esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In Reference [97], analytical tuning rules for a 2-DOF PID controller, where overall optimal closed-loop response is obtained via two-step tuning of the closed-loop transfer function, were proposed. Controller designs based on IMC PID tuning rules were proposed in References [8; 34; 67; 68; 69; 70; 107]. In Reference [71], a modified IMC design based on a 2-DOF control structure that allows the separate optimisation of tracking and disturbance-rejection performance was proposed. In Reference [72], an IMC PD controller with a fractional-order filter was proposed. In Reference [73], particle swarm optimisation (PSO) and artificial bee colony (ABC) algorithms were used for PID controller tuning. Tuning rules, obtained by optimisation, were also proposed in References [74] for PI/PID, [75] for PI/PID, and [76] for I-PD. The method proposed in Reference [75] applied an additional reference filter to reduce overshoots in the tracking response. In Reference [32], analytical PI/PID controller tuning rules for IPs with non-minimum phase zeros were proposed. Calculation of the controller parameters, based on the model and the minimum Integral of the Absolute Error (IAE) criterion, was accomplished via the golden section searching algorithm. To create a 2-DOF control system, a PID controller with a unique reference filter was proposed in Reference [83]. A PID controller with set-point weighting in series with a lead/lag compensator was proposed in Reference [84]. PI/PID controller tuning rules and a method for the identification of IPs with a time-delay and inverse response from an open-loop step response were proposed in Reference [100]. In Reference [86], a methodology for the closed-loop performance assessment of a PID controller is proposed. First, the process is approximated as IFOTD from the closed-loop step response, and then the performance is evaluated by comparing it with the performance provided by the SIMC [8] tuning rule. In case the performance is not satisfactory, the PID controller is retuned using the same tuning rule. Analytical PI/PID controller tuning rules for ITD and for IFOTD based on the MDPM were proposed in Reference [102]. If the closed-loop tracking responses have higher overshoots, the method employs a 2-DOF PID controller. Note that most tunings methods for IFOTD can also be applied to other types of IPs if the process can be modelled by the IFOTD model [8; 68; 69; 74; 75; 77; 78; 79; 80; 84; 87; 88; 91; 92; 93; 94; 95; 96; 98; 100; 106; 107]. Next, we review the tuning methods for general (higher-order) IPs with time-delay. A symmetrical-optimum method for tuning PID controllers for general IPs was proposed in Reference [108]. Although this method results in higher overshoots for tracking, it is effective for disturbance
# Experimental Study on Out-of-Plane Seismic Performance of New Type Masonry System
## 3 Shaking Table Tests
### Test Setup and Measurement System  
The test setup of the shaking table test is shown in Figure 7. Masonry walls are generally fixed to the slab and beam with mortar at the top and bottom, and the out-of-plane behavior of the wall is formed by the behaviors of the building and the wall itself. In this test, due to the restrictions of the experiment equipment, the behavior of the building  
Figure 6: Details of typical and proposed concrete block wall specimens (unit: mm): (**a**) Specim CB; (**b**) Specim HS; (**c**) Specim PS.  
was not reproduced, and only the out-of-plane behavior of the wall itself was considered. In order to reproduce this constraint, the specimen was built within the steel frames, as shown in Figure 7. To avoid the development of vertical force on the specimen, due to vertical confinement by the steel frame, a vertical gap of approximately 20 mm was left between the specimen and the top steel plate, whereas the specimen was tightly sandwiched by the rigid L-shaped angles to prevent horizontal slip of the specimen on/at the bottom/top steel plates during excitation. Thus, a roller condition with a freedom in the vertical direction was created at the top by inserting Teflon sheet between the L-shaped angle and the block wall, preventing vertical friction.  
A shaking table of 2 \(\times\) 2 m was used for the test. It had a maximum earthquake acceleration of 8.9 m/s\({}^{2}\), with a restricting weight of 1000 kg. To measure displacement and acceleration over the specimen's height during the test, six laser transducers and ten accelerometers were installed at different locations, as shown in Figures 8 and 9.
# 3D Reconstruction of a Single Bubble in Transparent Media Using Three Orthographic Digital Images
## 4 Benchmark with Synthetic Bubbles
### Effects of Posture Correction  
The bubble volume was used to measure the accuracy of the reconstruction results. The difference between the calculated volume and the actual volume in the form of mean relative error (MRE) was calculated during experiments. The standard deviation of the calculated results was represented by error bars. All the examples were tested three times and the rotation angles around \(x\)-axis, \(y\)-axis, and \(z\)-axis of each test case were generated randomly.  
