esa-sceva/satcom-chunk-collection huggingface dataset:
# A Passively Q-Switched Holmium-Doped Fiber Laser with Graphene Oxide at 2058 nm
## 3 Q-Switching of a 2058-nm Fiber Laser
This sort of pulse temporal behavior depending on pump power is a typical phenomenon, which is generally reported in passively Q-switched fiber lasers using an SA [16; 24; 35]. Subsequently, the average optical power, pulse energy, and repetition rate of the output pulses were monitored while changing the pump power. The results are illustrated in Figure 6. The pulse width decreased from 2.01 to 1.56 us as the pump power was adjusted from ~759 to 1072 mW, while the repetition rate changed from 45.56 to 56.12 kHz, as shown in Figure 6a. The pump power-dependent pulse width change could be attributed to the pump-induced gain compression effect [16; 35; 83]. The pulse energy as well as the average output power was observed to increase as the pump power was enlarged, as shown in Figure 6b. The maximum values of pulse energy and average output power were ~11.62 mW and 207.05 nJ, respectively, each at the maximum pump power of ~1072 mW. Finally, a performance comparison between previously demonstrated Q-switched Ho-doped fiber lasers with SAs and our laser is presented in the Table 1. It is obvious from the table that our laser exhibited better performance than the laser using a CNT-based SA in terms of pulse energy. From a perspective of pulse energy scalability, the Ho-doped fiber SA is the best among the three due to its high damage threshold. Apparently, nanomaterial-based SAs have damage threshold issues, which are critical to obtaining high energy pulses directly from a laser cavity. Figure 5: (**a**) Measured optical spectrum of the output pulses at pump power of 759 mW. (**b**) Measured oscilloscope traces of the output pulses at a variety of pump powers. Figure 6: (**a**) Measured repetition rate and temporal width of the output pulses as a function of the pump power. (**b**) Measured pulse energy and average output power of the output pulses as a function of the pump power.
## 3 Results and Discussion
### Zeta Potential and Effect of pH on P Adsorption  
Zeta potential is the electrical potential at the boundary of the hydrodynamic shear plane of the charged adsorbents close to an adsorbent surface where adsorption of ions from solution phase occurs and it is positively related to the surface charge. The higher the positive zeta potential the higher the anion exchange capacity, resulting in adsorption of larger amounts of anions such as phosphate. The positive zeta potential values obtained on the Dowex samples were nearly the same for the pH values 4 to 7 and they were slightly reduced at pH 8 (Figure 2). The reduction in zeta potential at  
Figure 1: EDS analysis of Dowex 21K XLT.  
the highest pH of 8 was due to the deprotonation of quaternary methylamine functional groups of Dowex at this pH resulting in decreased number of positive charges.  
The constant zeta potential in the pH range 4 to 7 suggests that the adsorption of phosphate should be the same in this pH range. However, the adsorption data shows that phosphate adsorption increased from pH 4 to 7 (Figure 2). This discrepancy may be due to the excess amounts of Cl added in HCl to reduce the pH in the experiment. Cl may have competed with phosphate for adsorption resulting in decline of phosphate adsorption as pH decreased. At pHs beyond pH 7.5 the phosphate adsorption again decreased. This is because of decrease in the positive surface charges as indicated by the reduction in zeta potential (Figure 2).
## 2 Research Methodology
### Preprocessing  
From the feature extraction section, a feature matrix is obtained containing the various features obtained from the signal processing for each observation. To improve the classification algorithms, the feature matrix can be rescaled using either normalization or standardization, when the features have different scales. Normalization rescales the data by squeezing the values into the range [0, 1]. This technique might be useful in cases where all parameters need to have the same positive scale, but normalizing the data can be sensitive to outliers. Standardization (or z-score normalization) rescales the data to have a mean value (\(\mu\)) of zero and a standard deviation (\(\sigma\)) of one. In this study, the choice of standardization is adopted since the data has a Gaussian distribution and there is no need to obtain a positive range for the feature matrix.
