esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Kinematic Modeling
### Inverse Geometries  
In this section, the inverse geometric model of CDPRs is presented. Firstly, two coordinate systems are defined, i.e., the local frame \(R_{e}\{O_{e}x_{e}y_{e}z_{e}\}\) fixed on the moving platform and the global frame \(R_{G}\{O_{G}x_{G}y_{G}z_{G}\}\). Then, the homogeneous transformation matrix \(T\) from \(R_{e}\) to \(R_{G}\) could be defined as a function of the pose of the moving platform \(p_{j}(j=1,...n)\). For example, when \(n=6\), \(P=[x,y,z,\alpha,\beta,\gamma]^{\mathrm{T}}\), where \(x\), \(y\), \(z\) represent the position of the end-effector in Cartesian space and \(\alpha\), \(\beta\), \(\gamma\) represent the orientation of the end-effector using Euler angles.  
\[\mathbf{T}=\left[\begin{array}{cccc}\mathrm{c}\mathrm{c}\mathrm{a}\mathrm{s} \beta\mathrm{c}\gamma-\mathrm{c}\mathrm{a}\mathrm{c}\mathrm{g}\mathrm{s} \gamma-\mathrm{s}\mathrm{c}\mathrm{c}\mathrm{\gamma}&\mathrm{c}\mathrm{a} \mathrm{s}\beta&x\\ \mathrm{s}\mathrm{c}\mathrm{c}\mathrm{\beta}\mathrm{c}\gamma+\mathrm{c} \mathrm{a}\mathrm{s}\gamma&-\mathrm{s}\mathrm{c}\mathrm{g}\mathrm{s}\gamma+ \mathrm{c}\mathrm{a}\mathrm{c}\mathrm{c}\mathrm{\gamma}&\mathrm{s}\mathrm{a} \mathrm{s}\beta&y\\ -\mathrm{s}\mathrm{c}\mathrm{c}\gamma&\mathrm{s}\mathrm{g}\mathrm{s}\gamma& \mathrm{c}\mathrm{\beta}&z\\ 0&0&0&1\end{array}\right] \tag{1}\]  
Then, any attachment point on the moving platform \(\mathrm{A}_{i}\) could be expressed in the global frame:  
\[{}^{G}\mathrm{A}_{i}=\mathbf{T}\cdot{}^{e}\mathrm{A}_{i} \tag{2}\]  
Finally, the inverse geometrics of CDPRs could be formed as:  
\[l_{i}=\left\|{}^{G}\mathrm{A}_{i}-{}^{G}\mathrm{B}_{i}\right\|\,,\,(i=1,...m) \tag{3}\]  
It should be noted that the driving cable lengths are usually controlled by winches and motors. Therefore, it is necessary to introduce a simple equation to substitute the cable length \(l_{i}\) with the winch rotatory angle \(\theta_{i}\).  
\[l_{i}=\theta_{i}\cdot d_{i}/2 \tag{4}\]  
where \(d_{i}\) is the winch diameter. Then, the inverse geometric model can be reformed as:  
\[f_{i}(p_{1},p_{2},\cdots p_{n},{}^{e}\mathrm{A}_{i},{}^{G}\mathrm{B}_{i}, \theta_{i},d_{i})=0\ \ (i=1,\cdots m) \tag{5}\]  
Figure 1: Structural schematics of general cable-driven parallel robots (CDPRs).
# A Simple Model to Predict Machined Depth and Surface Profile for Picosecond Laser Surface Texturing
## 2 Modeling of LST and Experimental Procedures
### Experiments for Laser Modeling  
AISI 304 stainless steel sample (Shengjili Shenzhen, China) with a size of 25 mm \(\times\) 20 mm \(\times\) 0.5 mm and with an as-received surface roughness of \(\sim\)10 nm was used in this study. The sample was irradiated by a commercial Nd: YVO4 laser (PX50, Edgewave, Germany) with wavelength of 532 nm and pulse duration of 10 ps. A 50 mm focusing lens was used to focus the laser beam, and a diameter of \(\sim\)16 \(\upmu\)m with Gaussian profile was obtained at the focal plane, which was measured using [PERSON]'s method, as described in [31]. An x-y-z motorized stage (PS-30, Borui, China) with a movement precision 500 nm was employed to move the sample in the x-y plane.  
