esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Six Active Power Control Strategies of Interconnected Grids with VSC-HVDC
## 5 Transient Stability Simulation Results with Six Different Active Power Control Schemes
### Angle Spread Evaluation with PGs  
In order to evaluate angle spread, the Overcorrections index, which is generally used for control response determination, is introduced in this paper. Assume that \(x_{initial}\) is the original steady state value of variable \(x\), \(x_{F}\) is the first encountered peak of \(x\) during a transient event, and \(x_{T}\) is the second encountered peak of \(x\) during a transient event.  
\[x_{0}=\left|\frac{x_{initial}-x_{F}}{x_{initial}}\right|+\left|\frac{x_{initial} -x_{T}}{x_{initial}}\right| \tag{12}\]  
\(x\) is defined in this paper as the angle spread. Thus, the larger \(x_{0}\) is, the worse the angle stability of the PGs, and if \(x_{0}\) is large enough, there is a possibility of loss of synchronisms. The locus of \(x_{0}\) as well as the brief conclusions are well illustrated with each scheme hereafter.
## 2 Algorithms
### Backward Oracle Matching, BOM  
BOM is similar to B(N)DM, but the suffix automaton of the reversed pattern is replaced by a simpler deterministic automaton, the factor oracle [8]. The factor oracle of a string \(x\) (which corresponds to the reversed pattern \(\overleftarrow{p}\)) of length \(m\) has the following properties.  
1. If \(y\) is a factor (substring) of \(x\), then there exists a path spelling \(y\) from the start state to some state which is not the FAIL state; we say that \(y\) is _recognized_.
2. The only string of length \(m\) recognized is \(x\).
3. It has the minimal number of states (\(m+1\)) necessary for recognizing \(x\) (omitting the FAIL state).
4. It has between \(m\) and \(2m-1\) transitions (omitting those into the FAIL state).  
It may recognize more strings than the substrings of \(x\) (although in practice not many more), but is easier to construct. It still guarantees that, once the FAIL state is reached, the sequence of read characters is not a substring of \(x\). We refer to [8] for the construction details and further properties of the oracle; an example is shown in Figure 1.  
The cost function \(\xi^{p}(w)\) is the number of characters read when entering FAIL (including the FAIL-inducing character), or \(m\) if \(p=w\). The shift function is based on the principle that the window can be safely shifted beyond the FAILed substring; so \(\mathit{shift}^{\,p}(w)\) is defined as \(m\) minus the number of successfully read characters in \(w\) if \(w\
eq p\), and \(\mathit{shift}^{\,p}(p):=1\) (although this special case for \(w=p\) can be improved by examining the pattern).  
By construction, BOM never gives longer shifts than B(N)DM. The main advantage of BOM over BDM is reduced space usage and preprocessing time; the factor oracle only has \(m+1\) states and can be constructed faster than a suffix automaton.  
Figure 1: Factor Oracle for \(x=\textsc{CACCACCCT}\), corresponding to pattern \(p=\textsc{TCCCACCAC}\). Omitted edges lead into the omitted FAIL state. The string ACCT is recognized (states 1,2,3,8), although it is not a substring of \(x\).
