esa-sceva/satcom-chunk-collection huggingface dataset:
# Modular Combined DC-DC Autotransformer for Offshore Wind Power Integration with DC Collection
## 5 Simulation Study
### Simulation of the Recovery Strategy  
At \(t=0\) s, the test system is in steady-state. The sequences of the fault, applied control, and the protection measures are as follows:  
1. At \(t=0.2\) s, the three-phase short circuit fault occurs at the AC bus, and the fault time lasts for \(0.1\) s. At \(t=0.20\) s, the MCAT, DAB converters inside WTs and onshore converters are blocked. An AC chopper facilitates the WT to consume the surplus energy and limits the DC overvoltage. At \(t=0.206\) s, the fault current reduces to 0 due to the blocked MCAT.
2. At \(t=0.3\) s, the MCAT is unblocked, while the control strategy of MMC\({}_{\text{H,1}}\) is switched from passive control to DC voltage control (reference value is \(100\) kV) and reactive power control (reference value is \(0\) MVar). At the same time, DAB converters inside WTs and onshore converters are unblocked. At \(t=0.6\) s, MMC\({}_{\text{H,1}}\) is switched to passive control again. Figure 6 shows the simulation diagram of fault response characteristics of the test system.  
Figure 7a-e shows currents and voltages in the test system. When the three-phase short-circuit fault occurs at the AC bus, the maximum value of fault current reaches \(0.75\) kA at about \(0.201\) s, then the fault current is cut off rapidly. At \(t=0.206\) s, blocking the MCAT make the DC current drop to \(0\), but \(U_{\text{L,1}}\) would reach \(1.2\) pu because of the imbalance  
Figure 6: Simulation results of wind speed variation: (**a**) Active power transmitted by the MCAT; (**b**) A phase AC output current of MMCs; (**c**) DC currents of high-voltage and low-voltage DC grids respectively; (**d**) DC voltages of MMCs; (**e**) DC voltages of high-voltage and low-voltage DC grids respectively; (**f**) voltages of \(C_{\text{H,1}}\) and \(C_{\text{H,2}}\); (**g**) A phase AC voltage of the AC bus.  
of power transmission. Since the AC bus is not grounded, its voltage is not dropped to 0. At \(t=0.40\) s, \(I_{\text{dclow}}\) reaches 2 pu due to violent fluctuation of transmitted power. Therefore, the threshold value of overcurrent protection should be adjusted to prevent the MCAT from blocking again. At \(t=0.8\) s, the currents and voltages in the test system are in steady-state again.  
Figure 7f,g shows the simulation results of \(u_{\text{CH,1}}\), \(u_{\text{CH,2}}\), and power changes of each MMC. At \(t=0.3\) s, \(C_{\text{H,1}}\) and \(C_{\text{H,2}}\) have temporary overvoltage, and \(u_{\text{CH,1}}\) reaches twice as the rated voltage. The transient process of capacitors voltages lasts for 0.4 s. At \(t=0.8\) s, the MCAT transmits rated power again.  
Figure 7h shows the AC voltage of the AC bus could reach stable operation rapidly.
# Patent Portfolio Analysis of the Synergy between Machine Learning and Photonics
## 3 Research Design
### Correspondence Analysis  
Correspondence analysis uses a low-dimensional perceptual map to process categorical variables, analyze the relative position of studied targets, and present the relationship between related attributes [31]. Correspondence analysis uses figures to present the data in a cross tabulation. It considers multiple categorical variables simultaneously and presents the relationship between the variables. In addition, it uses points to present the ratio of the elements in the rows and columns of the cross tabulation in a lower dimensionality. In other words, correspondence analysis can change the frequency of the cross tabulation into a ratio and calculate the corresponding relationship. This study used two-stage cluster analysis to classify the assignees and the international patent classification (IPC) numbers and used the coordinates obtained from the correspondence analysis in the cluster analysis to perform grouping. The spatial positions were used to determine the patent portfolio and clustering of the assignees.  
