esa-sceva/satcom-chunk-collection huggingface dataset:
# SVDC: Synchronous Update with the Division and Combination of SRv6 Policy
## 4 Policy-Dividing Algorithm in SUDC
### Algorithm Overview  
The core idea of this algorithm is to divide the SID lists by identifying the longest common prefix of different SID lists. The longest common prefix refers to the longest common SID block starting from the first SID. In the example given in Section 3, SID block 1->2->3->6->7 is the longest common prefix of policy A's SID list and policy B's SID list. The policies perform prefix matching sequentially in descending order of SID list length. When the complete SID list of a shorter policy is the prefix of the SID list of a longer policy, it becomes possible to obtain two entirely identical SID sequences and the suffix sequence of the longer SID list by partitioning the longer SID list. As shown in Figure 3, the identical sequence is abstracted to be wrapped into a policy distribution SID block, which will be distributed to target nodes set in a multicast manner. The suffix sequence of the longer SID list can go for prefix matching with the SID list of other policies.  
In this algorithm, if a SID list is the prefix of k SID lists at the same time, then this SID list is abstracted and wrapped into a policy distribution SID block. This policy distribution SID block will be multicast to k+1 target nodes. And the remaining k suffix sequences continue to wait to be matched. In the worst case, none of the k suffix sequences can be matched. Then, all these suffix sequences will be distributed to corresponding target nodes in unicast form. The total number of times for policy distribution is k + 1. This algorithm ensures that the number of times for policy distribution in SUDC is always less than or equal to that in the conventional unicast-based sequential distribution method.
# Blockchain Technology for Intelligent Environments
## 5 Blockchains for IoT Systems
### Data Delivery and Storage
#### 5.2.3 Cyber-Physical Chain  
_Cyber-Physical Chain_ (_CPCChain_) is a data platform for IoT systems supporting data acquisition, storage, sharing, and applications [73]. It separates the data, contract, and application layers from control, which supervises data interaction through a blockchain. Raw data are encrypted on the user side and stored in a distributed hash table (DHT). Only hash values, as unique identification of data, and credentials for integrity and correctness are published on the blockchain. Parallelization is supported by separating data storage from control. Re-encryption and homomorphic encryption technologies are combined to support one-to-many authorization.  
CPChain plans to develop a hybrid consensus model that combines dynamic committee election with PoW consensus. The election process is performed in rounds. A node is eligible for election only if its credibility is above a threshold. However, it is not specified how a node's credibility is determined and updated.  
To address the different requirements of IoT applications in terms of delay and security, sidechains implementing lightweight consensus protocols are proposed. For industrial scenarios, altruistic cooperative models are proposed.
## 3 Proposed Method for Attribute Weighting
### Proposed Method for Attribute Weighting Based on PCA and Entropy  
The proposed method is a hybrid strategy with an objective focus that merges the strengths of two methods employed in attribute weighting: Principal Component Analysis (PCA) and the Entropy Method. The integration of these two approaches not only reflects a solid mathematical foundation but also leads to a more accurate and meaningful weighting of attributes. This combined process optimizes decision quality in complex environments by providing a more holistic and detailed understanding of the elements involved.  
Principal Component Analysis transforms the original dataset, generating a new set of data. Each attribute in the new dataset offers insights into the relationship between the original attributes, considering the highest percentage of explained variance from the original dataset. In scenarios where expert opinions are unavailable, this information serves as the basis for constructing the decision matrix. Each element in this matrix includes scores  
Figure 1: Multicriteria decision-making process.  
that reflect the performance of each attribute in relation to the established objectives. The introduction of the Entropy Method in this process brings significant benefits, as it allows for the measurement of uncertainty by assessing mutual information between attributes and capturing non-linear relationships. This integration contributes to a more informed and precise decision-making process.  
