esa-sceva/satcom-chunk-collection huggingface dataset:
# Flaw Size Quantification for Cable Flaw Inspection System with Inductive Search Coil Sensor
###### Abstract  
Bridge cables are commonly used in bridge construction, and assessing their condition is crucial for ensuring bridge safety. However, current inspection methods often rely on large and heavy detection mechanical structures, which can be inconvenient during the inspection process. In light of these limitations, this paper proposes a portable non-destructive inspection method for bridge cables that detects the total magnetic flux variations utilizing an inductive search coil sensor. The occurrence of corrosion and cracks in bridge cables leads to changes in their original sectional area and permeability. These changes result in variations in the induced current that is excited by the coil, leading to distortions in both the magnetic field energy and the coil's self-inductance. Thus, cable damage can be detected inversely by monitoring coil self-inductance variation. To assess the feasibility of this method, a numerical analysis is conducted, and an experimental structure is designed using a mock-up cable specimen with parallel steel wires that have varying degrees of damage. The experimental results demonstrate the effectiveness of the proposed method, which utilizes an inductive search coil sensor, intuitively identifies and quantifies internal flaws on the cable in real-time while maintaining a lightweight structure. The width and amplitude of the coil inductive response show a positive correlation with the flaw's axial length and cross-sectional area, indicating that these parameters can be utilized to quantify the size of the flaw.  
non-destructive testing; flaw inspection; self-inductance; inductance measurement +
Footnote †: journal: Applied Sciences
# An Asymmetric Encryption-Based Key Distribution Method for Wireless Sensor Networks
## 3 The Key Management Scheme for Cluster-Based WSNs
### Network Initialization and Definitions  
In the network, there are \(n\) sensors, which are denoted as \(S_{0,\ldots,n-1}\), and \(m\) cluster heads (CH), which are denoted as \(\text{CH}_{0,\ldots,m-1}\). Each sensor has a unique identification code _ID_si_, which has a length of 2 bytes stored in the chip. After the initialization of the network is completed, all nodes automatically run the cluster formation algorithm (this algorithm is not discussed in this paper; for more information, please refer to [38]), which results in \(m\) clusters being formed randomly by all nodes. There is only one CH and \(n/m\) sensor in each cluster. Figure 1 shows a typical network of three clusters. Each cluster contains one CH and three sensors.  
After network deployment, each CH runs a cluster forming process, and sensors are divided into clusters with no cross coverage. After a period of operation, some sensor may move into another cluster's region. In this situation, the subsequent key distribution and update process will be performed via the CH of the present cluster. In the following section, we will describe the scheme in regard to two aspects: static sensors and mobile sensors.  
The following definitions will be used in our scheme and analysis:  
Figure 1: The network topology.  
\(SK_{i}\) denotes the symmetric session key with a length of 16 bytes shared by the base station and sensors located in \(DG_{i}\).  
_PUK_ denotes the public key of the BS, and \(PVK\) denotes the corresponding private key. _PUK_ can be obtained through public key infrastructure (PKI).  
The function \(E(x,y)\) denotes encryption (symmetric or asymmetric) operation, parameter \(x\) denotes encryption key, and parameter \(y\) denotes the plain message that needs to be encrypted. The function \(D(x,y)\) denotes decryption operation.  
_ID_CHi_ denotes the identity code of the cluster with a length of 1 byte, and it can be acquired using the CH of that cluster. It is stored in the chip of each CH, and a tamper proof mechanism is used.  
_ID_si_ denotes the identity code of sensor \(S_{i}\) up to a maximum length of 2 bytes. It is stored in the chip of each sensor, and the tamper proof mechanism is used.
