esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploring Benefits of Using Blending Splines as Transition Curves
## 3 Method
### Blending Spline Fitting
#### 3.2.2 Local Curves  
The curves that can be used as local curves in a blending spline transition curve have to comply with three criteria. They must have the correct curvature for one end, be possible to connect to adjacent segments with a smoothness equal to or higher than the clothoid, and be scalable in length. The most intuitive curves to use as local curves in the blending spline, for this transition case, are circular arc segments, given by  
\[c(t)=(R\sin(t),R(1-\cos(t))),\quad 0<t<2\pi, \tag{7}\]  
with the same radius \(R\) as the original railway arcs (401 and 315, in this case). They have the correct curvature and smoothness for at least one end, are easy to scale in size, and easy to place correctly against the adjacent segments. An example of two local arc curves starting at entry point \([p_{3},q_{3}]\) is shown in Figure 5.  
In addition to the circular arcs, Bezier curves are also tested as local curves in the blending spline. Bezier curves are industry standard representations of free-form shapes, and are available in most design and CAD software. They are expressed as  
\[c(t)=\sum_{i=0}^{n}c_{i}b_{i,n}(t), \tag{8}\]  
where \(c_{i}\) are control points, and \(b_{i,n}(t)\) are Bernstein polynomials of degree \(n\). Compared to the arcs, the Bezier curves are more flexible as their shapes can be altered through control points. The Bezier curves in this research are created by approximating the previously described arcs via performing a Taylor expansion of a point and \(d\) derivatives in the entry points (which becomes the start of the Bezier curves). To achieve sufficient smoothness at the entry points, while also having the opportunity to  
Figure 4: The three entry points for the blending spline transition curve on the circular segments.  
manipulate the control point at the opposite end, the degree of the Bezier curves should be at least four (\(d>3\)). For the local Bezier curve examples in this paper, the number of derivatives extracted in the start point is 4, which means that \(d=4\) and 5 control points are placed.  
To maintain a smooth connection to the adjacent circular arc segments, while at the same time experimenting with the placement of the local curves, the first four control points, from the start of the curves, are fixed and the last control point is moved to the start of the local curve in the opposite end. A visual explanation is given in Figure 6. This is expected to give different results compared with using arcs as local curves, and to show if placing the local curves closer to the original clothoid has a significant effect on the curvature.
# Application of Molecularly Imprinted Polymers in the Analysis of Waters and Wastewaters
## 3 Synthesis of Molecularly Imprinted Polymers
### Surface Imprinting  
In this method, the template and functional monomer(s) are mixed to form pre-polymerization complexes; then, the polymerization is conducted on the surface of the solid substrate (silica, chitosan, magnetite, etc.) in the presence of initiators and cross-linking agents to form the imprinted layer. Knocking the templates out of the polymeric layer by physical or chemical methods results in the formation of three-dimensional cavities on the surface of the solid substrate [13].  
This method has many advantages, i.e., being easy to prepare, widely applicable, size- and shape-controlled, offering large surface area with highly selective binding sites [20], and being reproductive and sensitive compared to other methods [21]. It enhances the mass transfer, resulting in complete removal of the template as well as providing a good accessibility to target molecules. This technique is suitable for imprinting macromolecules, including proteins, cells, and viruses, as diffusion limitation was the major issue encountered in traditional imprinting attempts. It is commonly performed on support beads, which are mainly silica beads and are also used for the deposition of imprinted polymers. This approach imparts core shell structure to the final imprinted polymer, in which, briefly, the template and monomer are directly polymerized on the support beads, thus creating an MIP shell on the core support material. Other support materials could be used based on the application, including polystyrene and chitosan.  
An important class of surface imprinting involves the use of magnetite as a solid substrate. Magnetic nanoparticles (MNPs) with superparamagnetism can serve as adsorbents in the pretreatment process that offer a significantly higher surface area-to-volume ratio, resulting in much greater extraction capacity, and can be recovered efficiently with an external magnetic field. The adsorbents and substrates can be rapidly separated, free of a large amount of organic solvent, special equipment, and a slow and time-consuming processes, which are the shortcomings of traditional pretreatment methods.  