The method that combines PCA and Entropy unfolds in two phases to optimize the evaluation of attributes in multicriteria decision-making problems. In the first phase, the construction of the decision matrix begins, utilizing information derived from PCA and focusing on the matrix of eigenvectors. This initial step lays the groundwork for understanding the data structure and highlighting the most relevant features. In the second phase, determining attribute weights takes priority through the application of the Entropy Method. In this stage, the focus is on quantifying information and reducing uncertainty associated with each attribute. The sequential application of PCA and Entropy enables a comprehensive and detailed evaluation of attributes, providing a more precise and enriched weighted assignment of the attributes involved. In Phase 2 of Figure 1, the fundamental steps of the proposed method for attribute weighting are presented, which is based on Principal Component Analysis and the Entropy Method.
## 4 Bibliometric Analysis
### Overview of the Publications  
#### 4.1.1 Annual Analysis of the Publications  
The authors screened papers from the online database Scopus between 2013 and 2023, analyzing a total of 305 papers for bibliometric analysis. Figure 3 shows the number of papers per year. The figure shows a low publication rate from 2013 to 2015, with fewer than five papers published per year. After 2016, the number of papers published per year began to increase rapidly, fluctuating between 10 and 20 between 2016 and 2019. In 2020-2021, the number of papers published increased dramatically, jumping to between 40 and 50, while in 2022-2023, the number of papers was between 70 and 80, accounting for about 58.52% of the total number of papers published at that time. In 2024, the publication rate also showed an upward trend. This clearly shows that researchers are beginning to invest more effort in this field of study, and the analysis predicts a significant increase in the number of papers in this field to continue.  
The authors also analyzed the number of citations to the papers by year. Figure 3 shows the distribution of citations per year for papers. The figure shows a consistent upward trend in citations over time. The figure shows that the authors have divided the time span from 2013 to 2023 into three stages, with the first stage (2013 to 2016) showing fewer than 80 citations per year, for a total of 1752 citations. This is only 14.34% of the total number of citations. In the next stage (2017-2020), the number of citations showed a rapid increase. The highest number of citations in this stage is 2734 in 2020, followed by 2021 in 2017. In this stage, the total number of citations is 7601, which represents 62.20% of the total number of citations. In the last stage (2021-2023), there is a decrease in the number of paper citations, with a total of 2868 citations. This stage accounted for approximately 23.47% of the total citations. This suggests that the field has experienced significant growth in recent years, with the literature from 2017 to 2020 making a significant contribution to the field. The authors argue that the lower number of citations to papers from 2021 to the present does not mean that this stage of research has stagnated, as the number of citations in the literature in this stage is still significantly higher than in the first stage and is similar to the average number of citations per year in the second stage. The literature of the last stage, which was published later and over a shorter period of time (only two or three years), resulted in a relatively low number of citations.  
Figure 3: Summary of annual characteristics of the papers.
## 3 Phase-Bias Results Assessment
### Comparison of the CNES Phase Biases
The first row is dedicated to the code biases and the second one to the phase biases. The median of the standard deviations of the differences between CNES and SGG is expressed in units of distance (centimeters) to evaluate the impact on the pseudorange and carrier-phase measurements. In Figures 9 and 10, the left column is the difference between the CNES RT products and SGG ones, whereas the right column is for the comparison between CNES POST and SGG products. Since the real-time OSB computed by CNES are not in the set of the OSB computed by SGG for Galileo, Figure 11 displays only the stability comparison for the POST products. For Glonass, the RT code biases could only be computed. On the graphs, each color represents a given signal. Some satellites may have missing values if they are not found in either the CNES RT, the CNES POST, or the SGG products. Since the CNES POST and the SGG code biases are constant, the mean-adjusted values are also constant, and their standard deviations are zero. This is not the case for the RT products. It has to be noted that the standard deviations of the difference are smaller for the code biases than for the phase biases, meaning that the latter is less stable than the former, which is in accordance with the conclusions drawn by [42]. For GPS, the POST phase bias difference with the SGG ones is more stable than their RT counterparts. For Beidou, this is the case only for the Beidou-3 satellites. For Beidou-2 ones, the POST phase biases are less stable than the RT ones. For Galileo, extremal values are reached for the E09 satellite. This is caused by a sudden decrease in the phase bias value at the end of the time series for this spacecraft. The post-processed standard deviation over five days is around 2 cm for GPS and Galileo (except for the extreme case described above) and 4 cm for the Beidou-3 satellites.
