esa-sceva/satcom-chunk-collection huggingface dataset:
# Wavelet-Based Analysis of Motor Current Signals for Detecting Obstacles in Train Doors
## 5 Experimental Results  
To validate the simulation results, an experiment was performed. A downscaled test bench model was designed in Solidworks and constructed. The dimensions of the test bench chassis were 162 \(\times\) 60 \(\times\) 75.0 cm\({}^{3}\) (L \(\times\) W \(\times\) H). The test bench comprised a chassis, sliding doors, wheels, a lead screw, corner brackets, and a few three-dimensional printed parts. The bottom of each door frame had two wheels, and a rack was fixed to the chassis as a path for the sliding motion of the doors. The brushed DC motor and lead screw were coupled and assembled on the chassis. The final assembly of the test bench is shown in Figure 13.  
The thresholds for W9 and peak were calibrated through extensive empirical testing and analysis using a healthy door system under controlled conditions. Specifically, the calibration process involved measuring the noise in the motor current signal and amplitude of the motor steady-state current (which helped us to analyze the dynamic friction) while operating the doors. This noise and dynamic friction value was added to the simulation model to simulate real-world conditions and various obstacle forces were applied. With the resulting amplitudes of W9 and peak, the rules in the MATLAB function were updated. Later, no-obstacle, soft-object, and hard-object t  
Figure 12: Hard-obstacle scenario. (**a**) W9 and peak amplitudes; (**b**) simulation output; (**c**) force between doors and motor current signal.
# Dynamic Reconfiguration of Active Distribution Network Based on Improved Equilibrium Optimizer
## 4 Optimization Algorithms
### Feasible Solution Determination  
In the distribution network reconfiguration problem, the optimal combination of switch action sequences must satisfy the radial and connectivity constraints of the network, ensuring that no islanding or ring network formation occurs. However, during the search process of intelligent optimization algorithms, the randomness in the combination of switch opening and closing states leads to a high number of infeasible solutions and low-quality feasible solutions, thereby increasing the computational cost of reconfiguration. To address this, a feasible solution determination model, \(\mathbf{JR}\), based on adaptive ordered loops, is proposed. This model helps to avoid infeasible solutions generated during the reconfiguration process and eliminates inferior solutions with high fitness values, thereby compressing the solution space.
## 2 Materials and Methods
### Quantitative Analysis  
High-resolution microscopic images (\(\times\)40 and \(\times\)100) of H&E- and Cresyl violet-stained cardiac and cerebellar sections, in addition to IP\({}_{3}\)R immunolabeled cardiac and cerebellar sections, were digitally captured using a Leica ICC50 HD video camera linked to a Leica DM 500 microscope (Leica Biosystems, Pacheco, CA, USA). A total of 20 camera fields were analyzed per group. In each photomicrograph of H&E-stained sections, the total percentage area fraction (Af) occupied by myocytes (Afm) and interstitial spaces (Afi) was determined using the grid method and the cell counter plugin of ImageJ. Afm and Afi were calculated using the following equation:  
\[\text{Afm or Afi}=(\text{App}\times\sum\text{p})/(\text{A slide})\times 100 \tag{1}\]  
where App = area per point, \(\sum\)p = sum of points, and A slide = set scale area.  
Counts (density) of granule and Purkinje cells in the Cresyl violet-stained sections and counts of IP\({}_{3}\)R expression in the cardiac and cerebellum immunolabeled sections were carried out using the cell counter plugin of ImageJ. Furthermore, myocytes were semiquantitatively scored for thin or wavy fibers; accordingly, 0 was assigned for no observations of thin or wavy fibers, 1 for small areas of wavy fibers, 2 for multiple areas of wavy fibers, and 3 for large areas of wavy fibers.