## 2 Materials and Methods
### Study Site and Samples
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Species**} & \multirow{2}{*}{**Successional**} & \multicolumn{2}{c}{**Number of**} & \multicolumn{2}{c}{**Tree Size**} \\  & & & **Type** & **2019** & **2020** & **DBH (cm)** & **H (m)** & **CA (m\({}^{2}\))** \\ \hline
1. & _Acer nipponicum_ H.Hara & Unknown & 2 & 1 & 19.62–41.11 & 13.70–16.90 & 14.72–28.87 \\
2. & _Acer nufiere_ [PERSON] \& Zucc. & Mid & 1 & 1 & 28.63–41.80 & 14.00–15.40 & 15.61–19.81 \\
3. & _Acer shirasawanum_ Koidz. & Late & 1 & 1 & 28.69–42.44 & 13.50–14.70 & 21.50–45.52 \\
4. & _Acer sieboldianum_ [PERSON]. & Unknown & 0 & 1 & 23.03 & N/A & 15.14 \\
5. & _Betula grossa_ Siebold \& Zucc. * & Early & 1 & 1 & 32.58 & 17.00 & 43.41 \\
6. & _Carpinus japonica_ Blume & Mid & 1 & 1 & 26.75–61.77 & 12.50–15.00 & 26.46–68.75 \\
7. & _Carpinus tschonoskii_ Maxim. & Mid & 1 & 1 & 31.07–36.10 & 15.30–22.00 & 25.49–45.47 \\
8. & _Chengiomax sciadophylloides_ (Franch. \& Sav.) [PERSON] \& [PERSON] & Mid & 1 & 34.87–41.50 & 14.30 & 17.80–29.49 \\
9. & _Cornus controversas_ [PERSON]. * & Mid & 1 & 1 & 55.48 & 20.95 & 117.35 \\
10. & _Fagus crenata_ [PERSON] & Late & 1 & 2 & 22.96–59.46 & 13.60–23.40 & 12.18–132.99 \\
11. & _Frazinus languinos_ Koidz. & Mid & 1 & 1 & 29.01–32.47 & 15.20–15.30 & 31.94–50.00 \\
12. & _Kalopanax septemblous_ (Thunb.) [PERSON]. * & Mid & 1 & 1 & 34.04 & 16.50 & 29.96 \\
13. & _Magnolia obovata_ [PERSON]. * & Unknown & 1 & 1 & 50.44 & 16.60 & 63.16 \\
14. & _Peterostyrax hispidus_ [PERSON] \& [PERSON]. & Early & 1 & 1 & 21.83–31.34 & 9.70–12.20 & 13.57–30.21 \\
15. & _Stevartia monadelpha_ [PERSON] \& [PERSON]. & Unknown & 0 & 1 & 38.51 & N/A & 43.27 \\
16. & _Stevartia pseudocamplia_ [PERSON]. & Unknown & 1 & 1 & 26.55–28.70 & 13.40–13.80 & 9.21–12.23 \\
17. & _Stytrax japonicus_ [PERSON] \& [PERSON]. & Mid & 1 & 0 & 21.26 & 10.60 & 5.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of species used in this study, including the tree size and successful type. Asterisks indicate the tree sample that was continually used for two years. The successful type was determined based on the regeneration status of seedlings in the canopy gaps at the study plot (personal observation) and the data for shade tolerance provided by [PERSON] and [PERSON] (2019). Species for which regeneration status could not be confirmed were classified as “Unknown”. Abbreviations: DBH, diameter at breast height; H, tree height; CA, crown projection area.
# 5G Cross-Border Operation for Connected and Automated Mobility: Challenges and Solutions
## 4 5 GCroCo Solutions
### Anticipated Cooperative Collision Avoidance (ACCA)  
The ACCA use case will be materialized by the integration of different services hosted at the MEC and at the cloud with the ability to deal with different critical situations as depicted on Figure 3. The MEC-hosted geoservices will provide near real time situation handling. State synchronization between MEC geoservices hosted by different MNOs will be handled by a cloud-hosted traffic management system, who will handle regions of interest and distribute information among the MECs in those regions (Figure 5).  
One MEC-based geoserver shall include all perceived objects and events in its region of interest, regardless of the MNO, the brand of the car, or even the road operator. One clear challenge will, therefore, be data interoperability and privacy between countries, MNOs, and automotive industries.  
Vehicles will be connected to the geoservices hosted at the MEC, and can receive information with different priority levels. Critical warnings, e.g., those warning about hazards close by, receive priority over non-critical ones. Finally, Vehicle-to-Network (V2N) Cooperative ITS (C-ITS) can be complemented by direct V2V communication to ensure a hybrid communication solution.  
The solution will also ensure optimized usage of valuable radio spectrum resources by fusing information relating to the same event in the MEC. Figure 6a shows the approach without fusion, where all messages are sent to all other vehicles within a certain area while Figure 6b shows how the amount of downlink messages is reduced if:  
* Information about the same event is not just forwarded but fused to a single downlink message;
* Information from multiple different events is contained in a single message and, therefore, packet header information is only transmitted once.  
Figure 5: ACCA component block diagram in a cross-border (cross-MNO) situation.
# Sparse Representation and SVM Diagnosis Method for Inter-Turn Short-Circuit Fault in PMSM
## 6 Conclusions  
Aiming at the problem of inter-turn short circuit fault in PMSM, this paper proposes a fault diagnosis method based on sparse representation and SVM. The sparse representation is used to extract the first and second largest sparse coefficients of both current signal and vibration signals by OMP, and they are composed into four-dimensional feature vectors, which are input into the SVM for training and testing then. Two PMSMs were used in the experiment, one manually set the inter-turn short circuit and one normal. The experiment results showed that the method is feasible and accurate. More advanced sparse representation algorithms and more motor faults need to be researched in the future.  
