esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods and Principles
### Equivalent Linear Dynamic Time-History Analysis Method
#### 2.2.2 Hydrodynamic Pressure  
It is widely acknowledged that seismic activity significantly influences the hydrodynamic pressure on the water-intake pipe culverts of nuclear power plants, markedly affecting the structure's dynamic response. Consequently, this pressure is a crucial dynamic load in the design of water-intake tunnels. Relevant regulations are specified in American standards (ASCE 4-98) [28]. In seismic response analysis, it is essential to consider both convective and impulsive effects arising from the oscillation of water flow within the immersed tunnel due to seismic activity. This oscillation induces pressure on the tunnel walls, necessitating the consideration of both horizontal and vertical components of fluid motion. The Housner spring-mass system [31], used to calculate hydrodynamic pressure, is illustrated in Figure 5.  
Figure 4: Schematic diagram of artificial boundary model.
# Comparative Study of Blockchain Hashing Algorithms with a Proposal for HashLEA
## 3 Background
### Hash Functions
#### 3.3.12 Skein  
The Skein hash function family, optimized for performance on 64-bit processors, was developed by [PERSON] et al. [80]. Skein is built on the tweakable block cipher Threefish, which supports block and key sizes of 256, 512, or 1024 bits. The distinctive chaining mode of Skein, designated as Unique Block Iteration (UBI), incorporates the Matyas-Meyer-Oseas construction with a flexible format specification for tweak values and a padding scheme. UBI functions as a unified mechanism for Initialization Vector (IV) generation, message compression, and output transformation, thereby facilitating the adaptability of Skein to a diverse array of use cases [81].  
The Skein system is based on the Threefish block cipher, which employs an ARX (Add-Rotate-XOR) construction. The addition is performed modulo \(2^{64}\), thereby ensuring efficient arithmetic operations. The bitwise XOR operation provides the desired non-linearity and the bit rotations contribute to the diffusion of the cipher state. The state of Threefish is organized into 64-bit words, with the MIX operation transforming pairs of these words. The rotation distances employed in the MIX operation are contingent upon the block size, round index, and word positions, thereby ensuring both variability and security. Threefish applies 72 substitution-permutation rounds to the input block, with subkeys derived via a straightforward key schedule that combines the cipher key and tweak value through the use of addition modulo \(2^{64}\). Subkeys are appended to the intermediate state at the conclusion of every fourth round [80].  
Throughout the SHA-3 competition, Skein benefited from a series of refinements designed to enhance its security. The key updates included the following: a tweaking of rotation constants after the second round with the aim of optimizing diffusion and an adjustment of constants in the Threefish key schedule during the final round with the objective of mitigating rotational cryptanalysis [82]. The minimal nature of these changes serves to highlight the robustness of Skein and its ability to adapt to advancements in cryptanalysis.
## 4 Discussion  
In each of the above three diagrams, it is noticeable that the two-tier scenario provided the most efficient network performance. Moreover, in all cases, better efficiencies were achieved with the small-cell scenario (250 pico BSs) than with macro cells (5 BSs) while preserving the same count and layout of users.  
Furthermore, regarding the two-tier scenario, it is evident that, in addition to improved performance, it enables significant financial savings as well.  
Moreover, considering different transmit power levels in the pico tier while keeping the macro-tier transmit power constant, \(SE\) shows a growing trend with respect to the ratio of transmit powers, Figure 5.  
From the curves, it is evident that \(SE\) of the whole HetNet under consideration exhibits exponential growth with respect to the transmit power ratio, when small cells are introduced around a single macro cell. However, with only a single macro tier, it can be seen that \(SE\) does not change with transmit power ratio variation.  
Consequently, the scenarios with a larger number of pico cells within the network inevitably lead to higher \(SE\), which is in accordance with the expected values coming out of the analytical model presented in Section 2.  
In summary, planning schemes affecting the efficiency of the overall heterogeneous micro/macro cell network, can adopt either BSs and UEs, being uniformly distributed in hexagonal geometrical structure, or their distribution that is based on stochastic geometry.  
Figure 4: Simulation results: (**a**) SE, (**b**) EE, (**c**) DE.  
\begin{table}
\begin{tabular}{c c c} \hline \hline _SINR_ & _SE_**[b/s/Hz]** & _EE_**[b/J]** \\ \hline
11.98 & 17.28 & 0.53 \\
11.06 & 15.96 & 1.04 \\
10.55 & 15.22 & 1.65 \\
9.86 & 14.22 & 3.09 \\
9.45 & 13.63 & 4.45 \\
9.16 & 13.22 & 5.75 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation results (after averaging).  