# POI Recommendation Scheme Based on User Activity Patterns and Category Similarity
## 3 The Proposed POI Recommendation Scheme
### Recommendation Phase
#### 3.4.2 Rule Based Score Calculation for Inactive User Groups  
Ascertaining the preferences and interests of users in the low-activity group solely based on their data is difficult. Thus, in the proposed scheme, general users are assumed to prefer POIs that are selected by the majority in the user's movement pattern. The proposed scheme suggests POIs for a specific user if the user is determined to exhibit low activity based on various specific patterns that are extracted during the analysis phase. The recommendation score is calculated as per Equation (1). In Equation (1), \(\alpha\), \(\beta\), and \(\gamma\) are adjustment constants. w\({}_{1}\), w\({}_{2}\), and w\({}_{3}\) are the weights of each attribute and can be adjusted depending on the application. Cate\({}_{1}\) means category similarity of point i. Dist\({}_{i}\) means distance score of point i. Chck\({}_{i}\) means visit count of point i.  
\[Score_{i}=\alpha\times w_{1}\times Cate_{i}+\beta\times w_{2}\times Dist_{i}+ \gamma\times w_{3}\times Chck_{i} \tag{1}\]
## 3 Results
### Quantification of the Major Compounds in Pepper by HPLC  
Piperine is an alkaloid compound in pepper that has a significant influence on the taste and flavor of pepper [32]. Quantitative analyses shown that piperine concentrations of 8613.27 mg/100g and 5087.97 mg/100g, respectively (Table 3 and Figure 1). Moreover, piperine concentrations in BPI and BPS were determined to be 3291.65 mg/100 g and 7388.50 mg/100 g, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Color** & **Origin** & **L*** & **a*** & **b*** & **Chlorophyll a** & **Chlorophyll b** \\  & & & & & **(mg/L)** & **(mg/L)** \\ \hline \multirow{3}{*}{Green} & India & 48.21 \(\pm\) 0.04 b  & \(-\)0.95 \(\pm\) 0.02 d  & 19.16 \(\pm\) 0.01 b  & 28.97 \(\pm\) 0.09 a  \\  & Sri Lanka & 49.47 \(\pm\) 0.06 a  & 0.27 \(\pm\) 0.05 c  & 19.43 \(\pm\) 0.02 a  & 21.92 \(\pm\) 0.07 b  & 8.62 \(\pm\) 0.03 b  \\  & India & 42.03 \(\pm\) 0.01 c  & 3.44 \(\pm\) 0.01 b  & 11.43 \(\pm\) 0.01 d  & 13.68 \(\pm\) 0.01 c  & 4.08 \(\pm\) 0.03 c  \\  & Sri Lanka & 36.66 \(\pm\) 0.03 d  & 3.64 \(\pm\) 0.02 a  & 12.35 \(\pm\) 0.02 c  & 7.15 \(\pm\) 0.02 d  & 3.12 \(\pm\) 0.01 d  \\ \hline \multirow{3}{*}{Black} & India & 48.21 \(\pm\) 0.04 b  & \(-\)0.95 \(\pm\) 0.02 d  & 19.16 \(\pm\) 0.01 b  & 28.97 \(\pm\) 0.09 a  & 15.41 \(\pm\) 0.06 a  \\  & Sri Lanka & 49.47 \(\pm\) 0.06 a  & 0.27 \(\pm\) 0.05 c  & 19.43 \(\pm\) 0.02 a  & 21.92 \(\pm\) 0.07 b  & 8.62 \(\pm\) 0.03 b  \\  & India & 42.03 \(\pm\) 0.01 c [FOOTNOTE:c]Footnote c: All data are expressed as mean \(\Effect of GPEs on LPS-Induced Nitrite Production, iNOS Expression, and Cell Viability of RAW 264.7 Cells  
Abnormal production of NO is a critical marker in inflammation. RAW 264.7 cells are quite sensitive to LPS treatment, and they produce NO in response to this treatment [33]. Because the green pepper extract (GPE)s showed stronger antioxidant activities and higher concentrations of piperine, we further investigated the effect of GPI and GPS on LPS-induced NO production and the subsequent inflammatory signaling pathways in RAW264.7 cells. Using the Greiss assay, we found that GPEs significantly suppressed the LPS-induced nitrite production in a dose-dependent manner without affecting the cell viability of RAW 264.7 cells (Figure 2A,B). Because NO production is dependent on stress-mediated iNOS expression, we evaluated the effect of the GPEs on LPS-induced iNOS expression in RAW 264.7 cells. Pretreatment with GPE significantly suppressed the LPS-induced iNOS expression in RAW 264.7 cells (Figure 2C).