## 3 Results and Discussion
### Atomic Force Microscopy Results  
Figure 7a-c illustrate the topography and corresponding height profile of HEA coatings with a higher Cu content (I8-7, 10% Cu), characterized as having the highest corrosion resistance compared to coatings with lower Cu content (I8-6, 5% Cu). The surface morphology, evaluated using atomic force microscopy (AFM), reveals a homogeneous granular structure across all samples, with distinct variations in roughness depending on the deposition power during the sputtering process. The coating deposited at 200 W exhibits the smoothest surface, with a root mean square roughness (RMS) value of 1.62 nm. This smoothness is attributed to the lower deposition rate, which allows for an adequate surface diffusion of atoms, promoting finer grain structures and reducing void formation.  
Figure 5: Diffraction patterns of as-cast alloy with Cu 5 wt. % and as-deposited coatings obtained by magnetron sputtering.  
Figure 6: Diffraction patterns of as-cast alloy with Cu 10 wt. % and as-deposited coatings obtained by magnetron sputtering.  
In contrast, the coating deposited at 250 W displays a slightly higher rms value of 2.32 nm, while the coating produced at 300 W demonstrates the highest roughness, with a rms value of 2.74 nm. The increased deposition power at 300 W results in a higher deposition rate, which accelerates the accumulation of atoms on the substrate, limiting the time available for atomic rearrangement and leading to the formation of larger grains and more pronounced voids between them. Coatings deposited from I8-6 (5% Cu) and I8-7 (10% Cu) HEA targets demonstrate similar topography and comparable granular size and roughness when considering the same applied magnetron sputtering power, as seen from the comparison in Figure 7c,d.  
All coatings exhibited a granular structure on AFM images, which is in line with the previously discussed obtained XRD results, suggesting that deposited films have a nanocrystalline structure. It is also seen that with the increase in the sputtering power, the size of the grains on the surface slightly increases. It is important to mention that the deposition time of the coatings at 300 W was 33% shorter than the ones deposited at 200 W, and the resulting thickness is 15-25% lower, respectively.
## 4 Experiment and Results
### Result on Cityscapes  
We also experimented with the Cityscapes dataset [22], a real-world dataset. The dataset contains 3475 annotated images of 20 classes and is split into 2975 data for training and 500 for testing. The segmentation maps are resized for the network from \(2048\times 1024\) to \(512\times 256\). To obtain the static segmentation map from Cityscapes, we cropped a 256\(\times\)256 static map from the resized map with a difference of 10 pixels. A total of 977 segmentation  
Figure 8: Supervised learning-based Media Integration and Communication Center Semantic Road Inpainting (MICC-SRI) qualitative results: (**a**) input map, (**b**) ground truth, (**c**) deep convolutional generative adversarial network (DCGAN) [20], (**d**) segmentation adversarial network (SegAdv) [29], (**e**) adversarial entropy network (AdvEnt) [27], and (**f**) semantic segmentation inpainting network (SSIN).  
maps were extracted, as presented in Figure 9. In contrast, the 977 dynamic maps were randomly selected, except for the map used for extracting the static map. The annotation class is redefined as noted in Section 4.  
We trained our method on the real-world Cityscapes dataset using only the segmentation map. The experiment was conducted using the DCGAN and our method. As shown in Figure 10, our method inpaints the dynamic objects with static objects using the map's nearby segmentation labels and structural information. In the first example in Figure 10, the DCGAN replaces the person on the left side with a chair label, which cannot be found on the map. In contrast, our method used the map information, filling in with the building and sidewalk labels. This is also shown in the second map, where the DCGAN fills the bus with stairs, whereas ours fills it with nearby vegetation. In addition, we made 30 ground truths to compare qualitatively and report the result in Table 5, which shows that our method outperforms the DCGAN in all metrics.