This method has few limitations, including the possibility of MIP film detachment upon repeated use and difficulty in controlling the thickness of the film.
## 4 Identification and Representation of Multi-Pulse Near-Fault Earthquake Motion
### Representation of Dominant Pulses
\[\mathbf{P}_{\theta}=\sum\
olimits_{i=1}^{i=N}\mathrm{p}_{i} \tag{7}\]  
After obtaining the multi-pulse component \(\mathbf{P}_{\theta}\) at the direction of \(\mathrm{\theta}_{max}\), the three criteria verified and used by Reference [1] were adopted in this paper to identify whether the extracted signal \(\mathbf{P}_{\theta}\) should be qualified as pulse-like motion: (1) pulse index (PI), (2) early-arrival of pulse velocity, and (3) omitted small PGV. One signal qualified as pulse-like if the following criteria were reached: (1) \(\mathrm{PI}>0.85\), (2) \(t_{10\%,\mathrm{pulse}}<t_{20\%,\mathrm{original}}\), and (3) \(\mathrm{PGV}>0.3\) m/s. \(t_{10\%,\mathrm{pulse}}\) is the time at which the extracted signal \(\mathbf{P}_{\theta}\) reaches 10% of its cumulative square velocity (CSV), \(t_{20\%,\mathrm{original}}\) is the time at which the original signal \(\mathrm{S}_{\theta}\) reaches 20% of its CSV, and PGV is the peak ground velocity of the original signal \(\mathrm{S}_{\theta}\). The cumulative square velocity is defined by Equation (8), where \(V(u)\) is the velocity time-history for \(\mathrm{S}_{\theta}\) or \(\mathrm{P}_{\theta}\). Detailed verification for these criteria can be found in [1], and is not covered in this paper for brevity. \[\mathrm{CSV}(t)=\int_{0}^{t}V^{2}(u)du \tag{8}\]  
The extracted parameters for the signal \(\mathbf{P}_{\theta}\), such as the number of dominant pulses, pulse periods, and mother wavelets adopted, were collected to characterize these extracted pulse components. Specifically, the parameter N defines the number of significant pulses. The pulse period \(\mathrm{T}_{i}\) of each dominant pulse \(\mathrm{p}_{i}\) embedded in the signal \(\mathrm{S}_{\theta}\) was calculated by Equation (9), where \(\mathrm{f}_{i}\) is the instantaneous frequency of \(\mathrm{p}_{i}\) in Hz, \(\mathrm{s}_{0,i}\) is the scale location that corresponds to the \(\mathrm{C}_{i,\mathrm{max}}(\mathrm{l}_{0,i},\mathrm{s}_{0,i})\), At is the sampling period of \(\mathrm{p}_{i}\), and \(\mathrm{f}_{c,i}\) is the central frequency of the specific mother wavelet selected to conduct the CWT in the \(\mathrm{i}_{th}\) iteration. \[\mathrm{T}_{i}=1/\mathrm{f}_{i}=\mathrm{s}_{0,i}\cdot\Delta\mathrm{t}/ \mathrm{f}_{c,i} \tag{9}\]
# Reproducibility and Data Storage for Active Learning-Aided Systematic Reviews
## 5 Data Storage Recommendations for Systematic Screening
### Input Data  
Sharing the search query, as requested by PRISMA, has become the standard in the field. For example, out of 117 systematic reviews published at Utrecht University in 2020, 91 published the entire search query [42]. [PERSON], [PERSON] [43] suggested a data structure for transparent and repeatable reporting of bibliographic searching. We advise users to follow such recommendations. A standardized way to report a bibliographic search might sound desirable, but reproducing such a search years later is problematic; journals are added to or omitted from databases, search functionalities change, and the meta-data are continuously updated. A solution would be to store the entire dataset resulting from the search; in the study, only 5 out of the 117 reviews did so.  
However, not all meta-data can openly be shared because abstracts might fall under the strict copyright of the publisher, which limits the application of global text and data mining research [44]. Until publishers adhere to the plea for releasing such restrictions, we propose storing a list of persistent digital object identifiers, like the DOIs. Then, via automated tooling, using Lens [45] or OpenAlex [46], it is possible to compose the full meta-data via DOI-matching. Note that storing the DOI is also not perfect since the meta-data associated with a DOI can change, for example, via an updated mesh term or keyword, but the object it refers to is persistent.