This study used correspondence analysis to analyze machine learning in photonics; the categorical variables were assignees and IPC numbers. Next, the perceptual map of the correspondence map was used to demonstrate the relationships among the assignees and the relationships between the assignees and IPC numbers. Assignees with a closer distribution signify that they have more patents on similar technologies and can be classified into the same group. An assignee with a closer distance to an IPC number signifies that the assignee has a superior number of patents in that IPC category.
# Network Slicing for Beyond 5G Systems: An Overview of the Smart Port Use Case
## 8 Performance Evaluation of Network Slicing  
In network slicing, performance of the network can be measured in terms of latency, data rate, and packet loss rate; it can affect the QoS requirements needed for the different slices. In the URLLC slice, applications are considered to be delay sensitive, and ultra reliability is required to meet the QoS demands. According to [47], the committed information rate (CIR) ratios for specific data traffic depends on its characteristics. Different applications have different CIR ratios. Performance of a certain application can be affected by less CIR ratios. When there is a higher demand than the total bandwidth, a CIR ratio greater than 100% is required for the elastic and inelastic data traffic. In the case of URLCC, a CIR ratio of 120% is needed to meet the QoS requirement [47]. In the eMBB slice, a higher data rate, and in this case CIR ratio of 140%, is required to meet the 100% throughput percentage [47]; however, further study is needed to investigate how a higher number of users will impact the performance of the network slicing in terms of maximum bandwidth utilization.
# Adaptive Satellite Navigation Anti-Interference Algorithm Based on Inverse Cosine Function
## 4 Simulation Analysis
### Performance Verification of The Improved Variable Step Size Algorithm
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**SNR (dB)**} & **LVS-LMS** & **VVS-LMS** & **NDVS-LMS** & **ICVS-LMS** \\  & **Algorithm** & **Algorithm** & **Algorithm** & **Algorithm** \\ \hline
20 & 170 & 380 & 360 & 30 \\
30 & 200 & 400 & 380 & 60 \\
40 & 260 & 430 & 450 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of iteration times under different signal-to-noise ratios. Figure 5: Comparison of algorithm tracking performance and convergence speed under different SNR values: (**a**) SNR = 20 dB; (**b**) SNR = 30 dB; (**c**) SNR = 40 dB. Based on the information presented in Figure 5, it is evident that the proposed ICVS-LMS algorithm can achieve rapid convergence in approximately 50 iterations, even when dealing with an unknown system that exhibits time-varying characteristics, while the LVVS-LMS algorithm, the VVS-LMS algorithm, and the NDVS-LMS algorithm need about 100, 350, and 400 iterations to reach convergence, respectively, while the proposed ICVS-LMS algorithm exhibited a rapid convergence speed while simultaneously sustaining a minimal level of steady-state error, with obvious advantages and better coping ability.
## 3 Methods  
To begin with, the problem addressed in this study must be clearly defined. We aim to improve the linear exploration process that occurs when using a conversational user interface (CUI) to explore previously generated image records, rather than the process of inputting text and receiving generated images iteratively in an image generation scenario. As mentioned earlier in the Introduction, linear exploration requires users to scroll through the records one by one to locate the desired past generation record. This process demands a significant number of interactions and a considerable amount of time. Therefore, we propose a method to automatically organize generation records hierarchically, enabling users to intuitively explore past records and reducing the effort and time required to locate desired records. Additionally, we developed a user interface (UI) by applying this method and validated its effectiveness through usability evaluations.  