The LST experiments were performed in air at room temperature. The pulse frequency was fixed at 1 kHz to reduce the heat accumulation, while the pulse energy was fixed at 8 \(\upmu\)J. Pulse trains with numbers of 2, 5, 10, 15 and 20 were firstly fired to create micro-craters on the sample. Then,  
Figure 1: Schematics for the formation of micro-channel.  
micro-channels with a width of 50 \(\upmu\)m and a length of 300 \(\upmu\)m were selected to create on the sample. The scanning speed ranging from 1 mm/s to 5 mm/s, and the adjacent interval ranging from 2 \(\upmu\)m to 5 \(\upmu\)m, were employed to create micro-channels. The raster trajectory was overscanned 8 times to achieve a larger depth. After LST, the sample was cleaned in an ultrasonic bath by alcohol and distilled water to remove the debris. A confocal laser scanning microscope (OLS-400, Olympus, Japan) was used to observe the profiles of the micro-craters and micro-channels. The averaged depth of the micro-craters and micro-channels were obtained after performing measurements at five different micro-craters or locations.
# An Extremely Close Vibration Frequency Signal Recognition Using Deep Neural Networks
## 4 Results and Discussion
### Recognize Superposition Vibration Frequency  
The second proposed vibration frequency recognition scheme system was tested by recognizing specific image-based vibration signals with a multiple or superposition frequency, which combines two extremely close frequencies between 50.00 Hz and 50.10 Hz, as shown in Figure 7. The frequency-recognized data range is the same as the previous scheme used to analyze the performance by comparing the probability values equally. Due to this scheme consisting of two extremely close frequencies, the frequency recognized is the two of the highest probability values.  
Figure 6: The recognition probability for a single frequency using simulation testing data.  
In Figure 7, the \(x\)-axis represents frequency, and the \(y\)-axis represents the probability value. The proposed system correctly recognizes superposition frequency, meaning that each line contains two superposition frequencies. The superposition frequency has one fixed frequency value of 50.00 Hz, and the other has a varied frequency value from 50.01 Hz to 50.10 Hz.  
The first red line represents a superposition frequency of 50.00 Hz and 50.01 Hz with a recognition probability of 75.78% and 72.93%, respectively. The following lines are blue, green, and orange, representing frequencies 50.00 + 50.02 Hz, 50.00 + 50.03 Hz, and 50.00 + 50.04 Hz, with the recognition probability of 75.33% and 68.83%, 75.87% and 70.59%, and 75.07% and 71.49%, respectively.  
The following line is purple, yellow, and grey, representing frequencies 50.00 + 50.05 Hz, 50.00 + 50.06 Hz, and 50.00 + 50.07 Hz, with the recognition probability of 75.01% and 72.66%, 75.44% and 72.11%, and 75.03% and 58.45%, respectively. The last three lines, magenta, brown, and sky blue, represented frequencies 50.00 + 50.08 Hz, 50.00 + 50.09 Hz, and 50.00 + 50.10 Hz, with the recognition probability of 75.22% and 72.89%, 75.42% and 70.40%, and 75.27% and 73.06%, respectively. The probability varies from 0.7854 to 0.9536, and the line peak value represents the frequency recognized in the \(y\)-axis.  
The DNN model correctly recognized eight frequencies from ten frequency combinations based on the superposition frequency result. It influences the true positive (TP), false negative (FN), false positive (FP), and true negative (TN) values. Additionally, the probability value of simulation data using single vibration frequency signals is higher than the probability value of superposition vibration frequency signals.
