esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Verification and Analysis
### Parameter Optimization of PLL  
Figure 23 shows the VSC output current waveforms before and after using the optimized PLL parameters selected in Section 3.2. As can be seen from Figure 23, before using the selected PLL parameters, the VSC output current oscillates severely. The oscillation frequencies are 175 Hz and 75 Hz. Using the selected PLL parameters at 4.055 s, the oscillation is significantly suppressed. Furthermore, the THD of the VSC output current is reduced from 90.09% to 0.65%.  
Figure 24 shows the \(dq\)-axis current waveforms before and after using the optimized PLL parameters. The frequency of the oscillation waveform is 125 Hz which corresponds to the frequency of the oscillation in Figure 23. It shows that the oscillations are effectively suppressed.  
It can be concluded that improving the anti-disturbance ability of PLL can improve system stability, and the stability evaluation method proposed in this article can effectively guide the design of PLL parameters.  
The stability evaluation method proposed in this article assists in the design of the parameters of the current control loop and PLL to suppress the oscillations caused by the current control loop or the PLL. It should be pointed out that the parameter optimization of the current loop and PLL are compatible, and they can be used either simultaneously or separately for oscillation suppression. If both the current loop and PLL cause instability of VSC, then both methods are used at the same time.
# Quality Assessment of Photogrammetric Methods--A Workflow for Reproducible UAS Orthomosaics
## 3 Results  
### Optimized Georeferencing Accuracy  
To evaluate the optimized georeferencing approach, the sparse clouds were iteratively filtered with decreasing RE thresholds. The sparse clouds of the six forest flights originally included between 560,000 and 680,000 tie points (Figure 3b) after the image alignment with a maximum initial checkpoint error of 3 m. The checkpoint errors were minimized to values between 0.021 and 0.046 m if a RE threshold of 0.4 m was used (Figure 3a). The corresponding pointclouds consisted of about 150,000 tie points. Further reducing the RE threshold to 0.1 m increased the checkpoint error (Figure 3b) due to an insufficient number of tie points for image alignments.  
In order to test the robustness of the method, the determined optimal RE threshold of 0.4 m was used to filter the sparse clouds of five identical computations of the six forest flights. The average controlpoint error in the horizontal direction was consistently below 0.02 m in all six flights and deviated less than 0.001 m in each of the five computations. The horizontal checkpoint error was between 0.02 m and 0.06 m over all six flights and deviated less than 0.01 m within the five computations. The error in the vertical direction (Z in Table 2) was up to five times higher; however, the reproducibility in each flight is still stable with a maximum deviation of 0.03 m over the five computations.  
Figure 3: (**a**) Checkpoint error in horizontal direction of the six forest flights with different reprojection error thresholds of the pointcloud filters. A reprojection error threshold of 0.4 m (red) led to the optimal checkpoint error of 0.067 m in the horizontal direction. The initial checkpoint error values of the sparse clouds without a camera optimization were 3 m on average. For better visibility, the y-Axis uses a log10 scale. (**b**) Number of points in the sparse clouds of the six forest flights with different reprojection error thresholds.  
In the grassland area, the iterative point cloud filtering only marginally improved the checkpoint error since almost all tie points had already very low RE of less than 0.4 m. The final checkpoint errors for the years 2013, 2015 and 2017 were 0.29 m, 0.18 m and 0.07 m, respectively. These errors are up to 10 times higher than in the forest time series, which is mostly due to the use of a conventional GNSS measurements for the GCP in the grassland compared to the RTK GNSS measurement in the forest. Nevertheless, the five computations of the grassland time series led to very consistent checkpoint errors with standard deviations close to 0 (Table 2).