# Ballasted Track Status Evaluation Based on Apparent Track Stiffness Index
## 1 Introduction
In the European research project (EUropean Research to Optimise BALLasted Track, EUROBALT) [6] for the improvement of the design, construction, and maintenance efficiency of ballasted tracks, optimal track stiffness was proposed based on the correlation between track stiffness and the standard deviation of track irregularity. [PERSON] [10] also developed a measurement vehicle for continuous ballasted track stiffness evaluation. In Japan, track stiffness was measured using a falling weight deflectometer (FWD) in a sleeper [11]. In Korea, ballast compaction is performed using equipment such as multiple tie tampers during track laying and maintenance to ensure sufficient stiffness [1]. [PERSON] et al. [12] used ground-penetrating radar and a light falling weight deflectometer (LFWD) on the Gyeongbu line to evaluate the adequacy of the ballasted track maintenance work. [PERSON] et al. [13] evaluated the apparent stiffness of ballasted track by applying a load to the sleeper using the LFWD while the sleeper was connected. They reviewed the applicability of the apparent stiffness as an index to represent the ballasted track status. The apparent track stiffness is a ratio of the maximum displacement to the maximum load that occurs in the sleeper. The apparent track stiffness is a concept different from the general track stiffness in the case of a hanging sleeper. The sleeper is connected to rail during testing; thus, the apparent track stiffness cannot include the effect of the ballast and subgrade layers. In spite of this limitation, the apparent stiffness measurement using LFWD was found to be simple to measure and easy to carry; moreover, it can better represent the track condition for a train passing. In this study, we propose a new evaluation standard for ballasted track status to overcome the limitations of the existing maintenance work that has been performed based on TQI without a fundamental cause investigation for track irregularity. The new ballasted track status evaluation standard is based on apparent track stiffness; this is related to track stiffness, which affects track irregularity directly. The concept of the stiffness index, defined as the difference between the average and standard deviation of apparent track stiffness, is introduced, and the correlation among the stiffness index, TQI, and cumulative tamping length is analyzed to propose an evaluation standard for the ballasted track status based on the stiffness index.
## 4 Discussion  
The experimental results obtained for each case study, as shown in Tables 6-8, are aligned with those obtained in the previous literature [36], thus confirming the robustness of the physical tests carried out and the methodology applied to measure the melt pool dimensions. Furthermore, from the evaluation of the melt pool morphology and analysis of the printability maps derived for each case study, it is possible to observe that the printability region becomes narrower as the layer thickness is increased, emphasizing the criticality of printing high-layer-thickness parts in the L-PBF process [33; 34; 35].  
The evaluation of the physical and ML model results is shown in Figures 3 and 4; it is possible to highlight a good match for both the melt pool depth and width. From the comparison of the predictions for the melt pool depth made by the best ML regression model, the gradient boost tree, and the Rosenthal solution side by side, we can see that both models are good at correlating and being reliable, with R2 values of 0.95 and 0.82, respectively. In contrast, in terms of MAE, the gradient boosted tree, with a calculated MAE of 9.38, turns out to be significantly better than the Rosenthal solution, with a calculated MAE of 37.71. The gradient boosted tree is confirmed to be significantly more accurate and reliable for melt pool width prediction, with an R2 score of 0.75 higher than the calculated score for Rosenthal solution of 0.54. More specifically, the ML model exhibits a considerable improvement, with a 10-fold reduction in MAE compared to the Rosenthal solution (10.86 against 108.9). The shown (achieved) results are of particular interest due to the possibility of predicting the melt pool shape over a wide range of layer thickness values. This can speed up the development of high-productivity process parameters or facilitate the application of a printing strategy such as hull bulk. In addition, this ML model's ability to predict how the shape of the melt pool will change as the preheating temperature changes  
Figure 4: Comparison of the gradient boosted trees model prediction results (orange line) and the melt pool width’s physical results (blue line). The validation of the ML regression model is carried out on 20% of randomly selected data from the dataset, in this case using 79 randomly extracted physical observations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **R2** & **MAE** \\ \hline Depth & 0.82 & 37.71 \\ \hline Width & 0.54 & 108.9 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Rosenthal model statistical evaluation in the investigated case studies.  
in the 80 \({}^{\circ}\)C to 170 \({}^{\circ}\)C range that most commercial L-PBF machines are used in is essential for fully understanding how melt pools form. In this sense, it is important to emphasize that the highest preheating temperature is usually set for the machines with multiple lasers to minimize the greater heat loss due to the presence of multiple lasers.