## 4 Results and Discussion
### Watermark Robustness
#### Resistance to Object Attack  
Under reordering attacks, the _NC_ values for the DTM-based watermarks keep as 1 and those for the SVD-based watermarks are all higher than 0.99, implying that the proposed scheme is effective against reordering attacks. The proposed watermarking scheme does not include object nor vertex identification, as such, the reordering attack has no effect on the feature sequence or the generated watermark. Because the SVD matrix used in this study has a fixed matrix size, reordering attacks could have influence on the singular value of the SVD matrix if an object contains more vertices than the matrix.  
Figure 6: The _NC_ change curves under increased levels of (**a**) randomly added vertices and (**b**) randomly deleted vertices.  
As shown in Figure 7a, our proposed method performs well under the object addition attack for both ZC and SZ. The _NC_ values of the feature vertex watermarks, namely ZC/DTM and SZ/DTM, keep above 0.84 within the attack range and only decline slowly as the attack intensity increases. The _NC_ values of the non-feature vertex watermarks, namely ZC/SVD and SZ/SVD, are always 1. Consistent with the non-feature vertex watermarks, the dual zero-watermarking schemes always have the _NC_ values of 1 under the object addition attack. In Figure 7b, the DTM-based watermarks have the _NC_ values of 1 for the first 35% deletion rate of the feature vertex watermarks and the quality of the watermarks greatly reduces as object deletion enhances. When more than 45% of objects are removed, watermarks could not be detected efficiently as the _NC_ values are lower than 0.8. The SVD-based watermarks could not be effectively detected when the deletion ratio exceeds 25%. The dual zero-watermarking scheme maintains the same trend as the feature vertex watermark, showing that the proposed scheme is robust against both object addition and deletion.
## 2 Background and Related Works
### LEACH Protocol Energy Efficiency in WSNs
In each CH determination loop, one WSN node was identified as a CH node in each zone, and the role was rotated between WSN nodes that occurred in the same zone. However, this technology does not deal with a large number of WSN nodes [41]. Therefore, the LEACH protocol remains amenable to improvement through increased efficiency in selecting the appropriate CH in each loop, and that will be one of the contributions of this paper. A User Datagram Protocol (UDP) is used as the data transmission protocol in WSNs to reduce the packet's clustering complexity and reduce CPU overhead. Moreover, to secure data transmission over UDP, the Datagram Transport Layer Security (DTLS) protocol is used on top of UDP [42]. However, these WSN nodes are designed to operate in various untrusted surroundings, which are not periodically monitored. This makes WSN nodes vulnerable to various security attacks, especially if they are related to important and sensitive data [43]. Moreover, with regard to WSN nodes' operational boundaries in CPU power and energy [44], it is sometimes difficult to provide a charger for them in these conditions.
## 3 Numerical Modeling and Validation
### Numerical Model Development
#### 3.1.1 Fundamental Model Settings  
(1) Geometric Parameters  
The rock mass was assumed homogeneous and isotropic, with groundwater effects and time-dependent behaviors excluded. Key geometric parameters included the following: tunnel radius: \(R\) = 6 m; full-face excavation advance length: \(l\) = 1 m; buried depth: \(H\) = 50 m; excavated section length: \(L_{1}\) = 99 m; and unexcavated section length: \(L_{2}\) = 101 m. The computational domain configuration is illustrated in Figure 1a, with LDP data collected post-excavation.  
Figure 1: (**a**) Geometric model for the numerical simulation of circular unsupported tunnels. (**b**) A mesh model for the numerical simulation.  
(2) Meshing Parameters  
A hexahedral mesh configuration was adopted in this model, characterized by progressive refinement near the excavation face. Specifically, in the Solid Mechanics module of COMSOL Multiphysics, the \"discretization\" of the displacement field was set to \"quadratic\", and the hexahedral mesh was generated through a sweep operation. The shape functions were 20-node quadratic Serendipity elements. The final mesh consisted of 44,415 hexahedral elements, as shown in Figure 1b.