Figure 9 compares the accuracy of the reconstruction methods (in the form of MRE) with/without posture correction, where the sphere (sample No. 12), ellipsoid (sample No. 7), and inverted teardrop  
Figure 8: Images of the bubble samples.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Bubble Shape** & **Sample No.** & **2a (mm)** & **2b (mm)** & **2c (mm)** & **Volume (mm\({}^{3}\))** \\ \hline  & 1 & 20 & 2 & 10 & 223 \\  & 2 & 20 & 3 & 10 & 316 \\  & 3 & 20 & 5 & 10 & 549 \\  & 4 & 20 & 7 & 10 & 784 \\  & 5 & 20 & 9 & 10 & 946 \\ Ellipsoid & 6 & 20 & 11 & 10 & 1113 \\  & 7 & 20 & 13 & 10 & 1321 \\  & 8 & 20 & 15 & 10 & 1623 \\  & 9 & 20 & 17 & 10 & 1825 \\  & 10 & 20 & 19 & 10 & 1997 \\  & 11 & 20 & 20 & 10 & 2079 \\ Sphere & 12 & 16 & 16 & 16 & 2141 \\ Inverted teardrop & 13 & 15 & 15 & 32 & 2522 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The parameters of the different produced bubbles.  
Figure 7: Synthetic bubbles: from left to right, a sphere, an ellipsoid, and an inverted teardrop. The \(2a\), \(2b\), and \(2c\) are three characteristic parameters that represent bubble shape. The axes \(2a\) and \(2b\) represents the two horizontal axes, while \(2c\) represents the vertical axis.  
(sample No. 13) are analyzed using the methods in this paper, [PERSON] et al. [21], and [PERSON] et al. [20]. Since the spheres have an equivalent length in whichever direction, posture correction does not theoretically influence the reconstruction, leading to slight distinction between the corrected and uncorrected data. On the other hand, the ellipsoids reflect the degree of deformation in the horizontal direction, while the inverted teardrops show the degree of deformation in the vertical direction. Therefore, by comparing the data on the ellipsoids and the inverted teardrops, it can logically be found that posture correction significantly enhances the reconstruction accuracy of the method in this paper, but the effects on methods in [PERSON] et al. [21] and [PERSON] et al. [20] are slight. This indicates that the posture correction is undoubtedly required in our method for the reconstruction of bubbles having serious deformation.
# An Efficient Approach to Manage Natural Noises in Recommender Systems
## 3 The Proposed Approach of Managing Natural Noises
### Detection of Natural Noise
#### 3.1.3 The Rules of Detecting Natural Noise  
After classifying users and items, we can make the following judgment. The ratings of Negative users on No-Preferred items should be low. If Negative users provide medium or high ratings on No-Preferred items, the ratings will be regarded as natural noises. Similarly, if the ratings of Average users on Av-Preferred items are low ratings or high ratings, the ratings are also natural noises. The ratings of Positive users on Preferred items will be considered natural noises if they are low or medium ratings. Table 4 summarizes the rules for detecting natural noises.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **Low Ratings** & **Medium Rating** & **High Ratings** \\ \hline (Negative users, No-Preferred items) & - & Natural noise & Natural noise \\ (Average users, Av-Preferred items) & Natural noise & - & Natural noise \\ (Positive users, Preferred items) & Natural noise & Natural noise & - \\ \hline \hline \end{tabular}
\end{table}
Table 4: The rules of detecting natural noises, where rating \(r_{ui}\) is determined whether it is noise based on the subcategories associated with the user \(u\) and the item \(i\).  
Figure 1: The distances between users (or item) and user (or item) categories. (**a**) User and its possible categories; (**b**) Item and its possible categories.
## 2 Materials and Methods
### Modeling Framework of Systems Mapping  
In this study, we incorporate EDIE within systems mapping to identify QTLs influencing the relationship among traits in light of environmental factors [26; 36]. The model's framework relies on a sequence of hypothesis tests across the entire genome. Specifically, in the hypothesis test to determine whether an SNP regulates the interaction growth, the null hypothesis means that the phenotypes of all genotypes are statistically consistent, and the alternative hypothesis indicates that phenotypes of mapping populations with different genotypes are statistically significant.  