Conceptualization, Y.C.; methodology, Y.C. and S.L.; software, S.L.; validation, S.L. and H.L.; formal analysis, S.L. and Y.C.; investigation, S.L.; resources, Y.C. and S.L.; data curation, H.L. and S.L.; writing-original draft preparation, S.L.; writing--review and editing, S.L. and Y.C.; visualization, S.L.; supervision, Y.C.; project administration, Y.C.; funding acquisition, Y.C.  
This research was funded by the National Natural Science Foundation (51607027), the National key R & D Plan Program (2018 YFB0106100), the Sichuan Science and Technology support Program (2016 GZ0395, 2017 GZ0395, and 2017 GZ0394), and the Central University basic Research Business funds (ZYGX2016J140 and ZYGX2016J146).  
The authors declare no conflict of interest.
# Towards Bio-Hybrid Energy Harvesting in the Real-World:
## 4 Research Progress in Energy Harvesting from Enzyme-Based Biofuel Cells (EBFCs)
### Principles of Electricity Generation from EBFCs
[265] reported their work where the anodes were fabricated with carbon nanotubes (CNTs) and modified with a polyaniline film and the cathode was made with PQQ (pyrroloquinoline quinone)-modified carbon nanotubes. [PERSON] et al. [266] developed a carbon nano-flower structure which can be successfully utilized for immobilizing enzymes for EBFC application. Another study reported the use of spray coating for producing flexible biocathodes [267]. In this study, the target of effective enzyme wiring was achieved with flexible biocathodes. These cathodes were spray coated using a conductive ink composed of carbon nanotubes dispersion. Thin continuous layers of CNTs were successfully coated on to of a gas diffusion layer paper with a variable thickness 1 and 7.8 um and were later with Lacase enzyme. A very concerning feature for implantable components, and thus for EBFCs, is their host immune response. This is of utmost importance for any implants where living bodies react to unfamiliar materials through a series of reactions that would lead to the formation of a capsule of collagen around any external component. However, EBFCs require regular exchange of analytes through the cell and any protective collagen layer around the implantable device limits this mandatory transition of analytes [268]. Moreover, there are also possibilities of extracellular matrix infiltration inside the EBFC which can cause degradation of the immobilized enzymes [269]. A pictorial description of the concept is given in Figure 12.
## 3 Biofilm Characterization
### Quantitative Assessment Methods
#### 3.1.1 Direct Methods  
Direct methods include (i) plate counting (colony-forming units CFU); (ii) DAPI (4',6-Diamidino-2-phenylindole dilicate); (iii) Live/Dead assay; (iv) fluorescent in situ hybridization (FISH) [28].  
Briefly, plate counting is a traditional microbiological method used to determine the number of viable bacteria in a biofilm. The biofilm is first disrupted, usually by sonication or vortexing, to release the cells. The resulting suspension is then serially diluted and plated on an agar medium. After incubation, the number of colonies that form (colony-forming units, CFU) is counted. This method provides a direct measure of the number of viable, culturable cells within the biofilm. It is widely used due to its simplicity and effectiveness but only counts cells that can grow under the given conditions, possibly underestimating the total cell population [29]. DAPI is a fluorescent stain that binds strongly to DNA, making it useful for visualizing and quantifying cells in a biofilm. When applied to biofilms, DAPI stains the nuclei of all cells, allowing them to be viewed under a fluorescence microscope. DAPI staining is used to estimate the total number of cells in a biofilm, regardless of their viability. This method is particularly useful for imaging biofilms and providing a total cell count, but it does not distinguish between live and dead cells [29]. The Live/Dead assay utilizes two fluorescent dyes to differentiate between live and dead cells within a biofilm. Typically, a green fluorescent dye (e.g., SYTO 9) stains all cells, while a red fluorescent dye (e.g., propidium iodide) penetrates only the damaged membranes of dead cells. The stained biofilm is then observed under a fluorescence microscope or analyzed using flow cytometry. This assay allows for the simultaneous quantification of live and dead cells within a biofilm, providing insights into cell viability and the effectiveness of antimicrobial treatments. It is widely used in biofilm research for assessing cell viability and treatment outcomes [31]. FISH is a molecular technique that uses fluorescently labeled probes that bind to specific nucleic acid sequences within cells. When applied to biofilms, these probeshybridize to the target sequences, allowing the visualization of specific microorganisms or groups of microorganisms within the biofilm using fluorescence microscopy. FISH is particularly useful for identifying and quantifying specific microbial populations within a biofilm, providing both spatial and quantitative information about the biofilm's microbial composition. It is highly specific but requires knowledge of the target organisms' genetic sequences [2].  