In this work, it is the latter one that we considered closer to reality than the former (deterministic) one.  
Contributions of this work stem from the following:  
Considering the locations of BSs and UEs to be PPPs, we introduced observing the OA small cells and not the small-cell CSG UEs. Having considered the CRE based selection of the serving BS, we derived analytical expressions for EE and SE for the two scenarios of interest.  
Moreover, we extended the joint analysis of SE and EE with the cost-oriented DE, to exhibit the trade-offs among the three parameters.  
Finally, our simulations have validated the significant benefits of cell planning according to the proposed model.  
It is possible to further improve the analysis in terms of complexity and fairness, if the model is extended by various mechanisms of network resources' management [24], or by other PPP variants, such as Poisson Cluster Process (PCP) [35] or Matern Hard-Core Process (MHCP) [27].
## 2 A Generic Multi-Path and Multi-Channel Proposal for HWMP
### Multipath Proposal and Implementation  
HWMP establishes by default a single path between the source and destination nodes. The purpose of this section is to modify the protocol to obtain and take into account all the possible paths between two nodes. As already said, these available paths will be assigned to the different applications (traffic types) depending on their priority. Therefore, the modules related to the route management, routing table and route assignment have been modified.
## 4 Discussion
### Phenology-Based and Unsupervised Classification Methods  
Machine learning and deep learning approaches can be applied as classifiers when mapping rice extent and cropping calendars [10; 72; 73; 74; 75; 76]. Since they are supervised classification methods, they require a large number of observations for model training. For example, a recent study [52] used 21,696 observations to train a convolutional neural network (CNN) model to map rice extent and growth stages in West Java, Indonesia. On the other hand, the phenology-based method used in this study does not require many observations. Our method based on k-means clustering can quickly identify rice fields from expert interpretation due to the unique spectra pattern of VH polarization and the NDVI of rice fields from Sentinel-1 and 2, respectively, resulting in high--up to 96%--accuracy rice maps. Due to its ability to generalize clusters from the data pattern, the k-means method can learn from the data and group time series into unique spectral responses related to rice phenology or other land uses. The method was also able to identify reflectance signatures due to soil tillage and planting, vegetative, reproductive, and ripening stages, as shown in Section 3.1. Nevertheless, to identify the reflectance signature, prior knowledge is required. To overcome this limitation, K-means and supervised methods such as random forest [75] can be combined to automatically produce rice extent and growth stage maps.  
An important feature of this study was the ability to map cropping intensity and cropping calendar data. RiceAtlas [77] and RICA [78] provide rice calendar and production data from around the world and rice crop calendars for Asia, respectively. However, those studies only present information on cropping calendars at the country level. In reality, the cropping calendar varies significantly within countries and could change due to local conditions. Using a phenology-based method, we have provided not only maps of rice extent but also cropping intensity and calendar at the pixel level. We have demonstrated that the unsupervised classification of Sentinel-1 and 2 time-series data and their implementation in the GEE is cost-effective in mapping rice extent, intensity, and calendars. In addition, the approach is scalable, and GEE can process data of vast areas [40; 79].