## 2 Materials and Methods
### Data Sets and Data Processing
#### 2.1.1 Dataset Statistics and Division  
The LUCAS dataset employed in this study comprised 19,036 samples, but individual feature columns exhibited varying degrees of missingness. To mitigate the impact of missing data on model training without incurring excessive data loss, the following strategy was adopted: for any target variable under prediction, only those rows with missing values in that specific column were removed. This approach balances sample size with data integrity, thereby enhancing model robustness and generalization. The remaining data were partitioned into a training set (66.6%) and an independent test set (33.3%), with fivefold cross-validation applied to the training set and 20% of the training samples withheld as a validation subset.  
Descriptive statistics were computed for each split, including the size (number of observations), mean (arithmetic average, reflecting central tendency), std (standard deviation, measuring dispersion), median (middle value, an alternative measure of central tendency), mode (most frequent value), kurtosis (peakedness, indicating tail heaviness), and lqr (inter-quartile range, a robust measure of spread). The results are summarized in Table 1.  
From a statistical perspective, nutrient-related variables such as organic carbon (OC), CaCO\({}_{3}\), total nitrogen (N), phosphorus (P), and potassium (K) display pronounced right-skewness; P and K additionally exhibit leptokurtic, heavy-tailed distributions, implying a prevalence of low values interspersed with a few extreme highs. High coefficients of variation for cation-exchange capacity (CEC) and K highlight marked spatial heterogeneity in soil fertility.  
With respect to soil texture, the mean fractions of clay (18.88%), silt (38.23%), and sand (42.88%) indicate that the study region is dominated by sandy loam. The close agreement between the median (37%) and mean for silt suggests an approximately symmetric distribution, whereas the wide lqr for sand denotes substantial variability in sand content. The large divergence between the mean (49.92) and median (20.80) of OC--togetherwith a kurtosis of 13.53--revealed a mixture of high-organic soils and typical arable soils. Collectively, the dataset captures the pronounced heterogeneity of European soils, posing a non-trivial challenge for predictive modeling. Nevertheless, the training and test sets exhibited strong concordance in key statistics (mean, standard deviation, median), and apart from a minor discrepancy in the lqr of K, all other parameters maintained stable lqr values across splits. This indicates a sound data partitioning strategy with no evidence of significant data leakage, thus providing a solid foundation for subsequent model training and evaluation.
## Materials and Methods
### Potentiometric Head Classification  
An inverse distance weighting (IDW) technique was used to obtain the potentiometric surface of the active model area. The point data used for interpolation was the average head data of prioritized wells over three and half years, based on the results of PCA. The logic for taking the average point data for obtaining flow patterns in the area was based on the cyclic trend of groundwater recovery. The potentiometric surface was then used to classify the groundwater head. The groundwater head variations in the study area were in the range of 22.89 to 41.37 m. A total of 19 classes were made with groundwater head variation of 0.97 m within each class. Three observation wells in each class were selected as prioritized wells. The aim of the study was to rationally reduce the number of observation wells, so we had two choices: either select the three wells with the highest scores in the PCA analysis or select three wells based on the spatial coverage of the groundwater head class. Each choice was tried, and the average standard error was determined for each network. The average standard error when selection was based on spatial coverage was lower than when it was based on the highest PCA analysis scores. Thus, the selection of wells in each class was based on the spatial locations of the wells. Figure 6b shows an example of how the wells can be selected using a head classification method. The contour classes (different colors) are of 0.97 m head variations. The wells shown in yellow are wells prioritized during the PCA stage. Of these yellow wells, red circled wells were selected as prioritized wells based on their spatial location in the groundwater head class (one well at the start of class, one well in the middle of the class, and one well at the end of the class). In addition, a few additional wells were selected based on the interpolation results, such as a few pockets of water which were part of a given class but were not continuous within that class. If there were already three or fewer wells in any class, they were left unaltered.
# Resonance Fatigue Behaviour of Concretes with Recycled Cement and Aggregate
## 4 Conclusions  
The main conclusions derived from this research are:  
* In the mixtures studied, the resonant fatigue limit was between 30 and 45% of the compressive strength.
* The fatigue results had a large scatter, and no direct influence on the resonant compressive fatigue limit was observed due to the presence of mix recycled aggregate.
