esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Introduction to IPv6
As Regional Internet Registries (RIRs) such as the Reseaux IP Europeens Network Coordination Centre (RIPE NCC) hand out the last available addresses, new policies are applied for their allocation. In particular provider-independent PI addresses, which are needed for organizations that require multihoming or prefer their network to be independent from their ISP, are harder to acquire [30]. There are also some initiatives taking place that encourage testing and implementing IPv6. In 2011, the \"IPv6 Day\" aimed for an initial large-scale test of IPv6, followed by the \"IPv6 Launch\" one year later. In 2012, websites like Google, Facebook or Yahoo! did not only test IPv6, but made their websites permanently available via IPv6. More than 70% of the participants are still reachable via IPv6 as of October 2012 [31]. According to surveys among ISPs and other network related organizations conducted by the Number Resource Organization (NRO), more than 70% of the participants have some kind of IPv6 presence internally or on the Internet. Only 7% do not plan to deploy IPv6 yet. Most of the participants report only very low traffic via IPv6 so far [32]. This shows that most network companies already have some kind of IPv6 in use and are gaining experience in handling it. Today almost all address blocks of the five RIRs are allocated. The RIPE NCC keeps statistics on the current available address space that is updated on a weekly basis. An excerpt from February 2012 to January 2013 is shown in Figure 1. The flat (blue) line represents the last/8 block of IPv4 addresses allocatable by RIPE NCC and a/13 block reserved for unforeseeable events. The red line shows the amount of IPv4 addresses left. As can be seen, the available amount of addresses shrunk very fast until it hit the last/8 block some time around the 38 th week of 2012. Since then, new restricted allocation policies have come into force. The maximum allocatable address size from this last/8 block are/22 blocks. Applicants for these address blocks must already have been given an allocation of IPv6 address space. Furthermore, applicants have to prove the need for more IPv4 addresses [33]. As the graph shows, these policies slowed down the depletion to a great extent. Now, the amount of available IPv4 addresses declines very slowly. In the foreseeable future this pool will subside. While some IPv4 addresses will always be available, larger address blocks will only be allocated from the IPv6 pool. Large companies who received very large address blocks at the beginning of the Internet will have enough space to support their networks for a long time, but new companies who never had the chance to get an IPv4 allocation will have to use IPv6 to build up their network. In the following, we will give a brief overview on selected aspects of IPv6 that are important topics for the secure deployment guidelines.
## 2 Background
### Overview of AR Placement Methods
#### 2.1.3 Hybrid Placement Approaches  
Hybrid approaches try to make a compromise by combining different methods, so overcoming their weak points and challenges. It not only delivers convincingly precise and robust results to AR applications, but it also reduces computational complexity. We cite, as an example, the study carried out by [47] which uses a hybrid approach (marker based and sensor based) to create virtual visits to underwater cultural heritage sites.
# Multiple Thermal Parameter Inversion for Concrete Dams Using an Integrated Surrogate Model
## 2 Methodology
### ISM-Jaya for Multiple Thermal Parameters Inverse Analysis  
This paper proposes a simple and efficient method based on the prototype monitoring temperature for multiple thermal parameter inversion of concrete dams.  
The ISM replaces the complicated and time-consuming finite element analysis, at which point the thermal inverse analysis is transformed into a parametric optimization problem. The Jaya algorithm then minimizes the difference between the observed and calculated responses to identify the concrete thermal parameters.  
The flowchart of the proposed ISM-Jaya method for multiple thermal parameter inversion of concrete dams is shown in Figure 3. The main steps are as follows.  
Step 1: Determine the thermal parameters of the concrete dam to be inverted, as well as the approximate range of these parameters, which can be referenced from experimental results or similar projects.  
Step 2: Obtain the training and testing datasets for ISM. Design parameter sample sets using uniform design, which are input to the finite element model for temperature analysis one by one. The error between the monitored and calculated temperatures is calculated using Equation (11). Thus, the training and test sets consisting of the parameter samples and temperature errors are obtained.  
Step 3: Construct the ISM. Based on the sample set obtained in Step 2, the ISM could be trained and tested with the thermal parameters as inputs and the temperature errors as outputs, until it meets the accuracy requirements for replacing the finite element analysis. Otherwise, it is necessary to return to Step 2 and increase the sample size.  
