)}+\vec{B}_{\text{ stray}}^{(i+1)})+\vec{B}_{\text{offset}}^{(i+1)}\). 4. turn off the coil current and repeat as in step 1 but now for the new location \(x_{i+1}\). 5. move the sensor to the location \(y_{i+2}\) and cycle through steps 1 to 4 until the final point in the planned measurement path is reached. The un-primed and primed values are measured much closer in time if compared to the permanent magnet case; therefore, much faster changes in stray fields (e.g., due to running elevators creating additional magnetic field) are acceptable. The field of interest can be calculated using Equation (4).
## 3 Results
### Calibration Curves  
Linearity was observed using 5 concentrations in all ranges. The correlation coefficient \(R^{2}\) value was 0.9996-0.9998. Selected calibration curves of styrene standards 0.002-0.08 ug/mL, 0-2 ug/mL and 2-10 ug/mL as previously mentioned are presented in Figure 2 (and Figures S3 and S4 in SI).  
Data is presented from the HPLC chromatographs of styrene analytical solution used in making up concentrations for the calibration curves. Specificity was evaluated by comparing the spiked styrene solution with blank runs of methanol. A peak representing 10 ug/mL of styrene is seen at the retention time 2.1-2.3 shown in Figure 7. HPLC method was validated through the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) [34]. Linearity, range, specificity, accuracy, precision, Limit of Detection and Quantification (LOD, LOQ) are all taken into account.  
Calibration curves: A regression analysis was performed examining the linearity and fit of the two variables generated a regression equation of \(y\) = 633.113x \(\pm\) 2.529 and a correlation coefficient (\(r^{2}\)) of 0.9996 showed a linear fit. A regression analysis of variance evaluated the significance of this linear relationship of F(1,70) = 21,903.27, \(p\) < 0.05 indicating a sensible linear model. Accuracy calculated through percentage recovery of samples and its Relative Standard Deviation its Relative Standard Deviation (RSD) was found to be 98.3 \(\pm\) 3.29%.  
A regression analysis examining the linearity and fit of the two variables generated a regression equation of \(y\) = 524.81x \(\pm\) 80.51 and a correlation coefficient (\(r^{2}\)) of 0.9996 showing a linear fit. A regression analysis of variance evaluated the significance of this linear relationship of F(1,43) = 107693.31, \(p\) < 0.05 indicating a sensible linear model. Accuracy calculated through percentage recovery of samples and its Relative Standard Deviation (RSD) was found to be 100.34 \(\pm\) 1.95%. Precision evaluated by RSD for repeatability (intra-day) and intermediate precision (inter-day) gave RSD values of less than 1% indicating good precision.  
Figure 7: HPLC chromatograph of methanol solution spiked with 10 ug/mL styrene, RT 2.1–2.3.
## 2 Principles and Methodologies
### Simulation Methods for Cracks, Contraction Joints, and Degradation of Elastic Modulus
#### 2.2.2 Simulation of Contraction Joints  
Eigenvalue problems can be utilized to determine the natural frequency of undamped systems [42]:  
\[\big{(}-\omega^{2}M+K\big{)}\phi=0 \tag{9}\]  
where \(M\) is the mass matrix, \(K\) is the stiffness matrix, \(\phi\) is the eigenvector, and \(\omega\) is the natural circular frequency.  
For arch dams with contraction joints, when simulating the interaction of contraction joints using surface-to-surface contact, the system's stiffness matrix comprises two parts: the dam body stiffness matrix and the contraction joint stiffness matrix:  
\[K=K_{\text{dam}}+K_{\text{joint}} \tag{10}\]  
where \(K_{\text{dam}}\) and \(K_{\text{joint}}\) are the stiffness matrix of dam body and contraction joints, respectively.  
Defining the relationship between contraction joint contact force \(p\) and penetration \(h\):  
\[p=p(h) \tag{11}\]  
This paper employs surface-to-surface contact to simulate the interaction of contraction joints and assumes that the contact relationship of contraction joints follows a piecewise linear relationship, namely:  
\[p=\begin{cases}kh&,h<h_{0}\\ kh_{0}+10k(h-h_{0})&,h\geq h_{0}\end{cases} \tag{12}\]  
where \(k\) is the elastic modulus of the contraction joint, and \(h_{0}\) is the penetration at that point.