## 3 Methodology
### Noise Signal Processing and Assessment  
The transformation of time-domain signals into frequency-domain signals is a fundamental technique in acoustics that reveals the spectral characteristics of sound. Our study employs the fast Fourier transform (FFT), an efficient algorithm for computing the discrete Fourier transform (DFT), to convert finite-length sequences into frequency-domain representations [45]. This process allows for a detailed analysis of noise signals by decomposing them into frequency components, with the amplitude and phase of each reflecting the signal's energy distribution across frequencies. We also applied a band-stop filter to remove interference from specific frequency ranges, typically background noise. This filtering is essential for isolating noise characteristics impacting metro drivers. The band-stop filter attenuates signals within a targeted frequency band while preserving others, thus enhancing the clarity of the noise profile relevant to our study [46].  
Figure 1: The proposed research framework for urban rail transit noise control from assessment to pattern identification and customized noise control strategy.  
To evaluate the impact of cabin noise on drivers, we converted filtered signals into sound pressure levels (SPLs), measured in decibels (dBs). SPL is essential for assessing auditory experiences and provides a basis for noise exposure evaluation. Key metrics include the equivalent continuous sound pressure level (Leq) since the average sound pressure over a period is essential for assessing long-term exposure. Maximum (Lmax) and minimum (Lmin) sound pressure levels identify the highest and lowest noise levels, reflecting both peak noise events and background noise [47].  
Additional metrics such as percentile sound pressure levels (L10, L25, L50, L75, L90) capture the distribution of noise over time, while the dynamic range (LR) measures fluctuations in noise levels. The main frequency component (MF) identifies dominant noise frequencies, and the time accumulation of energy (TE) provides a comprehensive view of total noise exposure. We also tracked noise events exceeding specific thresholds (NE70, NE75, NE80) to assess the frequency of potentially harmful noise peaks [48]. The noise assessment metrics are shown in Table 2.
## 2 Design and Development of 3D Curvature Sensor of FBG
### Feasibility Analysis of Space Curvature Measurement  
In this paper, the actual three-dimensional bending shape of the scraper conveyor is reconstructed by fitting the discrete point curvature of space. Since curvature is a vector, it has both direction and magnitude. The vector direction mainly represents the direction of curve plane bending, and the magnitude of the vector mainly indicates the degree of curve plane bending. In order to determine the curvature vector of a point on a space curve, it is necessary to measure the curvature vector magnitude of the point in two known directions to synthesize the direction and magnitude of the curvature vector. However, an FBG can only reflect the curvature value of one direction at a certain point, so at least two FBG strings are required. To simplify the synthetic curvature in two directions, the paper adopts the following method: two FBG strings are placed orthogonally at 90\({}^{\circ}\) on the axial surface of the substrate of the circular section, as shown in Figure 1.  
