esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
In [31], the subcarrier separation technology is used to allocate the subcarrier resources of the OFDM-based multi-user system to maximize the total rate. In [32], the subcarrier resources in the OFDM system are allocated on the basis of multi-user fairness. At present, satellite communication systems are one of the potential technologies for the development of 6G. The cooperative transmission schemes which are similar to the scheme proposed in this paper are introduced in the latest research of 6G. In [33; 34], ground cooperative transmission systems are introduced. In [11], the beams of GEO and LEO can cooperatively serve users. User information can be shared among all satellites. In [35], a multi-satellite cooperative access scheme is proposed. The users covered by multiple satellites can access these satellites at the same time. In [36], in the satellite-ground cooperative transmission network, a packet is cooperatively transmitted in multiple links at the packet level, which can allocate link capacity more finely. At the receivers, the tagged packets are reordered. However, it mainly focuses on the selection of the transmission path and lacks attention to resource allocation. In [37], an example of a multi-satellite joint transmission scheme is given. The user connects multiple satellites at the same time for joint transmission to obtain transmission diversity gain and increase system capacity. In [38], spot beams of GEO are used to coordinately transmit packets for mobile users. In [39], the spot beams of GEO are decomposed into virtual beams for coordinated transmission through beamforming of the gateway. There are problems of segmentation, synchronization, and signal processing. These problems have also been explored in the literature [40; 41; 42; 43; 44; 45; 46]. In [40], different segments of different contents are stored in cooperative caching systems. In [41], in order to prevent the interference packet from colliding with the expected packets, the packets are divided to reduce the bit error rate. In [42], the influence of different packet sizes on the system efficiency is analyzed. The system efficiency is improved by dividing the packets into different sizes. In [43], a satellite uplink time synchronization scheme that does not rely on time reference is proposed. In [44], a synchronization scheme is proposed in the multi-beam satellite system in the case of different beam differential delay transmission. In [45], a scheme of packet reorganization is proposed. In [46], the technology of packet reassembly in Wireless Sensor Networks improves the reliability of packet transmission. Therefore, inspired by the above researches, this paper proposes a multi-beam satellite cooperative transmission scheme based on cross-layer and cross-dimension resources optimization and irregular gradient adaptive packet segmentation to solve the problem of radio resource allocation in multi-beam satellite communication systems.
## 4 Theoretical Framework
### Time Series Modeling
#### 4.2.1 Autoregressive Model (AR)p  
With this model, described by Equation (1), the current value of the series is expressed as a function of the values that it took in the p previous samples weighted by a factor \(\varphi\_i\) and of a present random disturbance.  
\[\Phi_{p}(L)Y_{t}=u_{t} \tag{1}\]
# Artificial Intelligence in Biomaterials: A Comprehensive Review
## 3 Discussion
### Challenges and Limitations  
Despite the significant advancements, several challenges and limitations hinder the full potential of ML and AI in biomaterials research.  
* The development of robust ML models requires large, high-quality datasets [107]. However, the field of biomaterials often faces limitations in terms of standardized,large-scale datasets. Ensuring data quality and consistency is crucial for the reliability of ML predictions.
* ML models, particularly deep learning models, are often considered \"black-box\" systems, making it challenging to interpret their decisions [108]. Enhancing the interpretability of these models is essential, especially in applications where transparency and accountability are critical.
* Training complex ML models, such as deep neural networks, requires significant computational power and specialized hardware like GPUs and TPUs [109]. This requirement can limit the accessibility of ML techniques to researchers with limited resources.
* The use of ML in biomaterials research raises ethical concerns, including data privacy, bias in training data, and the potential for unintended consequences [110]. Addressing these issues is vital to ensure the responsible application of ML technologies.
* While ML can significantly accelerate material discovery and optimization, it is crucial to integrate these computational approaches with experimental validation [111]. Ensuring that ML predictions are experimentally validated can enhance the reliability and applicability of the findings.
* In biomaterials research, the biocompatibility of materials and potential toxicity issues are of paramount importance [8]. However, utilizing ML and AI techniques to evaluate and predict these factors presents notable challenges [12]. Moreover, the standards for sensitivity and accuracy required for biomedical applications are high. ML and AI methods play a crucial role in meeting these standards, yet their application in biomaterials research can be complex and demanding.