# 3 GPP 5G V2X Error Correction Coding for Various Propagation Environments: A QoS Approach
###### Abstract  
Vehicle-to-everything (V2X) communications is an emerging branch of wireless communications and has recently gained a lot of interest not only from academia but also from the automotive and telecommunications industry. An important feature of V2X telecommunication systems is that the communication quality varies quickly in time due to the high mobility of the transceivers and due to the rapid change in the fading characteristics of the communication medium. For this reason, it is vital to maintain tolerable quality of service (QoS) in a V2X communication environment, in terms of communication reliability, power, end-to-end latency, data rate, communication range, throughput and vehicle density. A significant impact on the quality parameters of the system has been the coding scheme used by the V2X telecommunication system. For this purpose, we examine the effect that popular coding schemes have on the QoS of a V2X communication system. More specifically, the impact of fourth-generation long-term evolution (4G-LTE) turbo codes, fifth-generation new radio (5G-NR) polar codes and 5G-NR low-density parity-check codes (LDPC) on the QoS parameters of a V2X communication system is researched. To this end, we employ stochastic V2X propagation models that simulate every possible V2X channel state and traffic environment, based on the third-generation partnership project (3 GPP) Release 16 specifications. These propagation models form the basis of our study, since through them, at a first level, we analyze the frame error rate (FER) performance for different levels of signal-to-noise ratios (SNRs) for all the aforementioned coding schemes and 128-bit V2X-compatible data frames, and, at a second level, we investigate the impact of these propagation models and coding schemes on the V2X QoS parameters. Our analysis shows that turbo-based coding schemes satisfy all the QoS parameters and achieve overall communication quality comparable to polar and better than LDPC, making them suitable for small-frame 5G V2X services.  
5G-NR; 4G-LTE; V2X; turbo codes; LDPC; polar codes; stochastic V2X model; system simulation scenarios; QoS simulation scenarios; power; reliability; latency; throughput; vehicle density; data rate; communication range +
Footnote †: journal: _electronics_  
_Acticle_  
3 GPP 5G V2X Error Correction Coding for Various Propagation Environments: A QoS Approach
# Multi-Objective Decision-Making Meets Dynamic Shortest Path: Challenges and Prospects +
## 2 Dynamic Shortest Path  
The DSP problem is a generalization of the Shortest Path (SP) problem, having applications in many areas like transportation networks, data flow analysis, database systems, and network routing [4; 5; 6]. Hence, to enhance comprehension, we begin with a formalization of the SP problem before introducing the DSP one. The SP problem can be represented by a graph \(G=(V,E)\), where \(V\) is a set of vertices and \(E\) is a set of edges composed of the traversing cost. A path corresponds to a sequence of vertices connected by edges. In an SP problem, the goal is to find a path between a source vertex \(s\in V\) and a destination vertex \(d\in V\), whose cost (of the comprising edges) is minimum. Traditionally, finding the shortest path corresponds to finding the path with the shortest distance. However, to make this concept broader, here we refer to finding the route with the minimum cost (or optimal route), which better accommodates other cost definitions, such as distance, time, energy consumption, and comfort. Figure 1 shows an example of an SP graph where the shortest path regarding the edge costs is through the vertices \(s\to c\to d\).  
When an SP problem is subject to vertex insertion/deletion, edge insertion/deletion, or edge cost updates over time, the problem becomes a DSP problem [7]. This kind of problem is present in real-life applications, such as changes in route planning, as well as changes in the routing of a computer network. A naive way to approach the DSP is to run an SP algorithm from scratch whenever the graph changes. However, this approach is inefficient since the knowledge obtained in previous executions is never reused [8]. The next subsection shows the existing classes of DSP problems in the literature [9].
## 2 Systematic Literature Review (SLR)
### SLR Methodology  
In our review, we employed a structured and transparent methodology to conduct a Systematic Literature Review (SLR). The process involved four key stages: identification, screening, eligibility assessment, and inclusion. The review began with a clearly defined scope, focusing on recent studies published between 2018 and 2025 that explored biometric authentication using ML or DL approaches. A targeted keyword strategy was adopted using terms such as \"biometrics,\" \" authentication,\" \"recognition,\" \"deep learning,\" and \"machine learning\" to identify relevant studies. A total of 55 records were initially retrieved after removing duplicates.  
During the screening stage, titles, abstracts, and keywords were reviewed to exclude articles that were unrelated to biometric authentication, published outside the target time window, or lacked full-text availability. This process reduced the number of articles to 53. In the eligibility phase, full-text articles were assessed based on more specific criteria. Studies were excluded if they did not report the dataset used, did not name or clearly reference the dataset, did not employ ML or DL for user recognition, or failed to report performance metrics. After applying these criteria, 43 studies were found eligible and were included in the final synthesis and analysis.  
Key information such as biometric modality, dataset name, fusion strategy, classification method, and performance metrics was extracted from the included studies to support trend analysis and identify remaining research gaps. Figure 1 illustrates the complete article selection and filtering process following the PRISMA framework.