The operational workflow of the proposed automatic hierarchy method consists of five steps, and the overall process is illustrated in Figure 1. The following is an explanation of the workflow: First, the user's image generation process is recorded in units of input prompts and the resulting images. This corresponds to Figure 1A. Next, features are extracted from these units, and a preprocessing step is conducted for hierarchy construction. At this stage, we devised two methods for extracting features from these units: a vision-based approach using the resulting images and a prompt-based approach using input prompts. This is shown in Figure 1B. In the third step, the preprocessed units of entire records are treated as individual points, and clustering is performed to organize them hierarchically. This step, independent of the preprocessing method, follows the same clustering process and corresponds to Figure 1C. Subsequently, to enhance the interpretability of the hierarchy and support intuitive exploration for users, representative keywords for each hierarchy level are extracted. This step is depicted in Figure 1D. Finally, the results of the hierarchical organization of generation records are provided to users through a UI that applies the automatic hierarchy method. This corresponds to Figure 1E. The design and implementation details of each step are discussed below.
# Probabilistic Shaping for Finite Blocklengths: Distribution Matching and Sphere Shaping
## 6 Approximate Complexity Discussion
### Storage Requirements
: _To realize ESS or (Algorithm 1 in [23]) for the setup in Example 6, at most \(Ln\lceil nR_{\mathrm{s}}\rceil=80.46\) kilobytes (kB) of memory is required. On the other hand for SM, at most \(L\log_{2}n\lceil nR_{\mathrm{s}}\rceil=7.54\)kB of memory should be allocated._  
**Remark 4**. : _To compute the required storage for SpSh in the BP case, we will assume that \(n_{m}\) is independent of \(n\). This assumption relies on the fact that the rate loss resulting from BP only depends on \(n_{m}\)[65]. Thus, for a fixed rate loss, the required value of \(n_{m}\) is independent of \(n\). Expressing the number of bits to store the exponent as \(n_{p}=\lceil\log_{2}\left(\lceil nR_{\mathrm{s}}\rceil-n_{m}\right)\rceil\), we see that \(n_{p}\) behaves as \(\log_{2}n\) for a fixed \(n_{m}\). We note here that for a fixed \(n\), \(\mathcal{A}\) and target \(k\), the natural choice for \(n_{m}\) is the smallest value that keeps the number of sequences at least \(2^{k}\)[65]._  
For the BP implementations of ESS, SM and (Algorithm 1 in [23]), each element of the stored shaping matrix is at most \((n_{\mathrm{m}}+n_{\mathrm{p}})\)-bit long [65]. Following Remark 4, the storage complexity of ESS and (Algorithm 1 in [23]) in the BP case is \(\mathcal{O}(n^{2}\log n)\). On the other hand the storage complexity of BP SM is \(\mathcal{O}(n\log^{2}n)\). **Example 11** (BP SpSh, required storage). : _To realize ESS or (Algorithm 1 in [23]) with \(n_{m}=9\) and \(n_{p}=7\) for the setup in Example 6, at most \(Ln(n_{m}+n_{p})=11.39\)kB of memory is required. On the other hand, when implemented using \(n_{m}=6\) and \(n_{p}=7\), SM demands at most \(L\log_{2}n(n_{m}+n_{p})=0.87\)kB of memory. We note that the mantissa lengths \(n_{m}\) are selected according to the discussion in Remark 4._  
In conclusion, we believe that storage requirements in the order of a few kBs are not critical for high-throughput operation, particularly in comparison to latency and complexity. Note that the required storage for BL-DM, PDM and PA-DM depends on the underlying algorithm.
## 3 Background on YANG and OPC UA
### Yang
#### 3.1.2 Data Definitions  
Data definition statements include data type definitions, configuration and state definitions, and any Remote Procedure Calls and notifications supported by the device. They are the critical elements that define the device's capabilities.  
* leaf is the simplest data definition, having at most one instance and no child data definition statements. leafs contain data values.
* leaf-list is otherwise the same as a leaf but can contain a list of unique leaves. A key refers to its value in the context of a leaf-list.
* container is a data dentition that is at most one instance and can hold no value but can have one or more child data definition statements, such as leaves or containers.
* list is otherwise the same as the container but can contain a list of unique containers. It is identified with one or more key leaves.