* Biomaterials often exhibit complex structures, making it difficult to fully characterize these materials [112]. ML models must effectively navigate this complexity to provide comprehensive insights.
# A Global Overview of SVA--Spatial-Visual Ability
## 5 Results
### Analysis by the Age Categories and Assessment Tools
#### 5.1.3 Ages 9 Years to 15 Years--Middle- and High-School Students (A3)
The study found a notable correlation between the education level of parents and their children's cognitive capacity, wherein higher parent education levels were connected to higher cognitive capacity [27]. In order to determine how engineering 3D modelling with SketchUp affects students' spatial thinking and visualisation abilities, the study conducted by [28] adopted the experimental approach of empirical research in education. Several assessments, including the Differential Aptitude Test: Space Relations, the Picture Rotation Test, the Mental Rotation Test, the Form Board Test, The Punched Holes Test, The Surface Development Test, and the Purdue Spatial Visualisation Test: Rotations, were employed to gauge the students' spatial visualisation skills. The findings established that the incorporation of spatial modelling with SketchUp in early science and technology classes can enhance the spatial-visualisation abilities of children [28]. In 2016, authors [PERSON], [PERSON], and [PERSON] developed the SRI (Spatial Reasoning Instrument) to assess spatial-reasoning abilities, particularly in mental  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Country** & **Year** & **Age** & **Sector** & **Objective** \\ \hline \multirow{4}{*}{[7]} & \multirow{2}{*}{USA} & \multirow{2}{*}{2013} & \multirow{2}{*}{13 years} & \multirow{2}{*}{A + G} & Determining the potential of spatial ability, & \multirow{2}{*}{mathematical reasoning, and verbal reasoning for future psychological research and their role in differential development across the lifespan.—SVA 1 (2D to 3D)} \\  & & & & & & \\  & & Grade 7 and & & & & \\  & Indonesia & 2021 & junior-high-school & \multirow{2}{*}{A + B} & Investigate the spatial cognition of students in geometry while differentiating between genders.—MRT \\  & & & & & & \\  & & & & & & \\  & & &  
rotation, spatial orientation, and spatial visualisation, for both educational and cognitive purposes. Correlations were calculated to assess the alignment of the SRI with established spatial-reasoning instruments, including the CRT, CCT, and PFT by [PERSON] et al., 1976 and the SOT by [PERSON] and [PERSON], 2001, as cited by [29].
## 1 Introduction
Hence, establishing a criterion for sampling frequency and period is crucial to ensure that the dynamic mode decomposition (DMD) output accurately reflects the system dynamics and its energy levels. In this paper, we study the convergence criteria of dynamic mode decomposition (DMD) parameters with respect to sampling frequency and window size. We conduct an in-depth analysis of mode responses in terms of shape and energy level. Additionally, we evaluate the performance of DMD in decomposing the flow field into meaningful modes with consistent energy levels and mode shapes. Our case study examines the flow past a square-back Ahmed body at a high Reynolds number, \(Re_{H}=7.7\times 10^{5}\). Initially, we discuss the numerical setup, followed by a brief overview of the DMD algorithm. Subsequently, we evaluate whether the flow field resolved by improved delayed detached eddy simulation (IDDES) is suitable for modal decomposition. In the results and discussion sections, we investigate the convergence criteria of drag force in different regions on the Ahmed body surfaces concerning sampling frequency and period. We then examine DMD parameter convergence (eigenvalues, growth/decay rates, and reconstruction relative error) regarding sampling frequency and time window. Furthermore, we analyze mode shapes across different sampling periods, leading to various frequency resolutions, and assess the consistency and uniqueness of DMD responses. Finally, we repeat all convergence studies with the mean value subtracted from the feeding matrix and compare the results to the classic DMD with the mean value included.