## 3 Results
### Distance Limit of Moving Target Detection and Resolution (Working Distance)  
The motion capture technology demonstrated the ability to track movement of a mobile target cyclically moving back and forth at 96 bpm. MO\({}^{2}\)CA successfully identified each individual revolution, out of a total of 10 revolutions per test of the moving target, over the range of 1 to 12 ft. Beyond 12 ft, MO\({}^{2}\)CA was unable to track each individual movement of the mobile target, with less than 10 revolutions being identified. The moving target had the greatest resolution, i.e., the lowest error rate, at 6 ft. The error was the highest at the extremes of distances studied, i.e., at 1 ft, and at 12 ft (Figure 9).
## 3 Results
There were no differences in age, medication intake, or fibromyalgia impact, as assessed by FIQ, between the longer duration of symptoms group (N: 15; mean age: \(52.07\pm 9.10\)) and the shorter duration of symptoms group (N: 14; mean age: \(59.36\pm 8.69\)) (see Table 3) using Mann-Whitney U-test and [PERSON]'s chi-squared test. The Mann-Whitney U-test was conducted to compare the group with less than 17 years with fibromyalgia symptoms (shorter duration of symptoms group) and the group with 17 or more years with fibromyalgia symptoms (longer duration of symptoms group) in the different EEG frequency bands (theta, alpha, beta-1, beta-2, and beta-3). Main significant differences were obtained in the theta power band. After applying the multiple comparison correction [28], the theta values in the frontal (Fp1, F4, Fz, F8, and F7), central (C3, Cz and C4), and parietal (Pz and P3) areas were significantly higher (\(p\)-value \(<0.05\)) in the group that was suffering from fibromyalgia for more years (see Figure 1). \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Shorter Duration of** & **Longer Duration of** \\
**Variable** & **Fibromyalgia** **Symptoms (N = 15)** & **Fibromyalgia** **Symptoms (N = 14)** \\  & **Mean (SD)** & **Mean (SD)** \\ \hline Age (years) & 52.07 (9.10) & 59.36 (8.69) \\ Year since initial diagnosis & 10.27 (4.92) & 31.86 (10.61) \\ FIQ-100 & 51.65 (17.74) & 44.75 (12.87) \\ Stiffness & 6.13 (2.53) & 4.21 (2.64) \\ Sadness & 4.00 (2.75) & 3.07 (3.02) \\ Pain & 6.00 (2.39) & 6.00 (2.25) \\ Anxiety & 5.53 (2.56) & 5.43 (2.79) \\ Education level (n of patients) & & \\ Able to write and read a & 0 (0\%) & 2 (7.1\%) \\ Primary education & 7 (46.70\%) & 7 (50.00\%) \\ Secondary education & 7 (46.70\%) & 5 (35.7\%) \\ Higher education & 1 (6.70\%) & 1 (7.1\%) \\ Medication (n of patients) & & \\ Antidepressants & 7 (46.7\%) & 4 (28.6\%) \\ Analgesics/relaxants & 7 (46.7\%) & 3 (21.4\%) \\ Complementary physical therapies & & \\ Massages & 2 (13.3\%) & 3 (21.4\%) \\ Physiotherapy & 5 (33.3\%) & 3 (21.4\%) \\  & Comorbidities & \\ Type 2 diabetes & 0 (0\%) & 2 (14.3\%) \\ Hyperlipidemia & 2 (13.3\%) & 5 (35.7\%) \\ Hypertension & 2 (13.3\%) & 5 (35.7\%) \\ \hline \hline \end{tabular}  
* Able to write and read but without formal education. FIQ: Fibromyalgia impact questionnaire; SD: Standard deviation.  
\end{table} 
Table 3: Descriptive measures of lower and longer duration of symptom groups. No other significant between-group differences were found in the rest of EEG frequency bands (see Figure 2 for the alpha power spectrum).
## 5 Methodology
### Method  
To avoid the experimental comparisons of all the combinations of inner and outer loops, in the first step, we identified the internal loop with the best performance. For these experiments, we used a proportional force controller for the external loop. As demonstrated later in this paper, the best results were obtained with an inner velocity loop. In the following step, the behaviours of several external regulators were compared. The best inner loop identified in the first step was applied.  
Regarding the identification of the optimal inner loop, in this work were considered the position and velocity loops in cartesian space. In the case of the position, both proposals, absolute and incremental, were treated. Also, in all the cases, the stiffness was evaluated.  
Figure 4: Experimental setup.  