# Four-Channel Polarimetric-Spectral Intensity Modulation Imager
## 3 Effectiveness Validation for the FCPS Scheme  
In this section, numerical simulation is conducted to demonstrate the feasibility of the FCPS system. The simulation is based on the theory proposed in Section 2. In this simulation, the related parameters are set as follows: the spectral measurement range of the system is 470 nm to 900 nm; the thickness of retarders are 1.8 mm and 3.6 mm, and the retarder is made of quartz; and the polarization spectral resolution is 0.2 nm.
# Impact of Groundtrack Pattern of a Single Pair Mission on the Gravity Recovery Quality
## 2 Simulation Environment
### Gap Evolution and the Sub-Cycle Concept  
In [14], the sub-cycle concept is defined as follows:  
Assuming \(\Gamma=\beta/\alpha\) as the number of orbit revolutions per day, the \"Fundamental Interval\" \(S\) gives the angular space between two Ascending Node Crossing (ANX) consecutive in time  
\[S=360^{\circ}/\Gamma \tag{5}\]  
The sub-interval \(S_{i}\) is the sampling angle after an entire repeat period (RC)  
\[S_{i}=360^{\circ}/\beta \tag{6}\]  
Therefore, one can rewrite the parameter \(S\) as follows  
\[S=\alpha S_{i} \tag{7}\]  
The parameter \(\Gamma\) can also be written as  
\[\Gamma=\frac{\beta}{\alpha}=I+\frac{N}{\alpha} \tag{8}\]  
where \(I\) is the integer part of \(\Gamma\).  
Within \(S\), the ANX of days \(n\) and \(n+1\) are always separated by a distance of \(N\) or \(\alpha-N\) sub-intervals \(S_{i}\).  
Two types of orbits can be recognized: (i) drifting orbits when \(N=1\) or \(N=\alpha-1\), and (ii) skipping orbits in the other cases. In a drifting orbit, the sampling of the fundamental interval is very progressive with large unobserved gaps, while a skipping orbit, on the other hand, has a more complex coverage pattern, reducing the persistence of the large unobserved gaps quite fast. In fact, the skipping orbit owns a very wide range of spatial and temporal coverage patterns.  
The concept \"sub-cycle\" (sc) can be defined as the smallest number of days after which an Ascending Node Crossing (ANX) falls at \(S_{i}\) or \((\alpha-1)S_{i}\) from the first ANX. According to this definition, the sub-cycle of a drifting orbit is one day. The sub-cycle can be an interesting parameter to measure how fast an orbit is in reducing the largest unobserved gap [8]. Among different kinds of skipping orbits, some orbits also feature one or more pseudo sub-cycle (psc) which can also be an interesting concept to study the temporal sampling of a satellite scenario [14]. The reader is referred to [8; 14] for more details about different categories of orbits and the sub-cycle concept with the gap evolution graphs.
# Application of Machine Learning Ensemble Methods to ASTRI Mini-Array Cherenkov Event Reconstruction
## 3 Machine Learning Techniques
### Extreme Gradient Boosting  
Extreme gradient boosting (XGBoost or XGB) is a powerful Machine Learning algorithm and represents an improvement over traditional gradient boosting algorithms, which train decision trees one at a time, in a sequential manner. In contrast, XGBoost trains decision trees in parallel, using the boosting technique.  
One of the main advantages of XGBoost is that it is very fast and efficient. It is implemented using parallel processing and cache-aware algorithms, which makes it much faster than traditional gradient boosting algorithms. Additionally, XGBoost has a number of advanced features that allow it to handle a variety of data types and complexity. For example, it has support for missing values, handling of imbalanced data, and handling of high-dimensional data.