# Automatic Vertical Parking Reference Trajectory Based on Improved Immune Shark Smell Optimization
###### Abstract  
Parking path optimization is the principal problem of automatic vertical parking (AVP); however, it is difficult to determine a collision avoiding, smooth, and accurate optimized parking path using traditional parking reference trajectory optimization methods. In order to implement high-performance automatic parking reference trajectory optimization, we establish an automatic parking reference trajectory optimization model using cubic spline interpolation, and we propose an improved immune shark smell optimization (IISSO) to solve it. Firstly, we take the length of the parking reference trajectory as the optimization objective, and we introduce an intelligent automatic parking path optimization model using cubic spline interpolation. Secondly, the improved immune shark optimization algorithm combines the immune, refraction, and Gaussian variation mechanisms, thus effectively improving its global optimization ability. The simulation results for the parking path optimization experiments indicate that the proposed IISSO has a higher optimization accuracy and faster calculation speed; hence, it can obtain a parking path with higher optimization performance.  
automatic vertical parking; reference trajectory optimization; improved immune shark smell optimization; Gaussian variation; refraction
## 2 Methodology
### ROI Definition Using Boom Detection  
The YOLOv11 architecture was applied to detect the boom object, which plays a central role in identifying the pouring zone. Note that in our coordinate system, the \(y\)-axis is oriented downward from the top of the image, which is the standard convention in image processing. This means that higher y-values indicate positions lower in the image. Once the boom is detected, its bounding box is used to define the ROI, where associated concrete objects are likely to be located during the pouring operation. As illustrated in Figure 2, the ROI is designed to spatially restrict detection to a region directly beneath the boom. This minimizes false positives from unrelated objects and improves detection accuracy by focusing only on the active pouring area. The ROI is mathematically defined as:  
\[\textbf{ROI}_{\textbf{Concrete}}=\left\{(\textbf{\emph{x}},\textbf{\emph{y}}) \mid\textbf{\emph{x}}\in[\textbf{\emph{x}}_{\textbf{Boom.min}},\textbf{\emph{ x}}_{\textbf{Boom.max}}],\textbf{\emph{y}}>\textbf{\emph{y}}_{\textbf{Boom.max}}\right\} \tag{1}\]  
The ROI is mathematically defined as in Equation (1). Here, \(\textbf{\emph{x}}_{\textbf{Boom},\textit{min}}\) and \(\textbf{\emph{x}}_{\textbf{Boom},\textit{max}}\) denote the left and right boundaries of the detected boom's bounding box, respectively; \(\textbf{\emph{y}}_{\textbf{Boom},\textit{max}}\) represents the vertical bottom edge of the bounding box. The ROI includes all pixel locations that are horizontally within the boom's bounding box range and vertically below the bottom boundary of the boom.  
This constraint reflects the physical assumption that concrete objects actively involved in the pouring task must appear directly beneath the boom in the image. By filtering out objects located outside this spatial range, the framework ensures that only relevant areas are analyzed. This allows the subsequent relationship-based analysis to focus solely on valid candidate regions that are contextually associated with actual pouring activity.