# Measurement of Sub-Surface Microstructures Based on a Developed Ultrasonic Atomic Force Microscopy
## 1 Introduction
These tasks apply additional ultrasonic excitation to the tip or the sample of the AFM system and reveal the sub-surface image of the sample. However, the measurement capability for the buried depth of the subsurface microstructures in these experimental samples is less than 500 nm [17, 18]. In addition to studying subsurface defects in samples, the CR-AFM technique can also be applied to study physical properties, such as stiffness, of various polymers or to examine the preparation quality of composite materials [19]. [PERSON] et al. [20] measured and mapped the transverse elastic modulus of three types of cellulosic nanoparticles: tunicate cellulose nanocrystals, wood cellulose nanocrystals, and wood cellulose nanofibrils by CR-AFM. [PERSON] et al. [21] obtained subsurface images from the same area of a polymer composite film containing single-walled carbon nanotube networks embedded in a polyimide matrix to estimate the depth of the buried carbon nanotube bundles by CR-AFM. It is desired to increase the buried depths of the detectable subsurface microstructures so that the measurement techniques can be applicable to more ultra-precision components. In this paper, to explore whether the ultrasonic AFM has the ability to detect deeper subsurface defects with a larger buried depth up to micrometric level, a new prototype of ultrasonic AFM is built. A new contact resonance method is employed to enlarge the detectable buried depth by the ultrasonic AFM and the change of the contact resonance frequency is taken as the feedback signal. Different silicon gratings covered by HOPG films with thicknesses ranging from 400 nm to 35 \(\upmu\)m are measured by the proposed method. Experiments show that the grating topography covered under the HOPG film can be clearly detected with a large buried depth up to 34.469 \(\upmu\)m, which greatly improves the detection range of the existing UAFM for measurement of subsurface topography with large buried depths.
## 1 Introduction
Based on the concept of the Standardized Precipitation Index (SPI), [PERSON] and [PERSON] [26] defined a standardized runoff index (SRI) as the unit standard normal deviation associated with the percentile of hydrologic runoff accumulated over a specific duration. A similar one is the Streamflow Drought Index (SDI) developed by [PERSON] and [PERSON] [22], where the streamflow sequence fits a particular distribution. Based on this, [PERSON] et al. [27] preferred the distribution parameters. The quantification of hydrological shortages along with drought indices is based on periods when hydrological variables, such as river flows, are below a certain threshold [27]. The selection of this level can use the average of a series [28], a percentile [29], or relative values for the average [30]. Additionally, a flow duration curve (FDC) is one of the most informative methods of displaying the range of river discharge from low flows to flood events [31]. In the United States, the most widely used indices are the 7-day, 10-year low flow (7Q10) and the 7-day, 2-year low flow (7Q2), which are defined as the lowest average flow occurring on seven consecutive days within the 10-year and 2-year recurrence intervals, respectively [32]. In the UK, the average of the annual series of minimum 7-day flows was used [33]. The definition of low flow varies from country to country and region to region. For example, the low flow definition in ungauged catchments is designed to predict specific low flow indices (e.g., Q95 or 7Q2) or to generate continuous flow time series using hydrologic simulation methods [32]. To respond to drought conditions (as determined by information on water sources and a drought warning system), it is necessary to determine the drought conditions in ungauged areas based on river and groundwater information. The aim of this study was to effectively prevent and respond to the negative effects of limited or disrupted water supplies (e.g., widespread inconvenience or damage to economic activity at a national scale) by proposing a plan to predict droughs and formulate appropriate responses, by judging the number of water abstraction days for local water sources or ungauged river intake stations, under changing climate conditions.
## 5 Results  
Two different recordings of the scene were evaluated at different ranges between the radar platform and the moving target. For both sets of data, a maximum duration of \(T_{max}=12\) ms of recorded data was deliberately chosen in agreement with the considerations made in Section 1.2.  
Figure 7 presents the evaluation of \(R^{(\tau_{D})}_{sg}(T)\), as defined in (22), for a target distance of \(D=51\) m. The zero points of the curve are reasonably deep and their mutual distance corresponds to a mean Doppler shift of \(f_{D}=441\) Hz, resulting in a relative velocity of  
\[v_{r,1}=7.056\text{ m/s},\]  
that, being equivalent to 25.4 km/h, matches the value of the speed difference of both vehicles. Table 2 presents the four Doppler frequencies obtained by pairwise computing the distances of subsequent zero positions. The standard deviation of the measured data is equal to 10 Hz resulting in an accuracy of \(\Delta v_{r}=0.16\) m/s (0.58 km/h).  
Figure 5: The radar was mounted to the rear of a standard car for moving-platform experiments on a temporarily closed runway.  
Figure 6: The radar platform (white) overtakes a truck (red). The velocities differ by 25 km/h, according to the tachometer values.  