Figure 1 is a cross-section diagram of the FBG strings layout of the sensing flexible substrate. MM\({}^{\prime}\) is the neutral plane of the flexible substrate, and NN\({}^{\prime}\) is the curved plane. MM\({}^{\prime}\)s plane and NN\({}^{\prime}\)s plane are perpendicular to each other. AA' is the distance FBG string 1 to the neutral plane MM\({}^{\prime}\). \(\overline{AA^{\prime}}=r\cdot\sin\beta\). BB' is the distance FBG string 2 to the neutral plane MM\({}^{\prime}\). \(\overline{BB^{\prime}}=r\cdot\cos\beta\). \(\beta\) is the included angle between MM\({}^{\prime}\) and Y-axis. According to Equation (3), the strain variables of FBG strings in the X direction and Y direction are, respectively:  
\[\varepsilon_{x}=\frac{r\cos\beta}{\rho} \tag{6}\]  
\[\varepsilon_{y}=\frac{r\sin\beta}{\rho} \tag{7}\]  
By substituting Equations (6) and (7) into Equation (5), the wavelength offsets of the FBGs in the X direction and Y direction can be obtained as  
\[\Delta\lambda_{x}=(1-P_{e})\lambda_{Bx}\frac{r\cos\beta}{\rho} \tag{8}\]  
\[\Delta\lambda_{y}=(1-P_{e})\lambda_{By}\frac{r\sin\beta}{\rho} \tag{9}\]  
where \(\lambda_{Bx}\) and \(\lambda_{By}\) are the initial wavelength of FBGs in two directions. From Equations (8) and (9), we can obtain the angle \(\beta\) of included curvature, and Y-axis is  
\[\beta=\arctan\left(\frac{\Delta\lambda_{y}\lambda_{Bx}}{\Delta\lambda_{x} \lambda_{By}}\right) \tag{10}\]  
In Equation (10), \(\lambda_{Bx}\) and \(\lambda_{By}\) are fixed values \(\Delta\lambda_{x}\) and \(\Delta\lambda_{y}\) are constantly changing during the bending process, and we can get that the space curvature at this time is  
\[\rho=\frac{\text{r}(1-P_{e})\lambda_{Bx}}{\Delta\lambda_{x}}\cos\left(\arctan \left(\frac{\Delta\lambda_{y}\lambda_{Bx}}{\Delta\lambda_{x}\lambda_{By}} \right)\right) \tag{11}\]
## 3 Modeling and Trajectory Planning  
The model-based MPC requires a model of the considered manipulator. As usual, in many electromechanical systems, the electrical components' dynamics can be neglected because of much smaller time constants than the mechanical part [1]. Consequently, it is sufficient to model the dynamics of the mechanical subsystem with rigid MBS to derive the equations of motion in minimal form by describing the kinematics with generalized coordinates, see for example, Reference [2].
## 3 Simulation and Discussion  
First of all, this paper used two signals (1, 2). Signal 1 is the non-stationary signal whose amplitude changes over time. Then the noise in different intensities was added to signal one and the de-noising task was conducted using SVD. Finally, it compared the effect of four methods of selecting the effective singular values including maximum peak of the difference spectrum of the incremental singular entropy (MPODSOISE), maximum peak of the curvature spectrum of incremental singular entropy (MPOCSOISE), modified method-based difference spectrum of incremental singular entropy (MMBDSOISE), and modified method-based curvature spectrum of incremental singular entropy (MMBCSOISE).  
Signal one: \(x_{1}=(1+0.6t/f\text{s})\sin(2\pi tf1/f\text{s})+2\cos(2\pi tf2/f\text{s});f_{ 1}=40\) Hz,\(f_{2}=15\) Hz  
Signal two: \(x_{2}=5\sin(2\pi tf1/f\text{s})+4\sin(2\pi tf2/f\text{s})\cos(2\pi tf3/f\text{ s})+8\sin(2\pi tf4/f\text{s})\); \(f_{1}=10\) Hz, \(f_{2}=20\) Hz,\(f_{1}=30\) Hz,\(f_{2}=40\) Hz  
\(f\text{s}=2400\) Hz  
\(f_{1}\text{--}f_{4}\) are the characteristic frequencies of signals, \(f\text{s}\) is the sampling frequency, and the number of sampling is 1024. \(t\) is the number of times.  
Effective order has a great influence on the performance of noise reduction, for the sake of reflecting the de-noised effective order intuitively, this paper computes the SNR of the de-noised signal. SNR is an evaluating indicator of the performance of noise reduction, which can be defined as follows [24]:  
\[\text{SNR}\ =\ 10\text{log}\left(\sigma^{2}_{\text{signal}}/\sigma^{2}_{ \text{noise}}\right) \tag{16}\]  
where \(\sigma^{2}_{\text{signal}}\) is the variance of the signal and \(\sigma^{2}_{\text{noise}}\) is the variance of the noise. SNR reflects the average power ratios of signal and noise; the higher the SNR is, the better the performance of the noise reduction.