## 2 Photodetector Performance Requirements  
It is useful to briefly recall the main performance requirements of integrated photodetectors for NIR optical applications. High speed, high responsivity, low dark current, low bias voltage and small dimensions are appealing properties for a photodetector and research efforts are now aimed at achieving all these in single devices.  
As bandwidth demand keeps increasing, it is essential that all-silicon photodetectors operate at 5 GHz, a frequency at which hybrid detectors have already been demonstrated [15]. Of course, higher bandwidths (20 GHz, \(>\)50 GHz) are desirable in order to anticipate future trends in optical interconnects. In fact, very high-speed photodetectors, combined with dense wavelength division multiplexing (DWDM) technology in the C band (1,528-1,561 nm) and L band (1,561-1,620 nm) [16], have the potential to achieve a bandwidth greater than 0.5 THz.  
Another important property of a detector is described by its responsivity, which indicates the current produced by a certain optical power. Reasonable responsivities are necessary for an acceptable signal-to-noise ratio and to ease the design and realization of the amplifier circuitry that follows. Responsivity is strictly linked to a device's quantum efficiency, a property describing how many carriers per photon are collected. It is worth noting that there is a difference between internal and external quantum efficiency: in the case of internal quantum efficiency, the number of carriers that contribute to the photocurrent is related to the number of absorbed photons, while in the case of external quantum efficiency, they are related to the number of incident photons. In the telecommunications field a responsivity \(\geq\) 0.1 A/W [17], corresponding to external quantum efficiencies \(\eta\) of 15%, 10%, and 8% at \(\lambda\) = 850, 1,300, and 1,550 nm, respectively, is required.  
In order to evaluate detector performance, an important property is dark current. This is a serious issue because the shot noise, associated with the fluctuations in a measured signal due to the random arrival time of the particles carrying energy, generates a leakage current which can increase the bit error rate (BER). Dark current depends on work frequency and it is worth noting that a higher dark current could be allowed if a system worked at frequencies at which the amplifier noise overcame photodetector noise. In a typical photodetector, dark currents less than 1 \(\mu\)A are required.  
A further requirement of photodetectors is low-voltage operation. It would be desirable to realize devices operating at the same power supply as the CMOS circuitry, _i.e.,_ bias voltage \(<\) 5 V and as low as 1 V for advanced CMOS generation.  
Finally, the shrinking of photodetector dimensions would allow for the integration of photonic components with integrated electronic circuits, enabling interconnection bandwidths that are not limited by the RC time constant or the reliability constraints of metal lines.