The second recording was made at a mutual distance of \(D=141\) m between the radar platform and the target vehicle. The analysis creates a function similar to that of Figure 8. This more distant measurement indicates that at increasing target distances, noise adds to the curve due to the decreasing signal power on receive but still the zero positions are very pronounced.  
A comparison of both measurements leads to the observation of a marginal velocity change from \(v_{r,1}=25.4\) km/h to \(v_{r,2}=22.84\) km/h, reasoned by the fact that at \(D=141\) m the radar platform gradually approached the end of the track and initiated a braking  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Zero Position** & **Corresponding Doppler Frequency** \\ \hline
2 & 454.75 Hz \\
3 & 439.75 Hz \\
4 & 440.33 Hz \\
5 & 430.48 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 2: The mutual distances between the four zero points of Figure 7 allow for a determination of the echo’s Doppler frequency.  
Figure 8: A second measurement recorded a few moments later with a target distance of \(D=141\) m shows that a marginal variation of the velocity results in significantly different positions of the zero points relative to the previous measurement.  
Figure 7: The Doppler extraction method applied to radar data obtained for a target in a distance of \(D=51\) m.  
process. This marginal change in velocity creates a significant shift of the zero points in Figure 8 compared to those of Figure 7, which is easily noticeable by eye.  
Thus, the experimental data show the high sensitivity of (22) even on marginal velocity changes underlining the precision of the velocity measurement achievable by the method of this paper.
# Removal of Chromium(VI) by Chitosan Beads Modified with Sodium Dodecyl Sulfate (SDS)
## 2 Materials and Methods
### Adsorption Experiments  
The SDS-chitosan beads synthesized in Section 2.1. were employed as the adsorbent of Cr(VI) in this study. The beads were placed in a 200 mL conical flask containing 50 mL of aqueous solution with a known amount of Cr(VI), and the suspensions were placed in a constant temperature-shaker (TAITEC Plus Shaker EP\({}^{-1}\) with Thermo Minder SX-10R) set at a prescribed temperature. Adsorption experiments were performed in the pH range of 1-7, SDS initial concentration of 10-9000 mg: L\({}^{-1}\), contact time of 1 to 72 h, adsorbent dosage of 0.01-0.06 g:L\({}^{-3}\), temperature of 288-318 K, and initial Cr(VI) concentration of 0.1-3.0 mg: L\({}^{-1}\). The pH of the test solution was adjusted by adding 0.1 mol:L\({}^{-1}\) NaOH or HNO\({}_{3}\). Secondary to the adsorption experiment, the suspension was filtered with 0.45 \(\upmu\)m filter paper (Mixed Cellulose Ester 47 mm, Advantec MFS, Inc. (Tokyo, Japan)), and the concentration of Cr was determined by an ICP-MS (Thermo Scientific Center: X-series II). Replicate experiments were basically performed three times. The operating conditions of ICP-MS are shown in Table 1.  
\[q_{e}=\frac{(C_{i}-C_{e})}{m}.V \tag{1}\]  
where \(q_{e}\) represents the adsorption capacities at equilibrium (mg:g\({}^{-1}\)), \(C_{i}\) and \(C_{e}\) are the initial and equilibrium concentrations of Cr in a batch system, respectively (mg:L\({}^{-1}\)); \(V\) is the volume of the solution (L); \(m\) is the weight of adsorbent (g).
# Bisector-Based Tracking of In Plane Subpixel Translations and Rotations
## 1 Introduction
In [14], the authors proposed a technique to track translations within the image plane \(xy\) of an object without using a specific target or template-matching techniques, but with the specific need of the object image having two nonparallel straight segments. They tracked displacements with an accuracy of hundredths of a pixel, or even of thousandths of a pixel in the case of tracking harmonic vibrations. The motion of the object is described by tracking the intersection of two straight lines obtained from the digitization of the two segments from the scene, which allows an investigator to locate that point with a subpixel resolution. Unfortunately, the object tracking is done through a single-point target, which does not provide information about changes of orientation. In this paper, we propose a significant improvement of the technique presented in [14] and simultaneously obtain information about the object's position and orientation in the image plane with subpixel accuracy. The method explained below exactly locates the object using a vector consisting of the coordinates \((x_{c},y_{c})\) of the intersection of two nonparallel straight lines and the angle (\(\beta\)) with the horizontal axis of one of the bisectors of the angles between those lines. The manuscript is structured as follows. In the next section, we describe the theoretical basis of the technique. The Method section is set for explaining the algorithm developed to compute these parameters. In order to check the performance of this technique, we propose a theoretical simulation and two lab experiments in the fourth section, in which we describe those experiments and discuss the obtained results. Finally, the main conclusions of this paper are outlined.