Let us consider a mapping population of full siblings consisting of \(n\) individuals, each genotyped across \(p\) SNP markers. We assume that an SNP has \(J\) genotypes. \(\overrightarrow{y}_{i}=\left(\overrightarrow{y}_{1i},\overrightarrow{y}_{2i} \right)=\left(y_{1i}(1),y_{1i}(2),\ldots,y_{1i}(T);y_{2i}(1),y_{2i}(2), \ldots,y_{2i}(T)\right)\) represents phenotypic values for 2 traits of sample \(i\) over a period of time \((t=1,\ldots,\text{T})\). \(\overrightarrow{y}_{i}\) follows a bivariate normal distribution characterized by the mean vector \(\overrightarrow{\mu}\) and the covariance matrix \(\Sigma\). \(\overrightarrow{\mu}\) is fitted by EDIE, corresponding to parameter set \(\Omega_{\mu}=(K_{1},r_{1},\alpha,c_{1},d_{1},K_{2},r_{2},\beta,c_{2},d_{2})\). The matrix \(\Sigma\) is determinable from a parameter set \(\Omega_{\Sigma}\), employing the first-order structured endependence (SAD(1)) approach [37; 38]. \(\Omega=\left(\Omega_{\mu};\Omega_{\Sigma}\right)\) is used to represent the phenotypic value of the target mapping population. A hypothesis test on the parameter set \(\Omega\) is implemented using maximum-likelihood estimation (MLE). The calculation of the test statistic, the log-likelihood ratio (\(LR\)), is denoted as  
\[LR=-2\left(log\frac{L_{0}}{L_{1}}\right)=-2\left(log\frac{\prod_{i=1}^{n}f \left(\overrightarrow{y}_{i};\Omega\right)}{\prod_{j=1}^{l}\prod_{i=1}^{n_{j} }f_{j}\left(\overrightarrow{y}_{i};\Omega_{j}\right)}\right) \tag{5}\]  
where \(f(\overrightarrow{y}_{i})\) is a bivariate probability density function, and where \(L_{0}\) and \(L_{1}\) symbolize the likelihood functions corresponding to the null hypothesis and the alternative hypothesis, respectively. \(n_{j}\) represents the count of samples possessing the genotype \(j\)\((j=1,\ldots,l)\), and it satisfies \(\sum_{j=1}^{l}n_{j}=n\). During the resolution phase, the fourth-order Runge-Kuttamethod [39] is employed for solving EDIE, while the Expectation Maximization (EM) algorithm [40,41] is utilized for parameter estimation. Finally, to reduce false positives, the \(p\)-value is compared with the critical threshold after applying FDR correction using the Benjamini-Hochberg (BH) method.
## 2 Methodology
### Design
#### 2.1.4 Optimisation  
Topology optimisation was selected as it provides a conceptual geometry for design development. In this case, the design objective was to produce maximum stiffness at minimum mass.  
The size of the design space (Figure 3) was limited by physical realities during cycling. Firstly, the limb of the cyclist prevents any protrusions on the pedal-side of the crank so the design space was trimmed in accordance with the Q-Factor and an approximate foot width. The opposite side was made parallel to a hypothetical chain stay to prevent contact with the frame.  
HyperMesh 13.0 (Altair Engineering, Bristol, UK) was used to preprocess and mesh the geometry created in CAD. Genesis Design Studio 14.0 (GRM Consulting, Leamington Spa, UK) was used to perform the geometry optimisation. The Boundary Conditions (Section 2.1.2) were inputs into the model. The mechanical properties of Ti64 were used as the material of choice. Elements of the model were separated into Design Space (DES) (optimised) and Fixed Geometry (FIX) (non-optimised), given as red and blue, respectively. The model configuration can be seen in Figure 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **EOS Ti64** & **EOS MS1** \\ \hline Density (g/cm\({}^{3}\)) & 4.41 & 8.05 \\ Young’s Mod. (GPa) & 115 \(\pm\) 10 & 180 \(\pm\) 20 \\ Yield Strength (MPa) & 860 \(\pm\) 20 & 1990 \(\pm\) 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: EOS Material Properties [22; 23].  
Figure 3: Design Space (view from inboard side).  
Since AM enables freeform manufacture, no manufacturing constraints were imposed. The design objective was set to minimise strain energy (maximising stiffness). Mass was also defined as a design objective, the 'weighting' of which was increased with progressive iterations.  
A relatively coarse initial mesh was used to increase optimisation efficiency. Once reasonable results were obtained, the mesh was refined, increasing the number of tetrahedral elements 10-fold to 504,495 (average element size of 1.09 mm\({}^{2}\)), increasing the accuracy of the resulting isosurfaces (the surface form of the topology optimisation). Use of tetrahedral elements allowed for more effective packing of the complex solid body. This made an increased mesh resolution possible, which both increases accuracy of results and produces a more precise resulting surface for improved interpretation during CAD modelling.