* The presence of mixed recycled aggregates in the concretes increased the strain recorded during the resonance compressive fatigue tests. The presence of recycled cement did not affect the strain values.
* The resonance frequencies were lower in the concretes with MRA because the stiffness of the system provided by the presence of MRA increased the strain suffered by the specimen.
* For all cases, it was observed that during resonance fatigue tests with constant load cycles, as the strain increased, the resonance frequency decreased.
* In the compressive resonance fatigue tests, the unmixed MRA specimens exhibited fracture planes across the paste-aggregate interface. While the specimens with MRA showed cracks through the recycled concrete aggregates and pulverisation of the ceramic fractions.
* The strength of all the concretes was similar. The presence of mixed recycled aggregates had a greater effect on the dynamic response (increased strain and decreased resonance frequency) than the presence of recycled cement, which did not show any influence on the dynamic response.
* The characterisation in resonant fatigue allowed a reduction in test durations and the comparison of the fatigue life of different types of recycled concrete, even with recycled cement. However, the dispersion obtained in the results suggests that the more heterogeneous concretes are more sensitive to resonant fatigue. For this reason, for recycled concretes, the method does not seem directly comparable with the classical methods of static characterisation.  
[PERSON] and [PERSON] performed the experiments; [PERSON]-A., [PERSON] and [PERSON] analysed the data and wrote the paper; [PERSON] and [PERSON] supervised the research work and revised the paper. All the authors contributed to the experiment design and analysed and discussed the findings. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Spanish Ministry of Science and Innovation within the framework of the project BIA 2013-48876-C3-2-R.  
Data Waiability Statement: Data sharing not applicable.  
The first author benefited from the Ministry of Education, Culture and Sport's pre-doctoral grant FPU16/02693. The authors would like to acknowledge the \"[PERSON]\" postdoctoral grant program of the University of Cantabria for support.  
The authors have no conflict of interest that may have influenced the research described in this paper.
## 3 The Bike-Share Case
### Evaluation--Formulation of Simulation and ML Models  
Causal mapping establishes perceived bike availability, a synthesis of real-time and forecasted data, as the bike-share provider's key information source to assess outage risk and adjust incentive actions. Incorporating flow rate predictions helps smooth the impact of real-time fluctuations on outage risk perception. The data science component of the organisational mental model also includes predictions of how incentives will impact bike-share demand, specifically the expected increase in returns and rentals from crowdsourced inventory balancing. The accuracy of these predictions directly influences projected bike stock and future incentive decisions.  
Figure 5 illustrates how these information flows--expected demand and response to incentives--impact material flows and system performance.  
Figure 5: High-level overview of the bike-share inventory model, highlighting the main flows of rentals and returns and their interconnections within the system.  
A standard metric for evaluating performance is the service level, which in the bike-share context can be defined as [51]:  
\[\frac{(\text{potential customers}\,-\,\text{no-service events})}{\text{potential customers}}\]  
In our case, improving prediction accuracy reduces no-service events, enhancing service levels. Bike-share demand, both rentals and returns (_Base Demand_), is driven by market demand, influenced by bike availability for rentals and dock availability for returns. Realised demand serves as the basis for _Demand Forecasting_. Through data cleansing (de-censoring), realised demand is adjusted to remove distortions from stockouts and outliers, producing a version closer to true market demand (_Inferred Base Demand_). The Demand Forecasting process then generates a forecast (_Expected Demand_), incorporating factors like weather forecasts that correlate with customer demand.  
The _Inventory Management_ process defines the provider's policies for ensuring adequate stock at individual stations (disaggregation of bike inventory) to meet expected demand. It also monitors real-time availability and triggers balancing actions or customer incentives to prevent stockouts. The process relies on expected demand and incentive impact predictions (_Expected Response to Incentives_) based on causal inference from historical incentive effects (_Inferred Incentivised Demand_ from underlying _Incentivised Demand_).  
We now discuss the ML forecasting process and causal inference that form the backbone of the algorithmic content in the mental model.