Next, we explain the commands used to implement the inner loops. In the case of position loops, we used the command script novel,  
\[\textit{movel}(x\textit{ vector},a,v,t,r).\]  
where \(x\textit{ vector}\) represents the cartesian position to be reached by the end-effector, \(a\) is the acceleration, \(v\) is the velocity, \(t\) is the time, and _radius_ is the blend radius. This command controls the position in the cartesian space in each sampling period. If the variable time is specified, the command will ignore the velocity and acceleration values. In our case, it was necessary to specify a time of 8 ms as a sampling period. Also, it was necessary to implement an external trapezoidal trajectory generator to specify the velocity and acceleration of the movement on the Y-axis. This trajectory generator was implemented with script commands.  
In the case of the velocity loop, we used the command script speedl,  
\[\textit{speedl}(v\textit{ vector},a,t).\]  
where \(v\textit{ vector}\) represents the cartesian velocity to be reached by the end-effector, \(a\) is the acceleration, and \(t\) is the time. This command controls the velocity in the cartesian space. We can indicate a specific cartesian velocity in each degree of freedom. However, we only can indicate a global acceleration. In this case, it was not necessary to specify the time because the command was applied in each sampling period.  
Then, the inner loop with the best performance was used with different outer force loops. A comparison between PD and PV algorithm is shown to study the effect of the damping action in the interaction task. In addition, the integral control action is compared with the feedforward action, in order to study which alternative is more appropriate in practical applications. All these results were obtained using the aluminium probe tool to avoid the vibrations of a real polishing tool.  
Finally, a real application is shown. A polishing task was performed with the polishing tool and with the best force control obtained. The task was performed with and without the force control to compare results. The gains, applied in the different control algorithms, were determined experimentally. These values were found after several experiments and analysis of the force response. The best values of these gains are exposed in this work.
# MPResiSDN: Multipath Resilient Routing Scheme for SDN-Enabled Smart Cities Networks
## 4 MPResiSDN System
### System Algorithm
In addition, the algorithm uses two nested four loops to construct rules, which incur a total algorithmic complexity of \(O(ps)\), where \(p\) represents the number of paths and \(s\) represents the number of switches inside a path. Therefore, our algorithm's complexity is \(O(n^{2}+ps)\), which can be simplified to \(O(n^{2})\) since \(p\) and \(s\) cannot be larger than \(v\). ```
Functions: TopologyDiscovery(): determines the network topology as a graph \(G\) ChallengeDetector(): decides the value of \(k\) based on environmental sensors. DiversePaths(\(k\), \(src\), \(dst\), \(G\)): determines \(k\) paths between source \(src\) and destination \(dst\) in a graph \(G\). FindSwitches(\(path\)): generates a list of all switches in a given path. DetermineInOutPorts(\(sw\), \(path\)): determines input and output ports for a switch \(sw\) in a given path. ReversePath(\(Path\)): determines the opposite direction of a path. GenerateRule(\(sw\), \(in\), \(out\)): generates an SDN rule for a switch \(sw\), given ports \(in\) and \(out\). Input: src: source node. \(dst\): destination node. Output: \(RulesList\): SDN rules. begin \(G\) = TopologyDiscovery() \(k\) = ChallengeDetector() \(Paths\) = DiversePaths(\(src\),\(dst\),\(G\),\(k\)) \(SDNRules\) = [] forpath in Pathsdo for\(sw\)in FindSwitches(\(path\))do in, \(out\) = DetermineInOutPorts(\(sw\), \(path\)) \(SDNRule\) = GenerateRule(\(sw\), \(in\), \(out\)) \(RulesList\).append(\(sw\),\(SDNRule\))  end for reversedPath = ReversePath(\(path\)) for\(sw\)in FindSwitches(reversedPath)do in, \(out\) = DetermineInOutPorts(\(sw\), \(path\)) \(SDNRule\) = GenerateRule(\(sw\), \(in\), \(out\)) \(RulesList\).append(\(sw\),\(SDNRule\))  end for return\(RulesList\)  end for
```  
**Algorithm 1**MPResiSDN algorithm.
# From Analogue to Digital Photogrammetry: Documentation of Padise Abbey in Two Different Time Stages
## 3 Re-Processing of Original Images
### Accuracy Assessment  
Accuracy checking is an integral part of photogrammetric processing. We have used all the available information that we found to provide a proper accuracy check. In the original survey, four distances were measured for scaling. We used the longest one for scaling and adjustment of our relative orientation. The remaining three distances we used as check distances and we calculated them in our photogrammetric model. The differences between measured value and calculated value are presented in Table 2. On check distances M1-M2 and M2-M3, there is a difference 0.005 m. This denotes a proper scaling of relative orientation. Check distance M5-M6 has a difference around 0.02 m. This distance is visible only on stereo-pair A5-B5, and markers M5 and M6 are placed out of the south wall.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Input Value** & **Calculated Value** & **Difference** \\ \hline c (mm) & 64.41 & 64.22 & 0.19 \\ pp\(\times\) (mm) & Unknown & 0.047 & x \\ pp\(\times\) (mm) & Unknown & 0.052 & x \\ \hline \hline \end{tabular}
\end{table}
Table 1: Resulted parameters of interior orientation (camera constant c and position of principal point PP\(\times\) and pp\(\times\)) by self-calibration. The calculated values are compared to the input values.  