## 3 Results and Discussion
### EM Wave Absorption Properties
Table 1 presents the EM absorption properties of some Fe\({}_{3}\)O\({}_{4}\)-based absorbers reported in the literature. The composite reported in this work displayed enhanced MAP as compared with those reported works. The EM wave attenuation coefficient which describes the capability of the absorptive material to rapidly attenuate and dissipate EM wave via its inherent magnetic and dielectric loss power can be measured by the attenuation constant (\(\propto\)) [25]. Figure 6:3  
Figure 11 clarifies that the attenuation of the samples intensifies with increase in C-Fe\({}_{3}\)O\({}_{4}\) in the PVDF matrix. The samples possess moderate attenuation with M40 displaying the highest attenuation which favors the absorption of EM wave. This finding reveal that combined moderate attenuation and good impedance matching promotes the EM wave absorption reported in this study. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Absorbers** & **Filling Ratio** & \begin{tabular}{c} **Optimal RL (dB)** \\ **(\(\upupsilon\)\(\upupsilon\))** \\ \end{tabular} & \
\begin{tabular}{c} **Thickness** \\ **(\(\upupsilon\)\(\upupsilon\))** \\ \end{tabular} &
\begin{tabular}{c} **Bandwidth** \\ **(\(\upupsilon\)\(\upupsilon\)\(\upupsilon\))** \\ \end{tabular} & **References** \\ \hline Fe\({}_{3}\)O\({}_{4}\) & 60 & \(-\)21.2 (5.7) & 2.0 & 3.6 & [9] \\ nanoparticles & 20 & \(-\)30.0 (9.5) & 4.0 & 4.2 & [37] \\ Fe\({}_{3}\)O\({}_{4}\)/C/PVDF & 60 & \(-\)36.5 (11.1) & 5.0 & 2.3 & [38] \\ MWCNT/Fe\({}_{3}\)O\({}_{4}\) & 70 & \(-\)18.2 (12.04) & 2.0 & 1.04 & [39] \\ Fe\({}_{3}\)O\({}_{4}\)/CF & 50 & \(-\)8.0 (9.2) & 2.0 & 0.7 & [40] \\ Fe\({}_{3}\)O\({}_{4}\)/C flowers & 50 & \(-\)54.6 (5.7) & 4.27 & 6.0 & [7] \\ Fe\({}_{3}\)O\({}_{4}\)/SiO\({}_{2}\)/PVDF & 40 & \(-\)28.6 (8.10) & 2.5 & 2.11 & [13] \\ Fe\({}_{3}\)O\({}_{4}\)/CF & 50 & \(-\)11.0 (10.37) & 1.70 & 1.03 & [41] \\ Fe\({}_{3}\)O\({}_{4}\)/C & 60 & \(-\)46.0 (15.50) & 2.5 & 6.80 & [42] \\ C-Fe\({}_{3}\)O\({}_{4}\)/PVDF & 20 & \(-\)43.9 (15.13) & 3.5 & 2.45 & This work \\ C-Fe\({}_{3}\)O\({}_{4}\)/PVDF & 40 & \(-\)41.75 (14.96) & 3.0 & 2.01 & This work \\ \hline \hline \end{tabular}
\end{table} 
Table 1: MAP of some Fe\({}_{3}\)O\({}_{4}\)-based composites reported in previous references and this work. Figure 10: RL curves for (**a**) M0, (**b**) M10, (**c**) M20, and (**d**) M40 with different thickness at x-band (8.2–12.4 GHz) and Ku-band (12.4–18 GHz) frequency.
## 2 Materials and Methods
### Statistical Analyses  
The baseline data were organized and summarized using Excel 2019. Statistical analyses were performed using IBM SPSS 29.0 software. The Shapiro-Wilk test was employed to assess normality distribution, with \(p<0.05\) indicating non-normal distribution, in which case, rank-sum tests were utilized for differential analysis. For normally distributed data, the results are expressed as the mean \(\pm\) standard deviation. Paired-sample \(t\)-tests were conducted to analyze the pre- and post-intervention indicators within the experimental and control groups, while independent-sample \(t\)-tests were used to analyze the between-group differences in performance indicator changes. The effect sizes were interpreted according to [PERSON]'s criteria, where d \(<0.2\) indicates a negligible effect, \(0.2<\) d \(<0.5\) represents a small effect, \(0.5<\) d \(<0.8\) denotes a medium effect, and d \(>0.8\) stands for a large effect [22].  
Finally, analysis of covariance (ANCOVA) was carried out on the post-test scores of the two groups. ANCOVA was used to evaluate the intervention effect of the training methods on the outcome indicators. The dependent variable was set as the post-test scores, the fixed factor was the group, and the covariate was the pre-test scores. In this way, after excluding the influence of the pre-test scores, the post-test scores of the experimental and control groups could be compared, and the innate differences between the subjects reflected by the pre-test could be eliminated. The Bonferroni method was used to correct the comparison results. Partial eta squared (\(\eta^{2}\)) served as the metric for quantifying the effect size of the between-group differences in intervention outcomes. When calculated, an \(\eta^{2}\) value falling within the range of \(0.01\leq\eta^{2}\leq 0.06\) was regarded as small, a value in the range of \(0.06\leq\eta^{2}<0.14\) was considered moderate, and an \(\eta^{2}\) value of \(\eta^{2}\geq 0.14\) was deemed to be large.