## Nomenclature  
\begin{tabular}{l l} \(x_{i,j}\) & Distance from the center of SVS on the \(x\)-axis (width) at pixel \((i,j)\) \\ \(y_{i,j}\) & Distance from the center of SVS on the \(y\)-axis (height) at pixel \((i,j)\) \\ \(d_{i,j}\) & Distance from the center of SVS on the \(z\)-axis (depth) at pixel \((i,j)\) \\ \(d_{T}\) & Threshold of depth used to determine a possible path. \\ \(\overline{d}_{n}\) & Minimum depth at \(n\)-th column of ROI. \\ \(\overline{D}\) & set of \(\overline{d}_{n}\) \\ \(\overline{D}_{k}\) & \(k^{\text{th}}\) set of \(\overline{d}_{n}\) which are larger than \(d_{T}\) continuously, \(\overline{D}_{k}\in\overline{D}\) \\ \(\overline{d}_{m}^{k}\) & \(m^{\text{th}}\) element of \(\overline{D}_{k}\) \\ \(w_{T}\) & Threshold of width used to determine a possible path. \\ \(w_{k}\) & Width at the range of a selected \(\overline{D}_{k}\) in the real world. \\ \(i_{c}\) & The center of FOV on the horizontal axis of the image coordinate \\ \(i_{d}\) & The middle of the selected section \(\overline{D}_{k}\) on the horizontal axis of image coordinate \\ \(i_{c}\) & The detected circle center on the horizontal axis of image coordinate \\ \(i_{T}\) & Left edge of the center region on the horizontal axis of image coordinate \\ \(i_{T}\) & Right edge of the center region on the horizontal axis of image coordinate \\ \(i_{h}\) & Left hip node on the horizontal axis of image coordinate \\ \(i_{th}\) & Right hip node on the horizontal axis of image coordinate \\ \(d_{lh}\) & Depth of the left hip node \\ \(d_{rh}\) & Depth of the right hip node \\ \(i_{ch}\) & The center between the left and right hip node on the horizontal axis of image coordinate \\ \(d_{ch}\) & The distance from the center between the left and right hip node to the SVS \\ \(d_{hu}\) & Near threshold values of tracking length \\ \(d_{kf}\) & Far threshold values of tracking length \\ \end{tabular}
# GNSS-Assisted Visual Dynamic Localization Method in Unknown Environments
## 2 GNSS-Assisted Visual Dynamic Localization Method Framework
### Subsequent Frame Sequence Tracking and Position-Solving  
Here, the process of solving the algorithm for solving the subsequent image position was analyzed as an example of solving image \(I_{4}\). The following position-solving was carried out using a 2D-3D model.  
As Figure 3 shows, it was assumed that \(I_{4}\) and \(I_{3}\) had matching points with the same name, and here, the position n and orientations \(\mathbf{R}_{C_{4}}^{C_{1}}\) and \(t_{C_{4}}^{C_{1}}\) in the world coordinate system could be solved according to the RPNP algorithm. Then, \(\mathbf{R}_{C_{4}}^{C_{1}}\) and \(t_{C_{4}}^{C_{1}}\) were further obtained using BA (bundle adjustment) optimization with the RPNP (robust perspectiven-point) [19,20] computed value as the initial value and the reprojection error as the objective function.  
We defined the reprojection error as follows:  
\[\begin{split} e&=x-\mathbf{P}X\text{ and }\\ \frac{1}{2}\sum_{k=1}^{n}\left\|e_{k}^{\mathcal{C}4}\right\|^{2} =\frac{1}{2}\sum_{k=1}^{n}\left\|x_{k}^{\mathcal{C}4}-\mathbf{P_{4}}X_{k}^{W} \right\|^{2}.\end{split} \tag{13}\]  
The \(\mathbf{P_{4}}\) optimal solution was selected by performing BA optimization using a reprojection error.
## 3 Materials and Methods
### Experimental Setup  
Dataset Description and Preprocessing  
The real-world dataset was collected from _Katcher's_, a Korean e-commerce platform, over the period from April to July 2023. During this time, a total of 101,644 orders were recorded, out of which 23,564 orders (approximately 23%) involved concurrent purchases, as illustrated in Figure 1. The dataset includes 1727 unique products across 555 different categories. The data used in this experiment cover a diverse range of products, and the geographical region is limited to South Korea.  