Figure 6: Resulted point cloud of the south wall created by re-processing with relative position of captured images.  
In the original processing, distances between each image of each stereo-pair were measured. We used this information for an accuracy check. We calculated the distances from computed exterior orientation of images and compared them to the measured values from the original survey (see Table 3). The difference between images of stereo-pair A4-B4 is the largest (0.021 m). This stereo-pair captures the south wall from a farther distance (approx. 19 m farther) and the tie points are not well-distributed in the images, which could cause worse accuracy. The remaining differences do not exceed 0.007 m.  
All the presented distances had direction more or less parallel to the \(X\) axis. The results show the accuracy of scaling the photogrammetric model in one axis. To have a proper accuracy check, we should check the distances in different directions (\(Y\) axis and \(Z\) axis). Since we did not have measured distances from the original measurement in different directions, it was not possible.
# Quality Evaluation of Wasteless Mining in Dongguashan Based on Intuitionistic Fuzzy Set and VIKOR
## 3 Intuitionistic Fuzzy Sets and VIKOR Method Theory
### VIKOR Method  
The multi-criteria compromise solution ranking (VIKOR) method is a multi-attribute decision-making method. It is characterized by the maximization of the group benefits and the minimization of individual regrets of opposing views [33]. The detailed decision-making steps are as follows:  
(1) Based on the group decision evaluation information, the positive ideal solution\(f_{j}^{+}\) and the negative ideal solution\(f_{j}^{-}\) are determined:  
\[f_{j}^{+}=[(\underset{i}{\text{max}}f_{ij}|j\in B),(\underset{i}{\text{min}}f_ {ij}|j\in C) \tag{5}\]  
\[f_{j}^{-}=[(\underset{i}{\text{min}}f_{ij}|j\in B),(\underset{i}{\text{max}}f _{ij}|j\in C) \tag{6}\]  
where \(f_{ij}\) is the \(j\)th attribute value of the \(i\)th evaluation object; \(B\) is the benefits set; and \(C\) is the cost-type set;  
(2) The group utility value \(S_{i}\) and individual regret value \(R_{i}\) are calculated for each evaluation object;  
\[S_{i}=\sum_{j=1}^{m}w_{j}(f_{j}^{+}-f_{ij})/(f_{j}^{+}-f_{j}^{-}) \tag{7}\]  
\[R_{i}=\underset{j}{\text{max}}[w_{j}(f_{j}^{+}-f_{ij})/(f_{j}^{+}-f_{j}^{-})] \tag{8}\]  
where \(w_{j}\) is the weight of each evaluation index; \(S_{i}\) and \(R_{i}\) are both cost-based variables, the smaller the value of the variable, the better the benefit of the evaluation object;  
(3) The tradeoff value \(Q_{i}\) is calculated, the smaller the \(Q_{i}\), the better the comprehensive benefit of the evaluation object, and the decision coefficient is \(v\in\) [0,1]:  
\[S^{+}=\underset{i}{\text{min}}\ S_{i},S^{-}=\underset{i}{\text{max}}\ S_{i},R^{+}= \underset{i}{\text{min}}\ R_{i},R^{-}=\underset{i}{\text{max}}\ R_{i}\]  
\[Q_{i}=\frac{vS_{i}-S^{+}}{S^{-}-S^{+}}+\frac{(1-v)R_{i}-R^{+}}{R^{-}-R^{+}} \tag{9}\]  
where \(v\in\) [0,1] is the decision mechanism coefficient, and it indicates the preference of the decision maker for group opinion and individual regret values. If \(v\) > 0.5, it means that the decision-maker prefers comprehensive overall benefits; if \(v\) < 0.5, the decision-maker prefers individual regrets; and if \(v\) = 0.5, there is no significant preference for the decision-maker;  
(4) On the basis of \(S_{i}\), \(R_{i}\) and \(Q_{i}\), the evaluation index level is determined, and then the object to be evaluated is rated.