Step 4: Parameter inversion. Several sets of initial parameters are generated as the initial population within the range of thermal parameters, and the fitness value of each individual is calculated by the trained ISM in step 3 to obtain the candidate solution set. Based on the optimal and worst solutions in the current candidate solution set, the positions of the individuals in the next iteration are updated with the optimization objective of minimizing the error between the monitored and calculated temperatures.  
Step 5: Inversion result output. The inversion results are obtained when the Jaya algorithm termination condition is reached.
## 3 Results and Discussion  
### Enzyme Production  
Recombinant _Is_PETase and Trx-_Is_PETase were successfully produced as soluble enzymes in _E. coli_ Rosetta-gamiTM2 (DE3). The SDS-PAGE analysis showed proteins of 31.5 and 43.5 kDa, which is consistent with the theoretically calculated molecular weights of _Is_PETase and Trx-_Is_PETase, respectively (Figure 2A). _Is_PETase had a production yield of 79 mg/L of culture, with an estimated purity higher than 90%, while Trx-_Is_PETase had a yield of 93 mg/L and purity of ~80%. A larger elution gradient was needed to achieve an acceptable purity compared with the Trx-_Is_PETase (Figure 2B). Furthermore, the purified Trx-_Is_PETase eluted in more concentrated fractions than the _Is_PETase, as shown by the shape of the peak in the purification chromatograms (Figure 2B). It is worth mentioning that _Is_PETase is sensitive to precipitation, which limits its production. To overcome this drawback, several strategies were evaluated, such as secretion systems, expression hosts, and the Trx fusion domain. The Trx domain together with the host strain _E. coli_ Rosetta-gamiTM2, conferred the highest production level compared with other cytoplasmic and even some extracellular systems (Table 1).  
Figure 1: Modeled ligands. (**A**) Bis-(hydroxyethyl) terephthalalate (BHET). (**B**) Bis-(hydroxybuthyl) terephthalalate (BHBT). (**C**) Bis-(hydroxyhexyl) terephthalalate (BHHT). (**D**) AkestraTM fragment.  
Figure 2: Purification analysis of _Is_PETase and Trx-_Is_PETase. (**A**) SDS-PAGE. (**B**) IMAC purification chromatograms. The sharp peak at 130 mL of elution in the _Is_PETase purification chromatogram corresponds to impurities.
# Detection of Geomagnetic Signals as Precursors to Some Earthquakes in China
## 2 Polarization Analysis  
The polarization analysis method is based on the measurement of the spectral power ratio of \(Z\) to the horizontal magnetic fields (\(H\) and \(G\)), as given by [PERSON] and [PERSON] [12; 24]:  
\[Y_{ZH}=\frac{S_{Z}(\omega)}{S_{H}(\omega)},\ \ \ \ \ Y_{ZG}=\frac{S_{Z}(\omega)}{S_{G}(\omega)} \tag{1}\]  
\[G=\sqrt{H^{2}+D^{2}} \tag{2}\]  
where \(S_{Z}\), \(S_{H}\), and \(S_{G}\) represent the spectral power of \(Z\), \(H\), and total horizontal component (\(G\)), respectively. The total horizontal component is obtained from Equation (2). The spectral powers of \(S_{Z}\), \(S_{H}\), and \(S_{G}\) are obtained through a fast Fourier transform (FFT). Previous geomagnetic diurnal variation studies have suggested that more artificial noise is included during daytime than nighttime [5; 22]. Each geomagnetic component (\(Z/H/G\)) was therefore chosen at local midnight (LT 22:00:00-01:59:59) as the analysis object to minimize artificial effects.  
The electromagnetic skin effect was used to obtain the possible emission frequency originating from the earthquake source [5]. The skin depth is defined as  
\[\delta=\left[\frac{2\rho}{\mu\omega}\right]^{\frac{1}{2}} \tag{3}\]  
where \(\rho\) is resistivity, \(\omega\) is angular frequency, and \(\mu\) is magnetic permeability, which can be obtain as \(\mu=\mu_{0}=4\pi\times 10^{-7}\) (H/m) where \(\mu_{0}\) is the vacuum permeability. A single resistivity was not identified near the hypocenter of these earthquakes; we thus used the resistivity of sedimentary rock as the average resistivity in this study (\(\rho=10\)\(\Omega\)\(\cdot\)m).