* grouping is data definition statements that can be used in multiple locations in the YANG model if grouped with a grouping statement. This enables the efficient reuse of definitions, thus reducing the modeler's workload and decreasing the likelihood of error. When a part of a tree is used in multiple locations, making a change to it affects all instances of that structure.
* typedef statement: each leaf and leaf-list data definition statement includes a mandatory definition that specifies the format required for the data to be considered valid. YANG provides basic built-in types, such as string, enumeration, uint64, etc.; these data types can be extended by redefining the type definitions using typedef statements.
* The rpc statements define remote callable procedures with specified input arguments and output results. Remote Procedure Call (RPC) definitions can thus establish a comprehensive application programming interface (API) that can be efficiently used over the network.
* Notification statements can be used to specify a set of important events emitted by network functions. These notifications can contain complex information using the same rules as data definitions. Consequently, notifications can provide a valuable event-based interface for the state of network functions. Client applications can subscribe to these events to observe device state changes.
# Graphene and Iron Reinforced Polymer Composite Electromagnetic Shielding Applications: A Review
## 5 Polymer-Based Composites
### Graphene@Iron@Polymer Composites
#### 5.1.2 Interpretation of Graphene@Iron@Polymer Based Composites Articles
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**S. No** & **Material** & **Thickness** & **Loading** & **Methods** & **Frequency** &
\begin{tabular}{c} **Shielding** \\ **Effectiveness** \\ \end{tabular} & **Year** & **Reference** \\ \hline
31 & Gn/SiCraw/PVDF & 1.2 mm & - & 1. Electrostatic assembly & 8-12 GHz & 32.5 dB & 2020 & [136] \\ \hline
32 & PVDF/graphene & 20 μm & 15 wt.\% & Solution casting method & 8-12 GHz & 47 dB & 2018 & [137] \\ \hline
33 & PVDF/GNP-Ni-CNT & 0.6 mm & - & Solvent casting method & 12-18 GHz & 46.4 dB & 2020 & [138] \\ \hline
34 & PVDF/n-Fe & 1.93 mm & - & Hot-moulding process & 12-18 GHz & 40.21 dB & 2016 & [139] \\ \hline
35 & PVDF/PFC & 2 mm & 1 wt.\% & Solution blending process & 12-18 GHz & \(-\)29.7 dB & 2017 & [140] \\ \hline
36 & CuMW-TAGA/Epoxy & - & 7.2 wt.\% & Thermal annealing method & 8-12 GHz & 47 dB & 2020 & [132] \\ \hline
37 & RGO-CF/EP & 3-5 mm & - & 1. Electrophoretic deposition & 8-12 GHz & 37.6 dB & 2016 & [141] \\ \hline
38 & GNP/Fe/Ox/Epoxy & - & 7 wt.\% & Co-precipitation method & 8-12 GHz & 37.03 dB & 2019 & [142] \\ \hline
39 & GNP/Fe/Epoxy & 5 mm & 5 wt.\% & Sonication method & 1-65 GHz & \(-\)78 dB & 2020 & [143] \\ \hline
40 & TGO/CI/Epoxy & 4 mm & - & Centrifugal mixing method & 8-12 GHz & 40 dB & 2015 & [144] \\ \hline
41 & GCF/MoS/EP & - & 0.5 wt.\% & - & Hummers Method & 18-26 GHz & 51.1 dB & 2017 & [145] \\ \hline
42 & RGO/PF/Epoxy & 3 mm & 60 wt.\% & Solution mixing method & 2–18 GHz & \(-\)10.26 dB & 2020 & [146] \\ \hline
43 & Polylactic acid/Biochar/Graphite & 0.25 mm & - & Hot-pressing method & 18–26.5 GHz & 30 dB & 2019 & [147] \\ \hline
44 & Polypyrrole/BST/RGO/Fe\({}_{3}\)O\({}_{4}\) & \(22.8\times 10.05\times\) & - & Chemical oxidative polymerization & 8-12 GHz & 48 dB & 2018 & [148] \\ \hline
45 & RGO@PEI & - & 2.5 wt.\% & Hummers Method & 8-12 GHz & 26 dB & 2018 & [149] \\ \hline
46 & Fe\({}_{3}\)O\({}_{4}\)@RGO/TFU & 1 mm & - & Solution casting method & 8–12 GHz & -15.51 \(\pm\) 1.6 dB & 2020 & [150] \\ \hline
47 & G/Polyurethane sponge & 9 mm & 18.7 wt.\% & Hydrothermal method & 8–12 GHz & 35 dB & 2019 & [151] \\ \hline