# Design of Magnetic Concrete for Inductive Power Transfer System in Rail Applications
## 3 Materials and Methods
### Transmitter Coupler Design with Magnetic Concrete  
The design of an IPT system with an LCL-S (Single Capacitance-Double-Inductance Series)-compensated topology would have Litz wires supported on ferrite bars [2]. To replace the ferrite bars with magnetic concrete would require forming a slab, as shown in Figure 5. Both coupler designs were simulated under identical system conditions using an LCL-S-compensated IPT configuration. The geometrical configuration and operating frequency of the receiver coil were kept constant to ensure a fair comparison [2].  
The design of the magnetic concrete slab is based on the dimensions of the ferrite bar coupler system and involves determining the required dimensions of the magnetic concrete slab to ensure the air space between the Litz coil and the receiver remain the same. Numerical models using ANSYS Maxwell (v.16) are then constructed to help visualize the potential magnetic flux propagation before finalizing the mix design. The magnetic concrete slab was fabricated with a slot specifically designed for the transmitter coil, as illustrated in Figure 5b. The dimensions of the magnetic concrete base used for the transmitter coil are shown in Figure 6. Both designs were tested with a consistent air gap of 12.7 cm between the transmitter and receiver coils, at an operating frequency of 85 kHz, and delivering an output power of 3 kW. The same magnetic core is used in both coupler designs 1 and 2.  
The coupling coefficient (\(k\)) was calculated to evaluate magnetic coupling efficiency between the transmitter and receiver coils using the following equation:  
\[k=\frac{M}{\sqrt{L_{1}L_{2}}} \tag{2}\]  
where \(M\) is the mutual inductance between the coils, \(L_{1}\) is the self-inductance of the transmitter coil, and \(L_{2}\) is the self-inductance of the receiver coil. This parameter allowed for a quantitative comparison of power transfer efficiency between the two designs, with higher \(k\) values indicating better coupling efficiency and more efficient power transfer.  
Figure 5: The IPT systems with the primary and receiver’s design for railway applications. (**a**) Coupler design 1 (with ferrite cores). (**b**) Coupler design 2 (with magnetic concrete).  
Figure 6: The transmitter coil embedded in a magnetic concrete base for IPT system testing.
## 2 Theoretical Solutions
### Derivation of Analytical Solutions
#### 2.2.2 Case 2: Current Flows in from one Boundary and out from the Opposite Boundary  
Figure 2b illustrate a current injected through one boundary and exiting through the opposite boundary. Equation (19) can be derived from Equations (12)-(14) and (18).  
\[u(x,\ y)=X(x)\cdot Y(y)+a_{0}x+b_{0} \tag{18}\]  
\[u(x,\ y)=\sum\limits_{n=1}^{\infty}\Bigl{[}\Bigl{\{}A_{n}\cosh\Bigl{(}\frac{ n\pi x}{b}\Bigr{)}+B_{n}\text{sinh}\Bigl{(}\frac{n\pi x}{b}\Bigr{)}\Bigr{\}} \cdot\cos\Bigl{(}\frac{n\pi y}{b}\Bigr{)}\Bigr{]}+a_{0}x+b_{0} \tag{19}\]  
The remaining boundary conditions are as follows:  
\[f(y)=\left.\frac{\partial u}{\partial x}\right|_{x=0},\ g(y)=\left.\frac{ \partial u}{\partial x}\right|_{x=a} \tag{20}\]  
Using Fourier transforms, Equations (21) and (22) provide all remaining constants, which are \(A_{n}\), \(B_{n}\), \(a_{0}\), and \(b_{0}\).  