# A Vision of 6 th Generation of Fixed Networks (F6G): Challenges and Proposed Directions
## 4 Optical Transmission: Key Challenges and Proposed Directions
### Capacity Increase Employing a Greater Number of Channels
#### 4.3.3 Summarizing the Benefits of UWB and SDM-Based Systems
A comparative study between UWB and bundles of C-band SMFs for their most important qualitative features is tabulated in Table 6.  
Concluding this sub-section, we wish to underline that using the currently available commercial components, it is possible to use UWB alongside SDM systems in a complementary way based on bundles of SMFs. SDM systems based on new fiber types (e.g., MCFs or FMFs) are expected to be further commercialized in the future, due to the immaturity of the technology, performance limitations, and the requirement for new fiber deployments that are very costly compared to their alternative solutions. In addition, UWB systems alone can be readily employed, extending the capacity of already deployed SMFs and providing a short- to medium-term cost-effective solution for network operators. However, there is a strong need to migrate to the spatial dimension at a rapid pace as it offers a theoretically unbounded capacity multiplier potential, allowing it to exceed 1 Pb/s rates, and can efficiently underpin the upcoming network transformation in order to support 6G and F6G services.
## 4 Fault Current Limiting Devices
### Non-Superconducting FCL
#### 4.1.4 Resistive Type FCL: Switch Type FCL (STFCL)  
An STFCL has been proposed in [14], and it is shown in Figure 7b. The STFCL, compared to the BTFCL, uses an inductor as a fault-current-limiting impedance instead of the BR. Furthermore, instead of using a thyristor bridge, a diode bridge has been employed in the STFCL, which decreases the installation cost. A snubber circuit has been used in order to utilize the inductor to suppress the transient over-voltage on the semiconductor switch, when it goes to the off state after the fault detection. After suppressing the first spikes by \(C_{f}\), which is smaller than \(C_{a}\), \(C_{a}\) absorbs the excess energy in the stator until its voltage reaches a steady state. Afterwards, the current pass, in the DC side of the diode bridge, is blocked and the inductor does its limiting operation. The STFCL's impact on the DFIG is the same as has been discussed for the non-controlled FCL located in the stator side.  
Figure 7: **(a)** The thyristor bridge type fault current limiter with bypass resistor (BTFCL-BR) [28]; and **(b)** the switch type fault current limiter (STFCL) with a snubber circuit [14].
# An 8 kb RRAM-Based Nonvolatile SRAM with Pre-Decoding and Fast Storage/Restoration Time
## 3 nvSRAM Peripheral Circuit Design
### Data Storage and Restoration Circuit  
The data storage and restoration circuit mainly includes a RRAM read/write drive circuit and decoding drive circuit, as shown in Figure 9. RVD is the system input, and MP6 and MP7 are MOS transistors of 2.5 V. The output of RBL0 is driven by controlling input signals such as Store, Program, and Restore. When Store, Program and Restore are low, MN5 turns on, and RBL0 is low, so the system maintains the normal operation mode of SRAM. When Store/Program is high and Restore is low, MP7 is on, and MN3 and MN5 are off. Then, RBL0 is pulled up to RVD due to MP7. While RWL is valid, RRAM is in set operation mode. Since Program becomes low, MN5 is turned on, and RBL0 is pulled down to low, which make RRAM enter reset operation mode. In case Store/Program is low and Restore is high, MN3 and MN5 are on, RBL0 is pulled down, and the system is in data restoration mode when RWL is valid.  
RRAM will share the address signal with SRAM during decoding. When RRAM performs set and reset operations, the voltage of RWL will be greater than the system power supply VD of nvSRAM, so RWL needs an independent voltage driving circuit. The driving circuit of RWL is shown in Figure 10[30]. MN0 and MP0 are 0.9 V MOS transistors. MN1, MN2, MN3, MP1, MP2 and MP3 are 2.5 V MOS transistors. Additionally MN1 is a MOS transistor with low threshold voltage, which isolates the voltage of Y and Z nodes to prevent device damage. While the input signals A, B and C are all VD, the voltage at X is VD. Then, the voltage at Y and Z is 0 through the inverter. Since MP3 is turned on, RWL is pulled up to VWL, and RRAM is driven to set or reset operation.