## 3 System Design  
### System Characteristics  
Figure 1 displays the system design of the present study. As shown in Figure 1, the system is made up of location-based AR and Web-GIS. Additionally, by combining pictograms, the system can effectively provide users with information concerning disaster support facilities in addition to sightseeing spots and tourism-related facilities in an easy-to-understand manner. In this way, users can easily obtain information concerning both sightseeing and disasters while sightseeing during normal times.  
In order to use the system, users can scan the QR code with their mobile information terminals and allow the application to be installed. By allowing this, the application will be installed, and users will be able to use various functions of the system by opening the application.  
In the system, sightseeing spots include parks, art museums and museums, and historical buildings, while tourism-related facilities include restaurants and cafes, convenience stores, train stations, parking lots, restrooms, accommodations, and stores and shops. Additionally, disaster support facilities include evacuation sites and shelters, water supply stations, medical facilities, convenience stores, restaurants and cafes, gas stations, support stations for those returning home, automated external defibrillator (AED) stations, and places that provide Internet access. For pictograms that represent the spots and facilities above, the standard guidance symbols provided by the Foundation for Promoting Personal Mobility and Ecological Transportation [42] were adopted. These symbols meet the international standards of the Japanese Industrial Standards (JIS) and the International Organization for Standardization (ISO), and they are recognized by the exploratory committee for their comprehensiveness and visibility.
# Rendezvous Based Adaptive Path Construction for Mobile Sink in WSNs Using Fuzzy Logic
###### Abstract  
In this paper, an adaptive path construction approach for Mobile Sink (MS) in wireless sensor networks (WSNs) for data gathering has been proposed. The path is constructed based on selecting Rendezvous Points (RPs) in the sensing field where the MS stops in order to collect the data. Compared with the most existing RP-based schemes, which rely on fixed RPs to construct the path where these points will stay fixed during the whole network lifetime, we propose an adaptive path construction where the locations of the RPs are dynamically updated using a Fuzzy Inference System (FIS). The proposed FIS, which is named Fuzzy_RPs, has three inputs and one output. The inputs are: the remaining energy of the sensor nodes, the transmission distance between the RPs and the sensor nodes, and the number of surrounding neighbors of each node. The output of FIS is a weight value for each sensor node generated based on the previous three parameters and, thus, each RP is updated to its new location accordingly. Simulation results have shown that the proposed approach extends the network lifetime compared with another existing approach that uses fixed RPs. For example, in terms of using the first dead node as a metric for the network lifetime, when the number of deployed sensor nodes changes from 150 to 300, an improvement that ranges from 48.3% to 83.76% has been achieved compared with another related approach that uses fixed RPs.  
WSN; mobile sinks; rendezvous points; fuzzy inference system; IoT; network lifetime +
Footnote †: journal: Computer Physics Communications  
1
# Toward a Practical Digital Twin Platform Tailored to the Requirements of Industrial Energy Systems
## 2 State of the Art
### Digital Twin Architectures  
A number of research articles have addressed DT concepts, architectures and platforms in varying levels of abstraction in recent years. [PERSON] et al. [16] and [PERSON] et al. [76] broach the issue of DT architectures as part of their respective review papers. However, we could only ascribe four references [51; 63; 77; 78] to the energy domain. While it is  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**ID** & **Requirement** & **Source** \\ \hline R1 & The DT must be able to observe the physical world in real time via the use of sensors. & [61; 67] \\ R2 & The DT must be able to monitor, inform and issue warnings on relevant physical alterations. & [37; 67] \\ R3 & The DT must be able to actuate physical components. & [67] \\ R4 & The DT should be designed in a modular fashion. & [61; 69; 72] \\ R5 & The DT must be able to make decisions and trigger actions on the virtual entity. & [67] \\ R6 & The DT should be open, re-usable, and scalable. & [61; 69] \\ R7 & The DT must be able to optimize operation of the physical entity. & [61; 67] \\ R8 & The DT must provide interfaces for seamless user interaction. & [70; 74] \\ R9 & The DT must be robust and able to provide automatic model adaptation, i.e., for simulating the physical entity. & [69; 72] \\ R10 & The DT must be able to predict the behavior of the physical entity based on simulations and sensing. & [61; 67; 69] \\ R11 & The DT should be able to accommodate simulation models for various applications and in arbitrary fidelity. & [61; 67] \\ R12 & The DT should build on standardized technologies and use available metadata, hence facilitating model integration, information exchange and maintenance. & [70; 72] \\ R13 & The DT must be able to process heterogeneous data from different sources. & [37; 61] \\ R14 & The DT should be able to store data with context information and exchange information in a semantically meaningful way. & [61; 69; 70] \\ R15 & The DT must allow for safe and secure operation of the physical entity. & [75] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Requirements of DT platforms for application in IES. The requirements are specified together with an identifier for later reference and with literature sources that provided motivation for them.  