48 & TPU/TRG & 2 mm & 5.5 vol\% & Solution blending method & 12–18 GHz & 32 dB & 2017 & [152] \\ \hline
49 & BaFe@TRGO@TPU & 0.25 mm & - & Solution casting method & 0.1–20 GHz & \(-\)61 dB & 2020 & [153] \\ \hline
50 & PEDOT/RGO/SrFe\({}_{12}\)O\({}_{49}\) & \(2.5\) mm & - & In-situ growth & 8–12 GHz & 42.29 dB & 2019 & [154] \\ \hline
51 & PEDOT/RGO/PbTiO\({}_{3}\) & \(2.5\) mm & - & Chemical oxidative polymerization & 12.4–18 GHz & 51.94 dB & 2018 & [155] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._
## 3 Overview of 3 GPP AKMA
### AKMA Architecture and Operation
#### 3.1.3 AKMA Procedures
While the \(K_{AKMA}\) is re-keyed through primary authentication, the \(K_{AF}\) can continue to be used until its configured lifetime expires, even if a new \(K_{AKMA}\) has been generated. This approach ensures that ongoing application sessions are not disrupted. However, if the \(K_{AF}\) expires, the AF must either reject the UE's access or refresh the \(K_{AF}\) by initiating a new key request. This process ensures that all keys remain within their secure and valid operational periods, thereby maintaining the overall security posture of the AKMA framework. Figure 4: AKMA initial phase.
## 5 Experimental Tests
### Step-Load System Response  
Tests were conducted to evaluate the system response to a step load. As described previously, the batteries must handle sudden changes in the external load, while the  
Figure 8: DC-DC converter and diode assembly efficiency.  
Figure 9: Images collected during the thermal test with the thermal camera: (**A–C**) correspond respectively to 100, 200 and 300 W of power flow.  
emulated FC has to follow the low-dynamic component of the power demand. Since the power split is performed by the power converter, the behaviour of the energy sources is determined by the energy management strategy. As described in Section 4, the EMS proposed for this preliminary test is a PI-based strategy. Thus, different parameters of the PI controller determine different time responses of the emulated fuel cell. To highlight this behaviour, the tests to evaluate the system response to a step load were performed with different parameter values of the PI controller. The external load power consumption for this test was determined as follows: from t = 0 s to t = 10 s, the external load was equal to zero; from t = 10.05 s to t = 100 s, the external load power was equal to approximately 50 W; from t = 100.05 s to t = 190 s, it was equal to 330 W; from t = 190.05 s to t = 280 s, it was equal to 50 W; and in the end, from t = 280.05 s to t = 370 s, it was equal to zero. However, the total power requested by the load unit is equal to the external load plus the estimated power requested by the auxiliaries of the FC system; thus, during the tests, it reached a maximum of approximately 390 W. This load profile is not realistic, but the aim of this test is to highlight the system behaviour in response to a high and sudden load variation rather than to a realistic load profile. The value of 330 W was chosen since it corresponded to the maximum power requested by the external load in the scaled reference powertrain. Figure 10 shows the system response to a step load with different PI gain parameters. The external load profile means the current requested without considering the FC auxiliaries' current absorptions, while total load current means the overall current requested by the load unit during the test; thus, it comprises both the external load and the FC system auxiliaries' absorptions. It can be stated that the power fluctuations of the emulated FC can be significantly reduced with proper tuning of the PI controller. On the contrary, if the gains are not properly tuned, the system may not be stable, and this translates into some oscillations in the power output.