## 2 Proposed Adaptive Finite Step Length Algorithm
### Adaptive Finite Step Length Algorithm
In the extreme case that \(\left\|\mathbf{u}^{k+1}-\mathbf{U}^{k}\right\|>>\left\|\mathbf{u}^{k}-\mathbf{U}^{k-1}\right\|\), \(\mathbf{U}^{k+1}\) is the point with a large deviation; this deviation will make the calculation inefficient, especially when the point nears the MPP. Therefore, such iteration points need to be optimized. The optimization model is defined as  
\[\mathbf{U}_{*}^{k+1}=\mathbf{U}^{k}+\theta^{k}\mathbf{d}^{k} \tag{14}\]  
where \(\mathbf{U}_{*}^{k+1}\) is the optimized iteration point of \(\mathbf{U}^{k+1}\), \(\theta^{k}\) is the optimized coefficient, and \(\mathbf{d}^{k}\) is the search direction that given by  
\[\mathbf{d}^{k}=\mathbf{U}^{k+1}-\mathbf{U}^{k} \tag{15}\]  
Theoretically, the optimized coefficient should minimize the value of the LSF along the search direction. However, it requires the exact line search to result in considerable computational effort [38; 39; 40]. For most optimization algorithms, the convergence rate does not depend on the exact search process. Therefore, the inexact line search can not only guarantee the acceptable reduction of the objective function but also make the final iteration sequence convergence, indicating which is powerful. The [PERSON] line search method [31] is defined as  
\[G\big{(}\mathbf{U}^{k}+\rho^{k}\mathbf{d}^{k}\big{)}\leq G\big{(}\mathbf{U}^{k}\big{)}+ \sigma\beta^{k}\
abla^{\mathrm{T}}G\big{(}\mathbf{U}^{k}\big{)}\mathbf{d}^{k} \tag{16}\]  
where \(\sigma,\beta\in(0,1)\) are pre-selected parameters, \(b\) is the smallest nonnegative integer satisfying the inequality, and \(\theta=\beta^{b}\). To ensure the global convergence, the merit function proposed by [PERSON] and [PERSON] [21] is used, which is given as  
\[m(\mathbf{U})=\frac{1}{2}\|\mathbf{U}\|+\rho\big{|}G(\mathbf{U})\big{|} \tag{17}\]  
where \(\rho\) is a constant and should satisfy \(\rho>\|\mathbf{U}\|/\
abla G(\mathbf{U})\). According to Equations (16) and (17), the optimized coefficient \(\theta^{k}\) can be calculated by  
\[\theta^{k}=\max_{b}\left\{\beta^{b}\Big{|}m\big{(}\mathbf{U}^{k}+\beta^{b}\mathbf{d}^{ k}\big{)}-m\big{(}\mathbf{U}^{k}\big{)}<0\right\} \tag{18}\]  
where \(\beta=0.5\) and \(\rho=\|\mathbf{U}\|/\
abla G(\mathbf{U})+10\) are used in the proposed method. In addition, the initial step length is generally selected as 50 in the FSL algorithm [29], which is unsuitable to a number of LSFs. In this paper, the initial step length is empirically defined as  
\[\lambda^{0}=\min\left(\frac{50}{\left\|\
abla G\big{(}\mathbf{U}^{0}\big{)}\right\| },50\right) \tag{19}\]  
where \(\mathbf{U}^{0}\) is the initial point, and the mean point in standard normal space is the same as the initial point. The convergence properties of the AFSL
## 3 Materials and Methods
### Type of Multi-Next-Hops Selection
#### 3.2.1 Disjoint Nodes  
Multi-link routing can result in paths whose nodes are disjointed. This case is known by a \"link-less\" routing, since no link is shared between the nodes or between the existing paths. An example of this category is shown in Figure 3(b). Indeed, the routes between the source node S and the destination node D (S-N2-N6-D and S-N3-N7-N8-D) are disjointed routes.