## Materials and Methods
### Experimental Procedures
#### 2.2.8 Transport Test Evaluation  
To ensure that the prepared nanoparticles suspensions could be transported through a porous medium, their transport and adsorption behavior were analyzed by experiments in an unconsolidated sand porous medium column test. The porous medium was constructed in a liquid chromatography column (diameter 2.5 cm, length 15 cm), filled with sand and sealed with two PTFE (polytetrafluoroethylene) end fittings, connected to a peristaltic pump (Masterflex(r) L/S peristaltic pumps, Cole-Parmer, Vernon Hills, Illinois, USA). The test consists of subsequent fluid injections in the column: 1 pore volume (PV) of distilled water, 3 PV of surfactant solution or nanoparticle suspension and finally 3 PV of distilled water. The concentration of the active substance (surfactant or nanoparticle) in the injected fluid was 0.1 (_m/v_) %, and the pumping flow rate was 0.1 mL/min. During the experiment, every ten ml of effluent were collected and analyzed by TOC detection for the surfactant-only injections or by UV-Vis spectrophotometry at 400 nm in the case of nanoparticles injection. The active substance content in the effluents allowed to plot the breakthrough curves, i.e., the relative concentration \(C/C_{0}\) (\(C\)--concentration in the effluent and \(C_{0}\)--initial concentration injected) as a function of the pore volume [11; 33]. Besides, to evaluate the content of adsorbed material in the medium, a graph of cumulative content recovered versus the pore volumes injected was obtained.
# Design and Implementation of a Multi-Function Hydrophone for Underwater Acoustic Application
###### Abstract  
In recent years, underwater acoustic applications have attracted much attention, for example, for underwater environmental monitoring, underwater exploration, etc. Hydrophones play a particularly important role. Although hydrophone design has been in multifarious application forms, it still needs to consider increasing demand for low-cost, low-consumption, and multiple-function devices, as well as issues around miniaturization, lossless data collection, etc. In this paper, we design a compact underwater acoustic device that has the capability of underwater acoustic signal storage, underwater acoustic signal transmission via the Internet, and decoding based on the direct sequences spread spectrum (DSSS). The key problem is how to implement multiple functions in only one micro-controller unit (MCU). The hardware and software of the proposed multi-function hydrophone are described in detail. In particular, the MCU, the pre-amplifier with gain control, and the analog-to-digital integrated chip are introduced. Moreover, underwater acoustic data storage, underwater acoustic transmission, and the DSSS receiver are depicted in terms of software. The different functions of the hydrophone are verified in sea trial experiments. The results show that the proposed multi-function hydrophone is able to sample underwater acoustic data at high quality. In addition, to demonstrate configurable parameters, the DSSS receiver with different carrier frequencies is provided. The proposed multi-function hydrophone realizes zero bit error rate (BER) when carrier frequency \(f_{\mathrm{c}}=9\) kHz, and the BER with \(10^{-3}\) order of magnitude when carrier frequency \(f_{\mathrm{c}}=15.5\) kHz. The results show that the proposed multi-function hydrophone has great potential to explore the ocean.  
multi-function hydrophone; underwater acoustic data acquisition; real-time transmission; parameter configuration; DSSS receiver +
Footnote †: journal: Marine Science and Engineering
## 2 Methods  
### Theory Analysis of Combiner Branch  
A multi-way combiner comprises multiple branches and isolation resistors according to RF combiner theory. These branches may utilize either lumped-parameter elements or transmission-line structures, or both. Consequently, the fundamental performance of an individual branch was prioritized for analysis.
## Notations  
\begin{tabular}{l l} Symbol & Explanation \\ lowercase \(a\) & scalar \\ bold lowercase \(\mathbf{x}\) & vector \\ bold uppercase \(\mathbf{X}\) & matrix \\ \(\|\mathbf{x}\|\) & Euclidean norm of a vector \\ \(\mathbf{x}(i:j)\) & subvector constructed by the \(i\)-th to \(j\)-th elements of \(\mathbf{x}\) \\ \(x(i)\) & the \(i\)-th element of \(\mathbf{x}\) \\ \(\mathbf{X}(:,i)\) & the \(i\)-th column of the matrix \(\mathbf{X}\) \\ \(\delta(\cdot)\) & Dirac delta function \\ \(M\) & number of ANs \\ \(N\) & dimension of the ANs and UNs \\ \(\mathbf{s}_{i},\gamma_{i},\iota_{i}\) & position, clock offset, and clock drift of AN \#i \\ \(\mathbf{p},\mathbf{v}\) & UN's position and velocity vector \\ \(\gamma_{i},\iota\) & UN's clock offset and skew \\ \(r_{i}\) & measured TOA from AN \#i \\ \(\mathbf{\tau}\) & collective form of \(r_{i}\) \\ \(\Delta\mathbf{a}\) & the error of