## 3 Related Work  
Recently, the prominent advantages of deep neural networks as well as the advancement of algorithms and deep learning models have become very attractive for viable applications thanks to their ability to automatically learn feature representations from input data without the need for human intervention. Deep neural networks and deep learning models have been able to automatically extract important features from input signals effectively, thereby improving the quality and accuracy of digital signal processing in fiber optic information systems and chaotic-modulated optical information systems. For example, the digital back-propagation through DNNs has been applied to eliminate the nonlinear effect limit in order to enhance the quality of digital signal processing in amplified fiber optic communication systems, as demonstrated by the work of [PERSON]. [PERSON] et al. [49]. Similarly, deep learning models have been effectively used to address the challenges of dispersion and nonlinearity compensation in high-speed wavelength division multiplexing (WDM) fiber optic communication systems employing multilevel modulation channels like 64 QAM, as demonstrated in [50; 51; 52; 53; 54; 55]  
For chaotic communication systems, recently, a multi-carrier chaos shift keying (DL-IM-MCDCSK) system utilizing deep learning (DL) and index mapping (IM) techniques to mitigate the information leakage risk associated with conventional MC-DCSK systems has been proposed [56]. The proposed system operates without a reference signal and utilizes a two-dimensional reshaping (TDR) index mapping structure to equalize the chaotic signals in both frequency and time domains. The offline-trained DNN classifier can significantly improve the bit error rate (BER) performance during information recovery without requiring conventional maximum likelihood estimation (MLE). In addition, a chaos synchronization that does not require hardware implementations [57] or reference chaotic sequences [58] can be achieved via deep learning models to provide high-level physical layer security for optical communications. For another actual implementation, very recently, a high-speed chaotic receiver with up to 32 Gb/s messages hidden in a wideband chaotic optical carrier has been experimentally demonstrated over a 20 km fiber link, showing a significant simplification while still guaranteeing security [59]. These promising potentials prove that both deep neural networks and deep learning models are effective and viable performance quality enhancements for solving signal detection and signal processing problems in chaotic secure communication systems as well as in high bit rate optical fiber communication systems.
# Secure Encapsulation Schemes Using Key Recovery System in IoMT Environments
## 3 System Model
### Common Proposed Key Recovery System Model
#### 3.1.2 Common Objects of Proposed Schemes  
The composition of the two system models proposed in this study can be seen in Figure 1. In Figure 1, the difference between M-KRA and KRA methods is shown for the types of participants in the two models. The remaining differences are detailed in each model's respective section.  
* Key Generation Center (_KGC_): Every participant _Part_ must perform the _KCG_ and key generation and communication steps to generate keys. All _Part_ can generate a private key through the private key generation step with _KGC_, and a public key corresponding to the private key can be generated. The _KGC_ publishes the public parameter _params_ for performing encrypted communication with _Part_.
* Devices (_Dev_): _Dev_ are medical devices and monitoring devices. Devices perform communication in the system managed by the _Med_. In this model, _Dev_s must perform communication in the format designated by _Med_, and the basic format follows the form of \((C\parallel KRF)\), in which the ciphertext and _KRF_ are concatenated. Devices participating in the communication need _Med_'s public parameters in order to make the session key used for message encryption into _KRF_. Furthermore, the generated _KRF_ should be designed to only be controlled by _KRC_ and _KRA_.
* Medical Institution (_Med_): _Med_ is a medical institution that manages device authorization control and data on medical devices. When a device requests _KRF_ key recovery, the _Med_ verifies that it is the lawful owner of the _KRF_. In this paper, the step of confirming whether the _KRF_ is a lawful owner is omitted. In addition, the _Med_ sends the _KRF_ to _KRC_ to help recover the key.
## 3 Results and Discussion
### Thermal Stability Characteristics
Closed symbols represent in-plane uniaxial magnetic anisotropy energy derived from experimentally measured data for \(M_{\text{s}}\) and \(H_{\text{k}}\), and the solid lines are computed based on the temperature changes in \(M_{\text{s}}\) and \(H_{\text{k}}\); (**b**) normalized thermal stability factor. Closed symbols represent normalized thermal stability factor derived from experimentally measured data for \(M_{\text{s}}\) and \(H_{\text{k}}\), and the solid lines are computed based on the temperature changes in \(M_{\text{s}}\) and \(H_{\text{k}}\). the temperature changes in \(M_{\rm s}\) and \(H_{\rm k}\). The temperature-dependent changes in the thermal stability factor show that the samples with Ta buffer and capping layers decreased by 43.2, 66.1, and 74.2% at 400, 500, and 550 K, respectively, compared with room temperature. Furthermore, those of the samples with W buffer and capping layers decreased by 40.3, 61.8, and 69.4% at 400, 500, and 550 K, respectively. These results indicate that the sample with Ta buffer and capping layers exhibited a larger temperature-dependent change in the thermal stability factor compared to sample with W buffer and capping layers. The normalized thermal stability factor enables a comparison of the thermal stability factors at various temperatures. In conclusion, the thermal stability factor of the samples with W buffer and capping layers was less sensitive to temperature compared with that of the sample with Ta buffer and capping layers.