considered an urgent necessity to define standardized DT frameworks, no consensus has been reached [9] yet. This is even more critical for DTs in the energy domain.  
In this subsection, concepts and architectures for DTs and related work for cyber-physical systems (CPS) are reviewed and then discussed with regard to their viability for DTs of IES.
# WAMs Based Eigenvalue Space Model for High Impedance Fault Detection
## 3 HIF Detection in Eigenvalue Space
### Robustness to Noise  
High sensitivity can be a double-edged sword as increased dependability can lead to false positives. The performance of the solution presented in this work was studied in the presence of noise. Noise injections do not change the general pattern followed by the eigenvalues, the overall pattern in simply shifted. Figure 11 illustrates this behavior.  
In extreme cases where the magnitude of the noise can be classified as a gross error in measurement, the eigenvalues travel far beyond their normal zones, somewhat similar to the behavior seen during high magnitude faults. A scheme for the identification of gross errors in measurements can be developed from this solution: when the Subspace Estimator sees a dramatic Eigenvalue drift, the estimator could verify the status of the relay(s) protecting the line. If over-current elements in the relay have not been picked up, this would indicate an erroneous reading. This could improve the robustness of WAMS based State Estimation solutions.  
Eigenvalue space could also be integrated into relaying schemes to enhance the security of the scheme. Similar to a permissive scheme, a relay could poll the status of the estimator before sending a trip or a block command. Figure 12 illustrates the algorithm of the model introduced in this work.  
Figure 11: Eigenvalue estimation in eigenvalue space with noise.  
Figure 10: Eigenvalue drift during a fault with a resistance of 100 \(\Omega\).  
A case study and the associated findings are presented in the next section.
## 2 Related Work  
### Catchment Modelling Process  
The modeling process involves multiple approximation stages, transitioning from perceptual to conceptual and eventually procedural models for quantitative predictions.  
A primary decision in modeling catchment processes involves selecting between lumped or distributed modeling methods. Lumped models represent the entire catchment as a single unit, utilizing state variables that denote average conditions across the entire area. Distributed models, in contrast, produce spatially explicit predictions, reflecting variability across different locations within the catchment.  
Hydrological models can also be differentiated based on their underlying methods for depicting catchment processes--either deterministic or data-driven [10]. Deterministic models solve explicit sets of equations representing physical processes within the watershed, resulting in single, precise outputs given specific parameter values. Data-driven models can replicate stochastic and probabilistic behaviors of input variables and outcomes influencing river flow.  
Data-driven models are frequently labeled as \"black-box\" models, since they rely predominantly on statistical or input-output relationships among hydrological variables, without explicitly modeling the underlying physical processes. However, this characterization might not always be accurate. In certain instances, input-output analytical approaches might yield conclusions substantially different from those suggested by conventional theoretical analyses, potentially indicating inaccuracies in theoretical assumptions [11]. Consequently, data-driven modeling approaches are increasingly preferred over process-based models for runoff forecasting applications [12].