Table 1 summarizes some of the most common direct methods used to quantify biofilm, showing their advantages and disadvantages.
## 4 Discussion  
The presented analysis dealt with implementing the GOMORS optimization algorithm into an existing framework and its potential to optimize the structural performance of BIW parts under a geometrical constraint. The results of the structural optimization for a linear and a non-linear crash-load case are promising, as the GOMORS outperforms the NSGA-II in terms of convergence ratio. Figures 4 and 5 show that GOMORS reduces the time for calculation and the number of expensive computations. The second part of the paper dealt with whether surrogate-based algorithms can handle the feasibility constraint. This question was successfully answered with Figure 7. The surrogate represents the model behavior properly so that the optimization converges quickly if the sampling size is of sufficient size. In [20], the authors tested Random Forrest Regression as a surrogate model and found that the penalized infeasible sampling's mean squared error was very high. Concerning the optimization of the linear load case with infeasibility constraint, this hypothesis can be partly refuted, as the GOMORS converges much better than the NSGA-II, which could be reasoned by a higher performance of the RBF surrogate model.  
The final test of the GOMORS framework for three-objective optimization analyzes the performance of the constraining approach by direct penalization of infeasible designs. It considers the displacement, mass, and feasibility. Firstly, Figure 11 shows the functionality of the approach as the final frontier only exhibits feasible results, which also show better structural performance than the initial design. Figure 13 compares the performance of the two-objective with the three-objective approach. While comparing the two-objective to the three-objective GOMORS approach, the three-objective approach performs slightly better in this specific scenario. Furthermore, GOMORS outperforms the NSGA-II for the  
Figure 13: The Pareto-frontier for the GOMORS with two and three objective functions and the NSGA-II with two objective functions. The performance is relative to the initial design.  
24 DV example. The results demonstrate the performance of the GOMORS framework in comparison to the NSGA-II. Still, one drawback of the GOMORS implementation is the number of parallel evaluations, which is limited to four, and for the current implementation, to one. Long calculation runs may benefit a substantial parallelization; a wider parallelization will make the GOMORS even more performant. It could help to reduce the time of optimization even further. Even if the convergence ratio of the NSGA-II is lower than the one of GOMORS, the nearly unlimited scalable parallelization makes it attractive for cheaper optimization tasks, such as linear calculations. Still, GOMORS may be of benefit here, as areas that tend to be not explored by the NSGA-II are explored.
## 3 Data and Results  
### The Data  
The historical data matrix used in this work was constructed from the daily data of the cumulative number of cases since the beginning of the pandemic, which are daily released by the Centre for the Coordination of Health Alerts and Emergencies of the Ministry of Health and correspond to the day prior to its publication. It is a set of 230 reports (until 19 October 2020) in pdf format, from which the daily values of the variable \(y\) were extracted. These reports can be found on the website of the Ministry of Health ([[https://www.mscbs.gob.es/](https://www.mscbs.gob.es/)]([https://www.mscbs.gob.es/](https://www.mscbs.gob.es/))), and one of them is referenced as an example [21]. As the Ministry has not provided information on weekends since July, the total cases corresponding to Fridaysand Saturdays were obtained by linear interpolation between the values of Thursdays and Sundays.  
The \(y\) value is defined by the Spanish Ministry of Health as \"total cases confirmed by polymerase chain reaction test (PCR) until 10 May, and by PCR and IgM antibodies against SARS-CoV-2 (only if compatible symptoms) according to the new surveillance strategy\" since 11 May. Therefore, in this study, all \(y\) values are confirmed cases by PCR or IgM compatible with symptoms. As explained in the previous section, the algorithm works with the moving average of total cumulative number of cases of seven days, represented as \(y^{*}\), that are directly calculated from the historical cumulative number of cases matrix. This value is related to the \"Cumulative number for 7 days of COVID-19 cases per 100,000\", a rate usually used as an indicator, calculated as the number of new cases of disease in seven days multiplied by 100,000 and divided by the total number of individuals in the population at risk (CI 7d/100k).  