# Application of an Electronic Nose as a New Technology for Rapid Detection of Adulteration in Honey
## 3 Results and Discussion
### Data Processing
#### 3.2.2 Machine Learning Classification
Therefore, in the case of analysis of data from Table S1, to determine sensitivity, the denominator of Equation (2) was 42 (6 samples of authentic honey \(\times\) 7 measurements), while for the specificity calculation, the denominator of Equation (3) was 77 (11 samples of adulterated honey \(\times\) 7 measurements). In contrast, for evaluation of the sensitivity and specificity of the average data (Table 1), the denominator of the corresponding equations was 6 and 11, respectively. Considering the classifiers IBK (k = 3, the accuracy of 97.5%), Kstar (k = 3, accuracy of 96.6%), IBK\({}^{*}\) (k = 3, the accuracy of 94.1%), and Kstar\({}^{*}\) (k = 3, accuracy of 100.0%), confusion matrices, sensitivity, and specificity were calculated and are summarized in Figure 5. For IBK (k = 3), the sensitivity was 90.5%, indicating the system could distinguish 38 of the 42 data labeled as authentic honey, attributing 4 measures as adulterated. In the same way, the specificity was 94.8%, which indicates that the sensor discriminated 73 of the 77 measurements as adulterated honey and 4 erroneously as authentic honey. For Kstar (k = 3), the sensitivity was 95.2% and the specificity was 97.4%; in other words, from the total known data, the classifier could discriminate 40 and 75 measures correctly as authentic and adulterated honey, respectively. When the average data were considered, IBK\({}^{*}\) (k = 3) and Kstar\({}^{*}\) (k = 3) both correctly attributed all samples concerning authentic honey, with a sensitivity of 100%. Regarding specificity, IBK\({}^{*}\) (k = 3) erroneously classified 1 of 10 measures (90.9%), while Kstar\({}^{*}\) did not miss any sample labeled as adulterated honey (100%). Figure 4: Comparison of accuracy obtained for different classifiers for discrimination between authentic and adulterated honey using individual cycle data (Table S1) and different values of k for the cross-validation (3, 5, and 10). \({}^{*}\) Classifiers using average sample data (Table 2).
## 4 Analysis of Specimen Fracture Morphology and Energy Consumption
### Energy Consumption Analysis of Specimens  
The energy change determined using the SHPB impact compression test is shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Inside**} & \multicolumn{8}{c}{**Size of Screen Mesh/mm**} & \multirow{2}{*}{**Gross Mass/g**} & \multirow{2}{*}{**Average Particle Size of Fragments/mm**} \\
**Diameter** & & & & & & & & & & & \\
**/mm** & **0** & **0.15** & **0.3** & **0.6** & **1.18** & **2.36** & **4.75** & **9.5** & **13.2** & & \\ \hline
0 & 2.88 & 3.28 & 5.95 & 4.93 & 5.95 & 7.28 & 18.67 & 21.44 & 52.49 & 122.87 & 8.26 \\
5 & 2.08 & 3.56 & 3.15 & 3.74 & 4.80 & 8.44 & 22.11 & 24.82 & 47.95 & 120.65 & 8.16 \\
10 & 2.65 & 2.68 & 3.52 & 7.57 & 3.45 & 5.98 & 21.99 & 23.01 & 45.96 & 116.81 & 7.76 \\
15 & 2.77 & 2.91 & 4.23 & 4.56 & 4.85 & 7.75 & 21.71 & 22.88 & 41.17 & 112.83 & 7.26 \\
20 & 2.62 & 4.25 & 4.74 & 6.71 & 4.20 & 8.66 & 16.27 & 18.51 & 35.40 & 101.36 & 6.12 \\
25 & 3.57 & 7.70 & 9.71 & 7.28 & 6.67 & 3.74 & 9.04 & 15.64 & 28.37 & 91.72 & 4.81 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Impact of different inner diameter specimen fragments in terms of sieve fraction following dry and wet circulation.  