# A Simplistic Downlink Channel Estimation Method for NB-IoT
## 6 Discussion
\begin{tabular}{l l} AWGN & Additive white Gaussian noise \\ EPA & Extended pedestrian A model \\ LS & Least squares \\ MIB-NB & Master Information Block for NB-IoT \\ MLT & MATLAB LTE Toolbox \\ NB-IoT & Narrowband Internet of Things \\ NPSS & Narrowband primary synchronization signal \\ NRS & Narrowband reference signal \\ NSSS & Narrowband secondary synchronization signal \\ OFDM & Orthogonal frequency division multiplexing \\ PRB & Physical resource block \\ QPSK & Quadrature phase-shift keying \\ RMSE & Root mean square error \\ SIB1-NB & System Information Block Type 1 for NB-IoT \\ SNR & Signal-to-noise ratio \\ TIFA & Time interpolation frequency averaging \\ TIFI & Time interpolation frequency interpolation \\ UE & User equipment \\ \end{tabular}
# Bearing Fault Diagnosis Method Based on Improved VMD and Parallel Hybrid Neural Network
## 5 Discussion
### Generality Analysis  
To prove the generality of the proposed method, a further test was taken on the data set of the faulty fan end of the rolling bearing of Case Western Reserve University (CWRU) in the United States with the parameters of the model unchanged. There are nine fault types and one normal type in the data set. The nine fault types are rolling fault (B), inner ring fault (IR), and outer ring fault (OR). Each fault type is divided into 0.18, 0.36, and 0.53 mm single-point pits according to the fault diameter of the bearing. On the other hand, the experimental platform is equipped with unidirectional acceleration sensors at the drive end (DE), fan end (FE), and base (BA) of the motor, and contains four load conditions, namely load 1 (1796 r/min), load 2 (1772 r/min), load 3 (1748 r/min), and load 4 (1722 r/min). The fault sampling frequency is 12 kHz and the sampling time is 10 s, so a set of (3, 12000) tensor matrices can be obtained for each fault type, in which 3 represents  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Fault Diagnosis Model** & **Accuracy (\%)** \\ \hline IRDN & 97.37 \\ ACM-GIF-AOLSTM & 98.67 \\ LSTM-Cascade CatBoost & 99.33 \\ HMF-DL & 99.57 \\ Proposed method & 99.72 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Accuracy of different models.  
the vibration signals at three different positions and 12000 represents the total length of the signals.  
In this article, the signals at the driving end under four load conditions are employed as experimental data, and the length of the training sample is 1024 data points, which is consistent with the above. At the same time, the overlapping sampling method is employed to expand the training samples. The step size of the sliding window is 1/5 of the sample length, that is, 204 data points. Finally, the number of newly generated samples is 583. Considering the subsequent statistics and analysis of the experimental results, the first 500 samples are regarded as the data set. Therefore, the size of each type of fault set generated after data truncation is (500, 1024). Since there are 10 types, the size of the experimental data under each working condition is (5000, 1024). After improved VMD processing, the training set and test set are divided in the proportion of 8:2, and the training set is substituted into the parallel hybrid network after the data are normalized. The settings of network parameters and training parameters have not changed. The test set is substituted into the model for diagnosis. The results are shown in Table 8.  
The results show that the improved VMD parallel hybrid neural network has also achieved satisfactory results on the CWRU data set, and the average accuracy under various load conditions has reached 99.73%, which proves that the proposed fault diagnosis method can be applied to different bearing data sets and has certain universality.
# A Deep Learning Approach for Short-Term Electricity Demand Forecasting: Analysis of Thailand Data
## 5 System Model
### Data Preparation  
EGAT, the largest company in the Thai electricity market, provided the dataset that was applied in this research. EGAT processes 100% of the transmission system and nearly 50% of the generation. The nation is divided into five regions by EGAT, which also maintains separate records of the electric loads [41]. The dataset spans seven years (from 1 January 2012 to 31 December 2018) and contains 122,736 instances of half-hourly electricity demand (in MW). There are additional deterministic, historical load, and interaction variables in the preprocessed data. In the entire dataset, 5 years (from 1 January 2012 to 31 December 2016) were selected as the training dataset, 1 year (from 1 January 2017 to 31 December 2017) as the validation dataset, and the remaining 1 year (from 1 January 2018 to 31 December 2018) as the testing dataset. The dataset was split into three groups, known as scenarios, unlike previous research mentioned in [2] in which [41] they were divided into four groups. Splitting the dataset proved effective in simplifying the modeling and demonstrated a significant accuracy enhancement compared with the related work.  
Our available dataset comprised half-hourly (HH) 7-year data with 122,736 instances. Hence, the numbers of training, validation, and test instances for each HH in each scenario were as follows:  
* Scenario 1: all the demand from the whole dataset (everyday demand): training = 87,696; validation = 17,520; test = 17,520.
* Scenario 2: only demand for weekdays (demand without Saturday, Sunday, and other holidays): training = 58,848; validation = 11,760; test = 11,808.
* Scenario 3: only demand for non-working days (demand for weekends and holidays): training = 28,848; validation = 5808; test = 5664.  
Figure 10: Proposed electrical demand forecasting methodology.