# Real-Time Anomaly Detection with Subspace Periodic Clustering Approach
## 4 Proposed Algorithm
After the execution, the match ratio for a cluster is obtained with the help of \(m\) and \(n\). If the match ratio is found to be 1, the corresponding cluster is fully periodic, else partially periodic. Each superimposed time interval produces a fuzzy time interval. This way, the fuzzy periodic clusters can be obtained. The pseudocode for the process is given below. The function \(\text{compsuperimp}(lt,lct)\) initially finds the intersection between \(lt\) and the core of \(lct\). If it is found to be non-empty, the function computes the superimposition process by reconstructing the membership values. If \(lt\) has been superimposed on \(lct\) it returns 1, otherwise it returns 0. get (0) and append () are functions operating on time interval lists to obtain a pointer to the next time interval in a list and to append a time interval, into a list, respectively. For each cluster, a counter (\(m\)) is also kept in order to keep track of how many time intervals are superimposed in one place. The match ratio is computed with the help of \(m\). If the match ratio is found to be 1, the corresponding cluster is fully periodic, else partially periodic. Finally, the fuzzy intervals can be generated with the help of superimposed intervals to get fuzzy periodic patterns. The flowchart for the proposed method is described in Figure 4 below. ```
Input: Set of clusters along with their lifespans (set of sequence of time intervals). Output: Set of fuzzy periodic clusters  Step 1. For each cluster \(c\) with list of linespans \(L\). Step 2. initially \(L\)=null//\(L\)c is the list of superimposed intervals  Step 3. \(lt\) = \(L\).get0 //\(lt\) points to the 1 st time interval (lifespan) in \(L\)  Step 4. \(Lc\) = append(\(lt\))  Step 5. \(m\) = 1 //\(m\) = number of intervals superimposed  Step 6. while((lt=L\).get0)!=null)  Step 7. [flag = 0  Step 8. while((lct =l\).get()!=null)  Step 9. if(compsuperimp(\(lt,lct\))  Step 10. flag =1  Step 11. if(flag == 0)  Step 12. \(Lc\).append(\(lt\))  Step 13. ] Step 14. ] Step 15. compsuperimp(\(lt,lct\))  Step 16. if(!intersect(\(lct,l\)!=null))
```  
**Algorithm 3**Algorithm for finding periodic (fully/partially) and fuzzy periodic clusters
Anomalies are data instances or groups of data instances that either belong to sparse clusters or don't fit the defined lifespans. As a result, a data instance may be anomalous depending on both its generation time and its distance from clusters.
# Optimal View Estimation Algorithm and Evaluation with Deviation Angle Analysis
## 5 Conclusions  
In this paper, we propose an image-based method for viewpoint estimation; verify that using a sampling point movement strategy to improve the uniform distribution of initial spherical viewpoints generated by analytical methods reduces the average distance of the maximum nearest neighbors; generate a small image dataset with viewpoint markers; and develop a calculation method for the accuracy of viewpoint estimation that integrates deviation angles. The viewpoint estimation experiments show that the accuracy of CNN-based estimation can be improved from 68.55% to 81.05% when the viewpoint angle deviation is \(\pi/6\); the proposed viewpoint estimation method can be used for analyzing the display angle of chairs on furniture websites.  
The main limitations of our method are that the CNN model in the algorithm framework is simple and lacks a comprehensive selection of various viewpoint estimation models, and the newly established dataset is too small. Although comprehensive selection is not the focus of this paper, it is a worthwhile direction for future research. If a comprehensive model selection is conducted and a more suitable machine learning model is found, the accuracy of viewpoint estimation can be improved. The scale of image datasets with viewpoint annotations provided in existing papers, including this paper, is still too small, so it is worth looking forward to generating large datasets and providing them to the public for use.  
Figure 14: The distribution of chair image viewpoints estimated using our method.  
**Author Contributions:** Conceptualization, M.Y. and H.L.; methodology, M.Y. and H.L.; software, H.L.; validation, M.Y. and H.L.; formal analysis, H.L.; investigation, M.Y. and H.L.; resources, M.Y. and H.L.; data curation, M.Y. and H.L.; writing--original draft preparation, M.Y. and H.L.; writing--review and editing, M.Y. and H.L.; visualization, M.Y.; supervision, H.L.; project administration, H.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** The newly created data and code are available at [[https://github.com/Hongjun2025/ImageViewpoint](https://github.com/Hongjun2025/ImageViewpoint)]([https://github.com/Hongjun2025/ImageViewpoint](https://github.com/Hongjun2025/ImageViewpoint)), access date: 12 March 2025.  
**Acknowledgments:** The author thanks the anonymous referees for their valuable suggestions.  
**Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.