# A Comparative Analysis of International Standards on Curved Surface Isolators for Buildings
## 2 Case Study Building and Seismic Action
In Eurocode 8 [57], seismic action is specified for a reference exceedance probability of 10% in 50 years, which translates to a return period of 475 years. This value can be adjusted using the importance factor (\(I\)) to reflect the importance classification of structures. The peak ground acceleration (PGA) serves as the reference seismic intensity, also associated with an exceedance probability of 10% in 50 years. The importance factor is similarly based on a reference return period of 475 years, ensuring that structures are designed to meet the no-collapse requirement, meaning they should not experience either local or total failure under the design seismic action. The shape of the elastic response spectrum defined in Eurocode 8 is illustrated in Figure 3a. Figure 3: (**a**) Illustrates the EC8 elastic response spectrum, which consists of distinct regions; and (**b**) represents the ASCE/SEI 7-22 two-period response spectrum, which defines the spectral acceleration using a simplified approach with key transitions at specific period values. Figure 2: The dynamic spectral factor is a key parameter in seismic design that accounts for the amplification of ground motion based on foundation soil type, seismic zone, and damping ratio: site S3, seismic zone ZIII, damping ratio (\(\zeta\) = 5%), and the ductility reduction factor (\(\mu\) = 1, 1.5, 2, 3, 4, 6). In contrast, ASCE/SEI 7-22 [58], in Chapter 11 \"Seismic Design Criteria\", defines the response spectrum using two different methods, as described in Section 11.4.5 of the same standard. The first approach relies on the multi-period design response spectrum, which provides a more detailed representation of the frequency content of design ground motions and is considered the preferred characterization of spectral response. The second approach, known as the two-period design response spectrum, offers a simplified representation based on seismic parameter values. Unless a site-specific ground motion study is performed, the two-period spectrum can be used for design purposes. Due to its simplicity and ease of application, this second method is adopted in this study, with Figure 3b presenting the corresponding two-period design response spectrum as described in ASCE/SEI 7-22 [58]. These differences in the definition of response spectra across CSCR-10, EC8, and ASCE/SEI 7-22 highlight the variation in seismic hazard characterization and design methodologies. While all three standards aim to achieve structural safety, their differences in reference return periods, spectral anchoring, and methodology for defining design ground motions can lead to variations in seismic demand predictions, ultimately affecting the design of base-isolated structures.
## 6 Results  
In order to evaluate the latency and energy consumption for the various scenarios (IP, IP+SCHC, NIDD and NIDD+SCHC), we conducted independent measurement campaigns. For each scenario with a specific set of parameters (radio conditions and message sizes), we measured 20 samples, except for radio condition 4, for which we extended the number of measurements due to the high dispersion of values. The results presented next are the average values of the latency and energy consumption.
## 2 Background and Motivation  
### DRAM Operation and Timing Parameters  
The hierarchy of a DRAM device organization is presented in Figure 1. As shown in Figure 1, a DRAM cell is the lowest level of DRAM structure, which stores one bit of data based on the charge of its capacitor. A cell encodes value \"1\" when the capacitor is fully charged and value \"0\" as it is fully discharged. The cells are written to or read from using the access transistors, which are enabled by the _wordline_ and the _bitline_ connects the cells of each column. Figure 1 shows how these components form a two-dimensional subarray of a DRAM module. The combination of numerous subarrays forms a bank and banks are cooperated to organize a DRAM chip, as shown in Figure 1. To activate a row, the row decoder enables the corresponding _wordline_, and then the stored information is transferred to the sense amplifiers. When the row is accessed, the data can be read/written using _rd/wr_ commands [23]. Each read operation consists of multiple states, and the memory controller produces different commands and manages the states (see Figure 2). In the precharge state, all _bitlines_ are precharged, and other open _wordlines_ are deactivated after using the precharge (PRE) command. Before any _rd/wr_ command can be generated, the corresponding _wordline_ must be opened via the activation (ACT) command. Next, _rd/wr_ commands can be sent to the opened row, subject to a minimum required time (activation time, \(\mathrm{t}_{\mathrm{RCD}}\)). For a subsequent read/write operation, it is necessary to issue a PRE command to deactivate the opened row. The next row will be accessible after a specified time named precharge time (\(\mathrm{t}_{\mathrm{RP}}\)). There are also other timing parameters such as \(\mathrm{t}_{\mathrm{RAS}}\) and \(\mathrm{t}_{\mathrm{CL}}\) that are used by the memory controller to manage DRAM operations [24; 25; 26; 27].