# Portable Sensor System for Registration, Processing and Mathematical Analysis of PPG Signals
## 3 Results
### Mathematical Analysis of PP Interval Series
#### 3.2.2 Frequency-Domain Analysis  
The obtained results in Frequency-Domain that are presented in Table 6 show high values of the very low frequency spectrum in a healthy group against arrhythmia group (1823.33 versus 1048.44). The low frequency spectrum in a healthy control group (1659.48 \(ms^{2}\)) is significantly lower in the group of patients that were diagnosed with arrhythmia (940.91 \(ms^{2}\)). For high frequency, the highest values are in the group of healthy individuals (941.33 \(ms^{2}\)), lower values were obtained in the arrhythmia patient group (683.11 \(ms^{2}\)).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & **Group1 (Arrhythmia)** & **Group2 (Healthy)** & \multirow{2}{*}{_p_**Value**} \\  & \(N\) **= 42** & \(N\) **= 40** & \\ \hline VLF [\(\text{ms}^{2}\)] & 1048.44 \(\pm\) 104.71 & 1823.22 \(\pm\) 124.62 & \textless{}0.0001 \\ LF [\(\text{ms}^{2}\)] &  
The calculated parameter LF/HF (known as index of sympathovagal balance [65]) in the control group has values (\(1.76\pm 0.39\)) that are within the normal values that are recommended by the heart rate variability standard of measurement, physiological interpretation, and clinical use [50]. The LF/HF of the patients with arrhythmia (\(1.38\pm 0.27\)) have values that are outside the normal values recommended by the standard.  
The power spectral density of PRV is presented in Figure 6; Figure 7. Power spectral density is obtained at Very Low Frequencies, Low Frequencies, and High Frequencies in \(\mathrm{s^{2}/Hz}\). Figure 6 (PPG data of healthy subject) shows high values for PSD in this frequency domain studied. This is an indicator of high values for Pulse Rate Variability, which shows the good overall health of the studied subjects.  
The power spectral density of the patient with arrhythmia (Figure 7) has low values in the Low Frequency and High Frequency domain. The calculated Power spectral density in the Very Low Frequency domain is higher than Power spectral density in the Low Frequency and High Frequency domain. The low levels of Pulse Rate Variability in an investigated patient diagnosed with arrhythmia indicate the presence of cardiac disease.
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 3 Metal Selenide Thin Films
### Boron-Doped Indium Selenide Films  
The boron-doped indium selenide (InSe) films were deposited on different substrates [120]. The XRD analysis showed a broad bump because of the glass substrate, and the intensities of the peaks reduced with the boron addition. The absorption tail began from 1.13 eV and ended at 1.96 eV, while Urbach energy was 792 meV when the substrate was on the glass slide.
# Nonlinear Dynamics of a Resonant-Impact Dielectric Elastomer Actuator
## 2 Methods  
### Design Overview and Working Principle  
The resonant-impact DEA system studied in this work mainly consists of a double cone DEA and a constraint, as illustrated in Figure 1a. The double cone configuration is adopted as the driving unit due to its ease of fabrication and large stroke/force output [38; 39; 40]. It consists of two identical circular DE membranes connected by a rigid rod in the center and support rings in the outer ends, which deform the two membranes to symmetrical conical shapes. An elastic constraint is fixed to one side with a gap to complete the resonant-impact DEA system. In this design, the gap is set to be higher than the quasi-static stroke of the DEA, i.e., no impact occurs when the excitation frequency \(\Omega_{e}\) significantly differs from the resonant frequency of the DEA unit \(\Omega_{r}\). When anti-phase voltages are respectively applied to the two DE membranes at an excitation frequency close to \(\Omega_{r}\), the oscillation amplitude of the moving mass (including the central rod and disks) increases to be greater than the gap, which realizes periodic impacts of the system. The motion of the resonant-impact DEA system in one typical impact oscillation cycle is illustrated in Figure 1b. It can be noted that the moving mass first departs from the constraint, which results in an increase in the elastic potential energy in the system due to the stretched membranes. When the moving mass passes its top dead-end, the stored elastic potential energy is released and converted to the kinetic energy of the moving mass. When impact occurs, the kinetic energy drops sharply and a part the energy is transferred to the constraint as the output.  