Figure 7: Relationship between the average particle size and the inner diameter of the specimen.  
The dissipation energy per unit volume varies with the inner diameter size, as shown in Figure 8.  
From Table 4 and Figure 8, it can be seen that with the increase in the inner diameter of the sandstone ring specimen, the average absorption energy decreased, and the unit volume dissipation energy was added.  
Under the impact load, the inner diameter of the sandstone ring specimen increased, the wall thickness decreased, and the volume decreased. Although the absorption energy decreased with the increase in the inner diameter, the decrease was less than the reduction in volume. Therefore, the absorption energy per unit volume increased, which directly led to an increase in the crushing degree of the specimen.
# Low-Cost Assessment Method for Existing Adjacent Beam Bridges
## 1 Introduction
Since joint damage is common in ARHB bridges, in order to avoid premature degradation of the bridge due to bearing loads higher than the designed value [13], a low-cost assessment method that can be combined with visual inspection is required to evaluate the degradation of load transverse transmission performance caused by joint damage and to arrange maintenance. When the transverse load transmission performance of the bridge deteriorates due to joint damage, the influence line of the lateral load distribution will become steep, and the lateral load distribution factor (LLDF) of the beam bearing the load will become larger. Therefore, the degradation degree of the ARHB bridge can be evaluated by variation in the LLDF due to joint damage. In China, the hinge connected beam method is the most commonly used design method for ARHB bridges to calculate lateral load distribution factors (LLDFs). By introducing the relative displacement of adjacent beams caused by joint damage, the modified method can be used for the calculation of existing ARHB bridges. Therefore, the research in this paper proposed a new evaluating indicator, namely, the lateral load distribution performance rating number \(LDN\), to evaluate the degradation degree of ARHB bridges due to joint damage. Section 2 presents the traditional hinge connected beam method and the modified method that considers joint damage, and analyzes the influence of the damaged joint at different positions on the LLDF of the beam. Section 3 illustrates the applicability of the modified hinge connected beam method on ARHB bridges with diaphragms and transverse prestressed ties by analyzing the shear transmission mechanism at grouted joints. Section 4 introduces the new assessment method and evaluating indicator of the degradation degree of transverse load transmission performance based on joint damage proposed in this paper in detail. The proposed method and indicator are exemplified through a case study, and the degradation degree calculated by the proposed indicator is compared with the degradation degree calculated by static load test results. Conclusions are drawn in Section 6.
# Evaluation of Grid-Based Rainfall Products and Water Balances over the Mekong River Basin
## 3 Methods
### Modeling Method for Hydrological Evaluation of Daily Rainfall Series  
#### 3.2.1 Model Setup  
ArcSWAT 2012, interfacing in ArcGIS 10.2, was used to simulate the hydrological properties of the MRB. The sub-basins were delineated using the SWAT watershed analysis module (watershed delineator) based on the DEM \(90\times 90\) m data and the stream network, resulting in 383 sub-basins in the MRB, with the average area of a sub-basin being around 2000 km\({}^{2}\). The sub-basins were then further divided into hydrologic response units (HRUs)--consisting of homogeneous land use, management, topographical, and soil characteristics--by the HRU module in SWAT, using the land use, soil characteristics, slope, and stream network of 2005 (Table 1). In this way, we obtained 2850 HRUs for the MRB according to five slope classifications: 0%-2%, 2%-6%, 6%-15%, 15%-25%, and \(>\)25%. Then, the basin was divided into five elevation bands (0-500 m, 500-1000 m, 1000-1500 m, 1500-2000 m, and \(>\)2000 m) to improve model performance in the runoff simulation [11; 39].  
The precipitation data needed to be imported into SWAT in the form of point data. It should be noted that the rainfall data of the station closest to the centroid of each sub-basin were used, but the number of sub-basins was much smaller than the gridded precipitation data (383 sub-basins in the MRB compared to 1689 gridded points at \(0.25^{\circ}\times 0.25^{\circ}\) resolution). Therefore, the virtual precipitation station method proposed by Ruan et al. [42] was applied to estimate the precipitation for the sub-basins by taking the average value of all of the grids within the sub-basins from the GPPs. A total of 383 virtual precipitation stations were then created, and their estimated precipitation values were provided as inputs for the SWAT model.