\[\begin{array}{c}\left[\frac{n\pi}{b}\sinh\bigl{(}\frac{n\pi a}{b}\bigr{)} \right.\\ \left.\frac{n\pi}{b}\right]\cosh\bigl{(}\frac{n\pi a}{b}\bigr{)}\end{array} \cdot\begin{bmatrix}A_{n}\\ B_{n}\end{bmatrix}=\begin{bmatrix}-\frac{2}{b}\int_{0}^{b}f(y)\cdot\cos \bigl{(}\frac{n\pi y}{b}\bigr{)}dy\\ \frac{2}{b}\int_{0}^{b}g(y)\cdot\cos\bigl{(}\frac{n\pi y}{b}\bigr{)}dy\end{bmatrix} \end{array} \tag{21}\]  
\[a_{0}=-\frac{1}{b}\int_{0}^{b}f(y)dy=\frac{1}{b}\int_{0}^{b}g(y)dy,\,b_{0}=- \frac{a_{0}}{2} \tag{22}\]  
Therefore, the final solution can be obtained by substituting \(A_{n}\), \(B_{n}\), \(a_{0}\), and \(b_{0}\) in Equation (19).
## 3 The Proposed Method
### Feature Extraction Based on RWKV, V-RRVKV and BERT Module
Here, \(\mathrm{H}\times\mathrm{W}\) represents the dimensions of the image (\(224\times 224\)), N denotes the total number of patches, and C is the number of channels. Each patch is flattened into a one-dimensional vector and transformed into a high-dimensional space through a linear mapping, resulting in patch embeddings, as defined in (6). The transformation utilizes a trainable weight matrix \(\mathrm{W}_{\mathrm{P}}\in\mathbb{R}^{(\mathrm{P}\times\mathrm{P}\times \mathrm{C})\times\mathrm{d}^{\prime}}\), where \(\mathrm{d}^{\prime}\) denotes the embedding vector dimension, and \(\mathrm{d}^{\prime}=150,528\) (\(224\times 224\times 3\)). Vision-RWKV outputs a high-dimensional global feature vector with a flattened dimension of 150,528. The core mechanism of Vision-RWKV is the Key-Value accumulation, adapted for spatial dimensions. Each image patch is treated as a spatial token, and the embedding is applied to transform each patch into a spatial feature vector \(\mathrm{E}_{\mathrm{V}}\). The Key and Value mappings are then calculated, as illustrated in (7), where the variables \(\mathrm{W}_{\mathrm{V}\mathrm{K}},\mathrm{W}_{\mathrm{V}\mathrm{v}}\) represent trainable weight matrices. The state update mechanism follows a recurrence formula analogous to the RWKV model but tailored for spatial data, as detailed in (8). The final output comprises contextual features for each patch, encoded in a hidden state matrix \(\mathrm{H}_{\mathrm{V}}\in\mathbb{R}^{\mathrm{N}\times\mathrm{d}^{\prime}}\). To produce a global feature representation, pooling operations (e.g., average pooling) are typically employed to aggregate the features of all patches into a unified feature vector \(\mathrm{h}_{\mathrm{image}}\in\mathbb{R}^{150,528}\), as shown in (9).
## 3 Look-Up Table Generation and Testing
### Look-Up Table Generation  
Detailed chemistry simulations under adiabatic constant pressure reactor conditions were performed using the software LOGEtable [53]. Initial mixture unburned temperature (T\({}_{u}\)), pressure (p), equivalence ratio (\(\phi\)) and mass fraction EGR (yEGR) are the input variables of the look-up table generator. Table grid points were chosen so that the typical range of thermodynamic conditions encountered in conventional direct injected engines that can be expected to be found in conventional direct injected engines. The table grid adopted in this work relies consists of 205,200 points which are distributed across tabulation parameters as outlined in Table 1.  
Except for the EGR mass fraction space, the points are distributed in a non-equidistant manner so that typical engine relevant conditions (i.e., around stoichiometry in equivalence ratio space) are better resolved. The EGR stream is assumed to have a fixed composition comprising combustion products (CO\({}_{2}\), H\({}_{2}\)O and N\({}_{2}\)) of the given fuel mixture at stoichiometry.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Lower Bound** & **Upper Bound** & **Number of Points** \\ \hline Unburned Temperature (K) & 300 & 1400 & 76 \\ Pressure (bar) & 1.0 & 200 & 18 \\ Equivalence Ratio (-) & 0.05 & 6.0 & 30 \\ EGR mass fraction (-) & 0.0 & 0.4 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Tabulation grid adopted for all tabulated chemistry runs.  