Figure 1c compares the displacements and reaction forces on the rig (which can be considered as the force output) of the proposed resonant-impact DEA and an identical DEA counterpart without the impact constraint. It can be noted that, despite the smaller displacement amplitude near resonance, the proposed resonant-impact DEA can exert a substantially higher peak output force than its counterpart without the impact constraint.
## 2 The Proposed SAR ADC
### Drive Circuit for DAC Capacitor Array  
The switching scheme designed for DACs directly affects the complexity of the control logic, making the design of a driver circuit difficult and raising the number of transistors used. As a result, a complex control logic consumes more power, resulting in a larger percentage of power being consumed by the digital module and increasing the overall power consumption of the SAR ADC. This work designs the simplest DAC driver circuit that could reduce DAC switching energy while minimizing the use of transistors.  
As shown in Figure 5, only two reference voltages are required per capacitor. The reference voltages of the capacitors in the main array and subarray are V\({}_{\text{ref}}\) and GND; the main array drive circuit only needs to pass through the CMOS inverter, and the capacitors in the subarray need to be controlled with AND and NAND gates, respectively. The reference voltages of the capacitors in the unit array are V\({}_{\text{cm}}\) and GND, and the drive circuit adopts a hybrid structure comprising a CMOS inverter and a CMOS transmission gate.  
Figure 4: Diagram of voltage change: (**a**) waveform of the proposed switching scheme; (**b**) flow chart of the 10-bit SAR ADC.
# Quality Analysis of Unmanned Aerial Vehicle Images Using a Resolution Target
## 4 Results
### MTF Analysis  
In Table 4, \(\sigma_{MTF}\) denotes the standard deviation of the MTF; the smaller the value of \(\sigma_{MTF}\), the clearer the image. \(MTF_{50}\) and \(MTF_{20}\) were also calculated and compared. Considering the \(\sigma_{MTF}\), the image quality worsened as the flight height increased in the MTF analysis, similar to those in the GSD, ground resolved distance (GRD), and edge response analyses.  
As shown in Figure 12, the iXM-100 sensor maintained a high MTF value as the spatial frequency increased to a height of 150 m and showed significantly better MTF results than those for the other camera sensors. The \(\sigma_{MTF}\) value for the iXM-100 sensor was the lowest at 0.331 (smaller the \(\sigma_{MTF}\) value, better is the image quality). The image quality worsened from top to bottom in the order of the MTF curves of the sensors presented in Figure 12. Specifically, the iXM-100 sensor exhibited the best results at a height of 150 m with \(\sigma_{MTF}\) = 0.331, \(MTF_{50}\) = 0.545 lp/pixel, and \(MTF_{20}\) = 0.831 lp/pixel. The UltraCAM Eagle M2 manned aircraft sensor exhibited the worst results, with \(\sigma_{MTF}\) = 0.715, \(MTF_{50}\) = 0.263 lp/pixel, and \(MTF_{20}\) = 0.399 lp/pixel. At the same height of 150 m, the boundary of the black and white lp of 10 cm width began to blur from the GSD values of 3.31 cm for the iXM-100 sensor, 4.58 cm for the a6000 sensor, 3.75 cm for the FC 6520 sensor, and 2.65 cm for the FC 6310 sensor. The boundary of the black and white lp that was 10 cm in width was no longer identifiable above the values of 8.31 cm, 6.98 cm, 5.72 cm, and 4.04 cm for the iXM-100, a6000, FC 6520, and FC 6310 sensors, respectively. Moreover, the boundary of the black and white lp that was 10 cm in width started to blur from the GSD value of 2.63 cm for the UltraCAM Eagle M2 sensor, which exhibited the worst performance, and was no longer identifiable above 3.99 cm.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Camera Model** & **Flight Height** & **Slanted Edge Target** & \(\sigma_{MTF}\) & \(MTF_{50}\) & \(MTF_{20}\) \\ \hline a6000 & 150 m & & 0.401 & 0.456 & 0.694 \\ \hline FC 6520 & 150 m & & 0.511 & 0.381 & 0.582 \\ \hline FC 6310 & 80 m & & 0.443 & 0.426 & 0.648 \\ \hline FC 6310 & 100 m & & 0.522 & 0.336 &