# Effects of Refiner Plates with Different Fillings on TMP Properties
## 3 Results and Discussion
### Comparison of Shive Contents of TMP and CTMP Made by the Plates with Different Patterns  
Figure 5 indicates that there was no remarkable difference in shive contents between TP A and TP B, suggesting that different plate patterns had no noticeable effect on shives left in both TMP and CTMP at the same freeness level. Shives are relatively large particles or bundles of fibers, and it was assumed that they might not be as sensitive to changes in plate pattern as other types of contaminants or fines. Furthermore, other factors, such as the type and quality of the raw materials used in mechanical pulp production, the refining conditions, and the overall pulp processing methods, may greatly influence shive content compared to plate pattern alone.  
In contrast, the shive content of CTMP was up to 1.66% lower than that of TMP, likely due to the use of NaOH and Na\({}_{2}\)SO\({}_{3}\) in the CTMP impregnation stage, which could soften lignin and hemicellulose and make fibers more easily separated during refining.
## 3 Signal Generation Method and Circuit Design Based on Time-Interleaving
### Circuit Design  
Based on the above analysis, this circuit selects the AK4490 high-resolution DAC chip from AKM Company. The channel switching module selects the multiplexer chip. The output of the multiplexer is sampled and held by the sample-and-hold. Then, a high-order low-pass filter for filtering processing is designed, so that the output waveform of the system changes from a ladder to a smooth waveform. By controlling the channel switching of the multiplexer, the output step number of the entire system becomes 4 times that of the original single DAC output step number, that is, the sampling rate of the original DAC is increased by 4 times. Figure 6 shows the overall design of the TIDAC-based high-resolution and high-speed signal generation circuit.  
In the control module, the field programmable gate array (FPGA) provides digital quantities to the 4 DACs of the TIDAC module; the TIDAC circuit mainly includes a DAC circuit, a reference power supply circuit, a channel amplitude control circuit and a channel switching circuit. The reference voltage of each DAC in TIDAC is the same and all work at the maximum sampling frequency. Then, the 4 DACs respectively output the corresponding analog waveforms, then respectively pass the corresponding 4 channel amplitude control circuits to fine-tune the amplitude and then output to the multiplexer for selection output. The analog signal output by the TIDAC circuit enters the sample-and-hold circuit and is sample-and-held output at a certain frequency; the waveform output by the sample-and-hold circuit is a ladder wave, which is filtered by the filter circuit to finally output a smooth waveform. The power supply module is the power supply module of the system, and all the device work is provided by it.
# Nearly Linear-Phase 2-D Recursive Digital Filters Design Using Balanced Realization Model Reduction
###### Abstract
The approach proposed in this paper differs in the sense that it ensures almost linear phase in the passband. Methods satisfying such specifications have been few [6; 8; 14; 18; 20; 29; 39; 40]. The paper introduces a new method for designing nearly linear-phase 2-D IIR filters by leveraging a balanced realization model reduction technique, with a focus on controllability and observability Gramians to ensure stability and reduced coefficients, demonstrating favorable results in numerical examples. The contributions of this paper in the area of 2-D IIR filter design include the emphasis on achieving nearly linear phase in the passband, the use of balanced realization model reduction, the incorporation of 2-D FIR filters, the utilization of structured Gramians, efficient computation method for Gramians through LMIs, stability preservation of the obtained filter, and the reduction of the number of filter coefficients. Additionally, the paper's comparison with existing techniques highlights its contribution to the field of 2-D IIR filter design. The paper is organized as follows: In Section 2, the definition of structured controllability and observability Gramians of 2-D discrete systems is presented. Section 3 presents an LMI-based algorithm for computing the structured controllability \(P^{s}\) and structured observability \(Q^{s}\) Gramians. In Section 4, a design procedure for a balanced realization model reduction technique for separable-denominator 2-D IIR digital filters is proposed. To demonstrate the effectiveness of the suggested approach, several examples, including plane wave filtering using a 2-D fan filter, are provided in Section 5. Section 6 concludes the paper.