Figure 1: Histogram depicting the number of products purchased per order. In the _Katcher’s_ service, 23% of all orders involve concurrent purchases, where multiple products are bought in a single order.  
The data were obtained directly from _Katcher's_ web portal, where user transaction histories were anonymized to ensure privacy. The dataset contains detailed information about each transaction, including product IDs, category IDs, and purchase timestamps. Only transactions made within the defined time frame (April to July 2023) were included in the dataset. The data were preprocessed to remove any incomplete or erroneous entries. Specifically, transactions with missing product IDs or categories were excluded from the analysis. Additionally, to eliminate outliers and ensure data consistency, only users with a total purchase count of fewer than 68 transactions during the collection period were included in the study.
# A Review of Harmonic Detection, Suppression, Aggregation, and Estimation Techniques
## 2 Harmonic Detection Principles and Methods
### Detection Based on Wavelet Analysis  
Another common harmonic detection method is the wavelet analysis method [19]; it does not use steady-state waveform signals, but a series of short-term oscillating waveform signals are used as the basis function. The research results show that the wavelet technique has an obvious advantage over the Fourier transform in the harmonic detection of dynamic changes; still, it is not as good as the Fourier transform in the detection of steady-state harmonics. Problems with this method are that, firstly, there is no definite standard for the selection of wavelet bases; secondly, there is the phenomenon of spectrum leakage and band aliasing; and thirdly, the resolution of the conversion process is low, which affects the detection of higher harmonics [13].
## 2 RNA Structural Requirements for Editing  
The host enzyme ADAR1 is responsible for editing the HDV amber/W site [7,8]. No additional factors aside from HDV RNA and ADAR1 are required [9,13]. ADAR1 contains a catalytic deaminase domain along with three dsRNA binding motifs (DRBMs) [14,15]. Specific adenosines in a number of cellular mRNAs and miRNAs have been identified as substrates for editing by ADAR1 and the closely related enzyme ADAR2 [16-20]. Two general features are common to all of the sites known to be edited by ADAR1 thus far. First, none have G as the base immediately 5' of the adenosine to be edited. In dsRNA substrates, which are subject to highly promiscuous editing, adenosines flanked by 5' guanosines are strongly disfavored for editing [21]. Second, all exhibit considerable base pairing that surrounds the editing site and extends at least about ~25 bp in one direction. In most cases base pairing extends 3' of sites and includes a limited number of mismatches, bulges and small internal loops. The current model for substrate recognition by ADAR1 is that the DRBMs recognize the base-paired region in the vicinity of the site and that the bulges and internal loops serve to position the catalytic deaminase domain at the adenosine to be edited [15,19,21-24] (Figure 2). This model is based primarily on the behavior of the _Xenopus_ homolog of ADAR1 on dsRNA and on analysis of interactions between the closely related enzyme ADAR2 and its substrates. For example, mutational analysis has indicated that the presence of a 6 nt internal loop strongly diminished editing of a dsRNA substrate by _Xenopus_ ADAR1 [22]. In the predicted secondary structure of most mammalian mRNA and miRNA ADAR1 substrates the base-paired region is contiguous with base pairing immediately surrounding the editing site, and internal loops and bulges are smaller than 6 nt. Perhaps the simplest editing site identified thus far is the ADAR2 GluR-B R/G site, which consists of a simple 28 bp stem-loop containing three single base internal loops. While the structural determinants of editing the GluR-B R/G site by ADAR2 have been analyzed extensively [24, 25, 26, 27], the specific RNA structural requirements for editing by ADAR1 substrates have not been analyzed to the same degree.  
As described below, the secondary structures required for amber/W site editing have been analyzed for two of the 8 HDV genotypes--type 1, which is the most common, and type 3, which is the most distantly related. The structures involved share the first feature common to other editing sites--the 5' base is not G, and at least some elements of the second, the amber/W site is present in a base-paired context. However, the overall size of the structure required for type 1 and type 3 HDV editing is considerably larger than for either dsRNA substrates of _Xenopus_ ADAR1 or the few substrates of ADAR2 editing that have been characterized.