# De-Noising Process in Room Impulse Response with Generalized Spectral Subtraction
## 3 Implementation and Experiment
### Noise Subtraction Procedure for RIR
Therefore, the generated EDCs were also used as a visual inspection of the change in the EDC dynamic range to determine the best optimal set of GBSS parameters to give the most significant increase in the dynamic range and noise reduction effects. Once the promising sets of factors were obtained, the RIRs measured in two normal rooms were implemented to verify the feasibility and accuracy of the algorithm. In the GBSS method, parameters RTs and EDT estimated from the reference EDCs were taken as a reference to control the subtraction factors. The EDT was applied to supervise the musical noise effects in the first part of the EDCs. The decay range for the RT estimation started at \(-5\) dB, while the lower limit decay level was determined by the decay level of cross-point B. Finally, the RTs calculated from the de-noised EDCs in the decay ranges were compared with the reference EDCs and the compensated EDCs.
## 2 Materials and Methods
### Obtaining Tetra- and Octaploid Plantlets  
#### 2.2.1 In Vitro and Hydroponic Culture  
Seeds of _A. arenasa_ were sterilized in 70% ethanol for 90 s, next in 25% commercial bleach for 12 min, and rinsed in sterile distilled water for 2, 3, and 5 min. Sterile seeds were sowed and cultured on solidified half-strength Murashige & Skoog medium (\(\frac{1}{4}\) MS medium). They were kept in the dark at 4 \({}^{\circ}\)C for 4-5 days and afterward in a growth chamber for germination. Sterile fragments of seedlings (leaves, roots, cotyledons + hypocotyls) were collected and placed on the \(\frac{1}{2}\) MS medium with different combinations of auxins and cytokins: 2 mg L\({}^{-1}\) 2,4-D (2,\(\AA\)-Dichlorophenoxyoxyacetic acid) + 2 mg L\({}^{-1}\) KIN (Kinetin), 0.5 mg L\({}^{-1}\) 2,4-D + 0.05 mg L\({}^{-1}\) KIN, 2 mg L\({}^{-1}\) 2,4-D + 2 mg L\({}^{-1}\) BAP (6-Benzylaminopurine), and 1 mg L\({}^{-1}\) 2,4-D + 1 mg L\({}^{-1}\) BAP to obtain callus tissue. Fragments of the obtained callus tissue were placed on media supplemented with 1 mg L\({}^{-1}\) TDZ (Thidiazuron) or 2 mg L\({}^{-1}\) TDZ to induce shoot formation or the \(\frac{1}{2}\) MS medium as a control (hormone-free medium). Then, the obtained adventitious shoots were transferred onto \(\frac{1}{2}\) MS media containing two auxins in different concentrations (0.5 mg L\({}^{-1}\) IAA (Indole-3-acetic acid), 1 mg L\({}^{-1}\) IAA, 0.5 mg L\({}^{-1}\) IBA (Indole 3-butyric acid), 1 mg L\({}^{-1}\) IBA) or without hormones for rooting. All media contained 30 g L\({}^{-1}\) sucrose and were solidified with 8 g L\({}^{-1}\) agar. The pH was adjusted to 5.7-5.8. The seeds, plants, and culture were maintained in stable growth-chamber conditions at 25 \(\pm\) 3 \({}^{\circ}\)C with a 16-h photoperiod under cool-white, fluorescent lamps (flux 70-100 umol m\({}^{-2}\)s\({}^{-1}\)). Regenerated, rooted plantlets with roots a minimum of 15 mm in length were transferred into a hydroponic culture with a modified half-strength Hoagland medium [39]. All chemicals were supplied by Sigma-Aldrich (Saint Louis, MO, USA).