vector \(\mathbf{a}\) \\ \(t_{i}\) & time interval from the beginning of the TD broadcast round to \\  & AN \#i's transmission time \\ \(\varepsilon_{i},\Delta\mathbf{s}_{i}\) & measurement noise and position error from AN \#i \\ \(\sigma_{1}^{2},\sigma_{\mathbf{s}_{i}}^{2}\) & variance of measurement noise and position error for AN \#i \\ \(\varepsilon,\Delta\mathbf{s}\) & collective of noise \(\varepsilon_{i}\) and error \(\Delta\mathbf{s}_{i}\) \\ \(\boldsymbol{\phi}\) & parameter vector, \(\boldsymbol{\phi}=\left[\mathbf{p}^{T},\mathbf{v}^{T},\gamma_{i},\iota^{2}- \|\mathbf{v}\|^{2},\gamma_{i}-\mathbf{p}^{T}\mathbf{v}\right]^{T}\) \\ \(\mathbf{Q}_{\varepsilon},\mathbf{Q}_{\varepsilon}\) & covariance matrices of \(\varepsilon\) and \(\Delta\mathbf{s}\), respectively \\ \(\mathbf{I}_{i}\) & unit vector from the UN to AN \#i \\ \(\mathbf{O}_{M\times N}\) & the \(M\times N\) zero matrix \\ \(\mathbf{I}_{M}\) & the \(M\times M\) identity matrix \\ \end{tabular}
## 2 Categorized Overview of Papers (Based on the Areas of Focus)
### Category 3: Business Process Optimization and Automation  
In addition to these papers collectively contributing to the field of data-driven applications and predictive modelling across a variety of domains, they offer specific approaches, such as innovative approaches to optimizing product layouts in supermarkets using sequential pattern mining and optimization algorithms [22]. Furthermore, they introduce a novel supervised learning algorithm for financial risk assessment [23] and explore the development trajectory of radio frequency identification (RFID) applications through academic citation and text mining analysis [24]. Another paper presents a deep learning framework for predicting important trading points in the stock market [25], focusing on high-margin opportunities. Additionally, they provide insights into factors influencing the adoption of business intelligence systems (BISs) in small and medium-sized enterprises (SMEs) [26], and conduct a systematic literature review on AI-based methods for automating business processes and decision support [27]. These studies collectively demonstrate the versatility and practical applications of data-driven techniques across diverse fields, highlighting their significance for informed decision making and optimization.
## 1 Introduction  
For decades, strain-gauge sensors have been employed to measure forces applied to target objects. A strain gauge is constructed from a conductive substance with a wavy pattern, which is attached to the object under investigation. Deformations induced by applied forces result in variations in the resistance [1, 2].  
Additive manufacturing using techniques such as inkjet [3], screen [4], aerosol [5], gravure [6, 7] and shadow-mask [8] printing have been attracting increasing interest because it is environmentally friendly and material-saving compared with conventional photolithography or chemical vapour deposition. This approach also allows direct printing of the strain gauge onto the surface of the target object without a gluing process, thereby avoiding force decoupling between the substrate and glued strain-gauge material [9].  
Highly stretchable as well as highly sensitive strain gauges have recently been used to measure on human motion in biomedical applications [10]. Carbon [11], silver (Ag) nanowires [12], Ag flakes [13], PeDOT:PSS [poly (3_A_-ethylenedioxythiophene): poly (styrene-nsulfonate)] [14] and graphite [15] have been tested as base materials for the conductive strain-gauge pattern.  
A full Wheatstone bridge with a single-symmetry structure was suggested for compensating for surrounding temperature variations when performing structural health monitoring outdoors [15]. A parametric design and fabrication process were investigated for developing analytical models and optimized process parameters for a sandwich-layer-structure strain gauge with an insulation layer [16].  
How the set-in effect, nonlinear responses and hysteresis influence the signals produced by printed strain gauges has also been investigated [17]. Moreover, a comparison study was carried out between screen-printed strain-gauge sensor with carbon and inkjet-printed sensors with Ag nanoparticles (AgNPs) [18]. The microstructure, gauge factor and long-term repeatability were analyzed. However, none of these studies performed a topographical analysis of inkjet-printed strain gauges constructed using Ag on a polyethylene terephthalate (PET) substrate.  
This study used inkjet printing to fabricate serpentine strain-gauge patterns with various thicknesses and widths of the conductive material. The initial resistances, thickness profiles and cross-sectional areas were identified after sintering the printed patterns, and strains were calculated under bending. The correlation between the gauge factor and topography was investigated using both theoretical and experimental approaches.