The theoretical border between the first and second pandemic wave was evaluated fitting \(y\) values from 16 May to 16 July, to a third-degree polynomial function (\(R^{2}~{}=~{}0.9980\)) and setting the second derivative equal to zero, so 15 June is obtained as the inflection point. At this point, the curvature changes from convex to concave and marks hypothetically the end of the first wave and the start of the second (Figure 3). Thus, our study covers from 15 June, although it uses data from 14 days before to calculate the first of the predictive equations.  
It is important to note that the information provided in Spain by the Ministry of Health has been the subject of controversy and criticism, being considered insufficient to understand the dynamics of COVID-19 and to take action, excluding ongoing retrospective series corrections [21].  
Figure 3: Cumulative number of cases in Spain from 23 February until 20 September. The arrow marks an inflection point at 15 June, which theoretically separates the two pandemic waves. Data source: Centro de Coordinación de Alertas y Emergencias Sanitarias (CCAES).
## 2 Related Work
To the best of our knowledge, we are the first to systematically use access patterns as features, as well as streaming ML to support caching and tiering policies. Streaming ML is becoming popular for continuously training models for various industrial applications, including cyber security, IT infrastructure maintenance (AIOps), anomaly scoring, and drift detection [55]. One work utilized steaming ML to develop a data mining-based horizontal fragmentation method in Data Warehouses to optimize query response time and system efficiency [56], while another work studied the combined problem of the system configuration and hyperparameter tuning of ML applications over distributed stream processing engines such as Apache Spark Streaming [57]. Streaming ML has also been deployed in other domains, such as in social computing for detecting online aggression on social media [58], in urban management for IoT-enabled waste management in smart cities [59], and in electronic health for online estimation and inference of treatment effects [60]. Several streaming ML libraries are available, each with unique features. Massive Online Analysis (MOA) [20] offers a collection of well-established online algorithms for streaming classification, clustering, and change detection. Vowpal Wabbit [61] is a streaming ML framework built on the perceptron algorithm with a strong emphasis on reinforcement learning. Jubatus [62] provides stream mining capabilities by tightly integrating its ML library with a custom-built Distributed Stream Processing Engine (DSPE). streamDM [63] is a data mining framework that implements streaming ML algorithms specifically for Spark Streaming. Lastly, Apache SAMOA [64] supports distributed ML computation across multiple DSPEs, including Storm, Flink, Samza, and Apex.
# Estimation of Impact Loads Transmitted to Vibro-Ripper Housing Using Transfer Path Analysis
## 1 Introduction
Prediction of the structural stability of a vibro-ripper, its durability, and life cycle span requires analysis of the impact load generated during rock crushing and transmitted to the housing through each part of the system. The crushing of rocks through rotational motion exposes the vibro-ripper to an extreme work environment involving continuous impact vibration and load. Therefore, loads transmitted during rock crushing must be quantitatively analyzed to verify the structural stability and design robustness of core parts of the crusher, which are closely related to its durability and performance. The quantitative load is a reference value for the design strength of the major parts of construction equipment and for the input load used in durability evaluation. This study sought to assess the impact load transmitted through the link modules of the vibro-ripper to the housing and the impact load transmitted in different directions. In this context, this study aimed to analyze and quantify the contributions of impact load to different parts of a vibro-ripper, and the transmission of loads during rock crushing work. To that end, Section 2 of this paper explains the theoretical background of the transfer path analysis method for impact load estimation and vibration experiments of a vibro-ripper. Section 3 presents the quantitative load estimation results using the TPA method and compares them with the striking force measured through the load cell. The research concludes with Section 4. Impact loads are transmitted to each part of a vibro-ripper through vibrational effects such as pitch, roll, and yaw motion (Figure 2). We applied vibration-based transfer path analysis (TPA) in considering triaxial vibrational characteristics to quantify the striking load during crushing work that is ultimately transmitted to the ripper housing through the link modules. We further analyzed the contribution of the transmitted impact load. The impact loads of each structural part, as derived using the TPA method, were compared with those in the vibro-ripper tooth derived from the operating load of a ripper, measured using a load cell. This comparison confirmed the applicability of the TPA method to mechanical systems subjected to impact loads and vibration. Figure 2: Triaxial vibration of a vibro-ripper during rock crushing.