###### Abstract  
The multi-beam array stitching test system (MASTS) based on the Hartmann principle is employed to measure the aberrations in large-aperture optical systems. As each small-aperture and ideal parallel beam traverses the optical system, it is converged into a spot at the focal plane of the optical system. The centroid position of the spot contains the information about the wavefront slope of the sub-aperture at that specific location in the optical system. Scanning the optical system with this small-aperture beam across the entire aperture of the optical system, we can yield the aberration information to be tested. To mitigate pointing errors induced by scanning motion and accurately obtain the aberration signals of the optical system, nine beams are integrated into a 3 \(\times\) 3 multi-beam array system, and their directions are aligned to be identical. However, achieving complete alignment in the same direction for all nine beams is a challenging task, resulting in errors due to their pointing differences within the array. This paper introduces an iterative algorithm designed to obtain high-precision multi-beam pointing errors and to reconstruct the wavefront of the optical system under test. This enables a more accurate measurement of wavefront aberrations in the optical system to be tested. Firstly, simulation models were implemented to validate the algorithm's feasibility. Additionally, a scanning optical measurement system with a multi-beam array was developed in our lab, and the iterative algorithm was applied to process our experimental data. The results were then compared with interferometer data, demonstrating that our algorithm is feasible for MASTS to measure aberrations in large-aperture optical systems with high accuracy.  
scanning Hartmann; large-aperture optical systems; aberration measurement; sub-aperture stitching; iterative algorithm +
Footnote †: journal: Applied Sciences  
Article
# A New String Edit Distance and Applications
## 2 Restricted Forensic Levenshtein Distance
### Implementation of the RFL Distance
#### 2.4.3 Practical Implementation  
The RFL algorithm is written in Python, compatible with NumPy and therefore Numba, which increases the speed over the same algorithm in pure Python by a factor of about 30 via an @njit wrapper. However, the Numba-optimized version of the algorithm only works if including the possibility of stuttering a single motif, due to limitations in the implementation with Numba's inherent dictionary types. Based on testing of the weighted-levenshtein package (written in Cython) [32], the RFL algorithm has potential for significant increases in speed. In preliminary testing, modifying the data structures to integer-coded arrays and rearranging the way the cost dictionaries are stored can accelerate the computations by more than an order of magnitude via Numba. Furthermore, preliminary testing of a Numba prototype for the base Levenshtein algorithm suggests that in the case of DNA, Numba can meet or exceed the speed of the generic Cython package.  
```
1 foreveryrowmfrom1 toparent_length:
2 foreverycolumnnfrom1 tothechild_length:
3
4 options=[]
5
6#substitution:lookupandtotheleftasinglestep
7 options.append(d[m-1,n-1]+costofchangingcharactermoftheparentintotcharactermofthechild)
8
9#insertion:lookupbackonecolumn
10 options.append(d[m,n-1]+costofinsertingcharacternofthechild)
11
12#deletion:lookupuponerow
13 options.append(d[m-1,n]+costofdeletingcharactermoftheparent)
14
15#forwardstutter:lookupback1,...,2k-1 columns,orbacktothefirstcolumn
16 if2k-1 stepswouldbeoutofrange
17
18 foriin1,...,min(lookupback,n):
19
20 foreverymotifbeingconsidered:
21
22 ifSisnottoolongfortheforward-stutterdictionaryforthismotif:
23
24 options.append(d[m,n-i]+costofinsertingS(fromforward-stutterdictionary))
25
26#backstutter:lookupback1,...,2k-1 rows,orbacktothefirstrowif2k-1 stepswouldbeoutofrange
27 foriin1,...,min(lookuphead,m):
28
29S=theicharactersimmediatelyprecedingandincludingcharactermoftheparentstring
30
31 foreverymotifbeingconsidered:
32
33 ifSisnottoolongfortheback-stutterdictionaryforthismotif:
34
35 options.append(d[m-i,n]+costofdeletingSfromback-stutterdictionary
36
37#thedistancefromthefirst=charactersoftheparenttothefirstmcharactersofthechildisattainedbytakingthemallestoftheoptionsatthisstep
38d[m,n]=min(options)
```  
Listing 2: Building the dynamic programming matrix in the RFL algorithm.