## 4 Summary and Conclusions  
In this study, we investigated a data- and ML-based approach for the spectral conversion from the encounter wave frequency spectrum \(S_{e}(\omega_{e})\) to the original-frequency wave spectrum \(S(\omega)\). This conversion is essential when waves and motions are measured from vessel-mounted sensors during navigation with forward speed. The spectral conversion can straightforwardly be performed by an equation in head-wise wave directions, but challenges arise in following-wise wave directions for certain vessel speeds due to the non-unique nature of the inverse problem. To resolve the problem under that condition, we employed an artificial neural network (ANN) model to estimate \(S(\omega)\) from \(S_{e}(\omega_{e})\) in a much more robust manner. Vessel speed (\(V\)), wave direction (\(\beta\)), and \(S_{e}(\omega_{e})\) values were used as inputs and \(S(\omega)\) values were obtained as outputs through the optimization of the ANN frame and hyperparameters. The model was trained using the synthesized data representing a pair of original and encounter wave frequency spectra for various wave parameters, vessel speeds, and wave headings. Our results demonstrated the effectiveness of the ANN model in accurately predicting the original wave spectrum from the encounter wave spectrum when that kind of spectral conversion is not possible. Regardless of various wave conditions and vessel speeds, it was shown that the ANN model robustly predicted the original wave spectrum with sufficient accuracy (R2 value of 0.987). The corresponding error distribution analysis provides valuable insights into the performance of the model across different wave parameters and vessel forward speeds. The applied ML method can also predict the bimodal (two-peaked) spectrum equally as well as the unimodal spectrum in the original-frequency domain. In the present paper, the incident random wave was assumed to be unidirectional. When a multi-directional random wave is considered, we can apply the same method while more wave parameters like directional spreading need to be employed. The proposed spectral transformation method from the encounter to original wave spectra is designed to be general and independent of specific ship types. However, the Kalman filter (KF) method introduced earlier in the manuscript does rely on ship-specific motion characteristics, as it uses the vessel's response amplitude operator (RAO) to reconstruct the wave spectrum in a vessel-specific manner.  
Conceptualization, J.P. and M.K.; methodology, J.P. and M.K.; software, J.P.; validation, J.P.; formal analysis, J.P. and M.K.; investigation, J.P. and M.K.; data curation, J.P.; writing--original draft preparation, J.P.; writing--review and editing, M.K.; visualization, J.P.; supervision, M.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 3 Controller Parameters Tuning
### Algorithm Flow of the G-PSO Algorithm  
The algorithm flow of G-PSO algorithm is shown in Figure 2, and the implementation steps are as follows.  
Step 1: Initialize the position and velocity of particles.  
Step 2: Update \(pbest\) and \(gbest\).  
Step 3: Calculate the inertia weight and learning coefficients according to Equations (17) and (18), respectively. According to Equation (6), update the position and velocity of the particles.  
Step 4: Generate the opposite population according to Equation (8). Calculate the fitness value of the opposite population and the initial population using Equation (19).  
Combine the two populations, then choose the top \(N\) particles with the best fitness to form a new population.  
Step 5: According to Equations (11)-(13), apply natural selection mutation to the population.  
Step 6: If the conditions are met, end the optimization algorithm and output the solution. Otherwise, return to step 2.
## 4 Results and Discussion  
### Clustering Results  
To determine the optimal number of clusters \(k\), the elbow method was applied by calculating the WCSS for \(k\) values ranging from two to ten, as shown in Figure 4. The WCSS curve exhibited a clear inflection point at \(k=4\), suggesting that four clusters offer the best balance between compactness and variance reduction. Based on this finding, k-means clustering was performed with \(k=4\), dividing the dataset into clusters that reflect seasonal variations and working conditions.  
With \(k=4\), the clustering results, shown in Figure 5, reveal that power consumption patterns were effectively categorized based on seasonal factors and work schedules. Cluster A represents working days from April to September, Cluster B represents working days from October to March, Cluster C corresponds to non-working days from April to September, and Cluster D represents non-working days from October to March. These clusters were then paired with corresponding LSTM models, each optimized for its specific cluster.  
Figure 4: WCSS-based elbow method for determining the optimal number of clusters.