For each of the table grid points, the progress variable C introduced in Equation (6) is used to trace the reaction trajectory, and its source terms (dC/dt) are stored in the table at 25 different points between unburned C = 0 and fully burned C = 1.0 state. In addition, mean molecular weight, the thermodynamic polynomial coefficients and any chemical species that the user decides to monitor are also stored across the mentioned progress variable grid. In the present work, the following species have been included in the tabulation process so that major engine-out emissions and standard engine performance analyses could be done: fuel molecule, i.e., n-C\({}_{7}\)H\({}_{16}\), O\({}_{2}\), N\({}_{2}\), CO\({}_{2}\), H\({}_{2}\)O, CO, H\({}_{2}\), C\({}_{2}\)H\({}_{2}\), C\({}_{2}\)H\({}_{4}\) and unburned hydrocarbons (uHC) (defined as the sum of all species containing an H and C or H, C and O atoms). The resulting size on disk was approximately 1 GB.
## 3 Results and Discussion
### Sensory Analysis  
Results of sensory evaluation (total mean values by ANCOVA) were presented on the Figure 5a-c. Time and treatments had significant effects on sensory scores. The values of all evaluated attributes decreased with time, and the decrease was the most pronounced in the control sample (Figure S1). The highest scores of all attributes were ascribed for the sample with cardamom followed by cloves.  
All extracts inhibited the odor alteration of meat during storage when comparing to control (Figure 5a, Figure S1). Similar results were reported by [11], who noticed that sensory scores for control sample differed significantly from pork nuggets with black seed addition, which might result from inhibition of lipid oxidation and off-flavor development during storage by phenolic compounds found in black seed extract. The odor alteration of cooked chicken meat with the addition of wine industry residues extract was also lower comparing to control, as shown by [45]. In accordance to the results of this study, the protective effect of clove extract on odor attribute of raw chicken was also noticed by [9].  
In regards to color attribute no significant differences were observed between bay leaf-black seed-treated, and control samples (Figure 5b). This could result from the dark color of the extracts (Table 4), as was previously observed in chicken meat with grape extract [45].  
The addition of spice extract to chicken meat improved the texture of the products (Figure 5c). Others reported lack of statistically significant differences between texture scores of control and black seed-treated pork nuggets [11], and control and plant extract-treated goat meat [46].  
Figure 5: Effect of various spice extract treatments on sensory attributes: (**a**) Odor, (**b**) color, and (**c**) texture of raw ground chicken meat stored at 4 \({}^{\circ}\)C during 12 days. Columns present total mean values (from ANCOVA analysis); vertical bars denote 0.95 confidence intervals, and means with the same superscript are not different (\(p>0.05\), [PERSON]’s test). \({}^{*}\) indicates significant difference of the sample from the control one (\(p\leq 0.05\), [PERSON]’s test).
## 4 Experiments
### Evaluations  
#### 4.2.1 Complexity and Running Times  
First, we compare the complexity between SSO and PSO (a typical intelligent algorithm). Assuming that the iteration number of \(i\) particles was \(N_{i}\), \(i=1\), \(2\),..., \(m\), \(m\) is the maximum number of iteration, \(N_{1}=N_{2}=\cdots=N_{m}=N\). Assuming that each particle in each iteration requires the computational time \(T_{t}\), the total execution time of PSO algorithm for optimal operation is \(N\cdot m\cdot T_{t}\). As for SSO, assuming that each particle in each iteration requires the computational time \(D_{t}\), the total execution time in optimal operation is \(N\cdot m\cdot D_{t}\), where \(D_{t}=\alpha\cdot D_{1}+\beta\cdot D_{2}+\gamma\cdot D_{3}+\lambda\cdot D_{4}\), \(\alpha=0.1,\beta=0.45,\gamma=0.25,\lambda=0.25\). Table 4 further illustrates the experimental comparison, and shows that the SSO algorithm is more efficient than the PSO algorithm.