## 4 Discussion
### Sensitivity Analysis
Then, when the launched power exceeds 1 dBm, the distortion caused by the fiber nonlinear effect begins to become the main factor that dominates the system performance. As a result, the MI performance of the two modulation formats start to gradually decrease after reaching the maximum values of 4.99 bit/symbol and 4.76 bit/symbol, respectively, and will drop to 4.67 bit/symbol and 4.45 bit/symbol when the launched power is 5 dBm. Overall, for the constellation structure of GS-32 QAM designed in this paper, its sensitivity to nonlinear noise does not exhibit obvious differences from that of the traditional R-32 QAM. Figure 12: The sensitivity analysis of the proposed scheme under different testing line width values. (**a**) MI results of GS-32 QAM with a combined linewidth of 100 kHz; (**b**) MI results of GS-128 QAM with a combined linewidth of 100 kHz; (**c**) MI results of GS-32 QAM with a combined linewidth of 500 kHz; (**d**) MI results of GS-128 QAM with a combined linewidth of 500 kHz. Secondly, the MI performance of GS-128 QAM and R-128 QAM after fiber link transmission are also tested and compared. After 160 km, i.e., two spans of SMF transmission, the MI results corresponding to the two modulation formats under different channel conditions are shown in Figure 14. For the R-128 QAM, due to the serious incompatibility between it and the PCPE algorithm, even at the optimal launched optical power value, the corresponding MI performance is only 4.84 bit/symbol after fiber transmission, which is far below the threshold required for reliable transmission. In comparison, the proposed GS-128 QAM signal achieves the highest performance value of 6.62 bit/symbol at 0 dBm. When the launched optical power gradually increases from 0 dBm to 5 dBm, the MI performance value of GS-128 QAM will gradually decrease, but even in the case of the most severe nonlinear distortion, the MI value can still reach 5.91 bit/symbol, which exceeds the threshold corresponding to 20% SD-FEC. Based on the above results, it can be seen that the GS-32 QAM and GS-128 QAM proposed in this paper also show good tolerance to ASE noise and nonlinear distortion, and can achieve MI performance significantly better than traditional odd-bit QAM signals even after 100 km of fiber link transmission. Figure 14: MI versus launched optical signal power into 160 km single mode fiber for 128 QAM constellations with PCPE algorithm. Figure 13: MI versus launched optical signal power into 400 km single mode fiber for 32 QAM constellations with PCPE algorithm.
# A Survey on Machine Learning Approaches for Personalized Coaching with Human Digital Twins
## 3 Context, Research Questions, and Search Strategy
### Research Questions  
This example clearly shows that ML can be applied in several components of HDTs. In this study, we will focus on establishing the state of the art in the selection and optimization of ML algorithms in personalized HDT applications in healthcare. To establish this state of the art, the first component to consider is machine learning algorithms, their applications, and current guidelines in algorithm selection. The second component is the application of ML in HDT applications within healthcare. In this survey, we are interested in evaluating the performance of personalized ML implementations of these HDTs. The last  
Figure 2: Virtual fitness coach components.  
component is the human behavior datasets, their characteristics, and their relevance in the process of selecting the ML algorithm. From these three components, we can formulate the following questions that need to be answered to arrive at the state of the art.  
1. What is the general approach used when selecting and evaluating ML algorithms?
2. Which ML algorithms are relevant in personalized HDTs?
3. How is the performance of ML implementations evaluated in HDTs?
4. What do we know about the influence of the characteristics of human (behavior) datasets on the performance of different ML algorithms?
## 6 Evaluation
### Performance Metrics  
In the evaluation effect part, three measures are considered, namely detection accuracy, false-positive rate (FPR), and false-negative rate (FNR), which are classified by the confusion matrix shown in Table 3. True positive (TP) is the number of attacksbeing attacked; false negative (FN) is the number of attacks seen as normal usagebeing attacked, false positive (FP) is the number attacks under normal usage situations, and true negative (TN) is the number of attacks seen as normal usage under normal usage situations. Detection accuracy means that the detection system can correctly identify whether it is an attack or normal. For example, according to Formula (4), FPR means that the detection system wrongly identifies attacks under normal usage situations according to Formula (5). FNR means that the detection system cannot identify the attacks and miss rate according to Formula (6).  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{4}\]  
\[FPR=\frac{FP}{FP+TN} \tag{5}\]  
\[FNR=\frac{FN}{FN+TP} \tag{6}\]