esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Constructions of Low-Hit Zone Frequency-Hopping Sequence Set Based on _m_-Sequence
## 2 Preliminaries  
For simplicity, in the remainder of this paper, we use \((N,M,q,L_{z},h)\)-LHZ FHS set to denote an LHZ FHS set of size \(M\) and length \(N\) over a frequency slot set of size \(q\) whose LHZ is \(L_{z}\), and the MPHC is \(h\) within the LHZ. Additionally, the notations and definitions presented in Table 1 will also be utilized.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notations \& Definitions** & **Description** \\ \hline \([x]_{y}\) & the maximum quotient when \(x\) is divided by \(y\). \\ \hline \(\langle x\rangle_{y}\) & the least positive integer of \(x\) modulo \(y\). \\ \hline \(L^{(\xi)}\) & the operator for cyclic left shift by \(\xi\) positions. \\ \hline \(N(\mathbb{S},g)\) & the number of \(g\) that occurs in any sequence \(\mathbb{S}\). \\ \hline \(\mathbb{F}_{q^{n}}\mathbb{F}_{q^{n}}\) & “the finite field” and “the extension field of a finite field”. \\ \hline \(\mathcal{T}_{q}^{q^{n}}(x)\) & the trace function from \(\mathbb{F}_{q^{n}}\) to \(\mathbb{F}_{q}\). \\ \hline \(\mathbb{F}_{q^{n}}/\{0\}\) & the set obtained by removing the element \(0\) from \(\mathbb{F}_{q^{n}}\). \\ \hline \(\lceil x\rceil\) & the greatest positive integer not exceeding \(x\). \\ \hline \(\lfloor x\rfloor\) & the smallest positive integer that is not less than \(x\). \\ \hline \(\oplus\) & the addition modulo \(q\) over \(\mathbb{F}_{q}\). \\ \hline \(x\
mid y\) & \(x\) does not divide \(y\). \\ \hline \(x\mid y\) & \(x\) divides \(y\). \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notations and definitions employed in the remainder of this paper.
# Dynamic Workload Management System in the Public Sector: A Comparative Analysis
## 3 Methodology
### Machine Learning Algorithms
Furthermore, Spark's real-time stream processing, fault tolerance, and scalability ensured the robust performance of the algorithms in processing large, dynamic datasets. Spark MLlib is a library that enables Apache Spark to perform machine learning algorithms with exceptional speed and accuracy. Built on the RDD API, MLlib leverages multiple cluster nodes to alleviate memory bottlenecks. Complementing MLlib, Apache Spark also includes SparkML, a DataFrame-based machine learning API. This dual-library approach allows developers to choose the most suitable option based on the dataset'scharacteristics and size, ensuring optimal performance. These libraries support various algorithms, including classification, regression, recommendation, clustering, and topic modeling. MLlib offers core machine learning features such as Featurization, Pipelines, Model Tuning, and Persistence. It supports essential tasks, including data preprocessing, model training, and prediction. With a design focused on simplicity and scalability, MLlib is highly effective for various machine learning tasks, including deep learning [41]. Apache Spark, developed in Scala, integrates effortlessly with APIs such as Java and Python and operates efficiently in both Hadoop and standalone environments [42]. A notable algorithm in MLlib is the Multilayer Perceptron (MLP) classifier, a feedforward Artificial Neural Network used for classification [43]. This research utilizes the MLP classifier, configured with a single hidden layer containing ten neurons. This configuration provides a robust framework for analyzing and modeling complex datasets. This layered analytical approach, executed within Apache Spark, allowed robust comparative analysis across models. By progressively introducing greater complexity and capability, the study identified the Adaptive Neuro-Fuzzy Inference System as the most effective algorithm. ANFIS outperformed others in accuracy, robustness, and interpretability, demonstrating its ability to model complex relationships within the dataset. This iterative and comparative methodology underscores the importance of tailoring algorithm selection to the specific challenges of the domain and leveraging powerful platforms like Apache Spark for efficient large-scale computation in dynamic workload management systems.
# Behaviour Investigation of SMA-Equipped Bar Hysteretic Dampers Using Machine Learning Techniques
## 5 Machine Learning Approaches
### Computational Costs  
Various machine learning (ML) techniques could be utilised to estimate the first yield point displacement and post-yield stiffness ratio in SMA-BHDs. However, in this paper, new models were developed by the artificial neural network (ANN) and group method of data handling (GMDH) techniques. The previous sections showed that proposed ANN models outperform GMDH models with lower errors and higher with low errors and high accuracy. On the other hand, considering the computational costs related to ML models' complexity, it can be mentioned that the proposed GMDH models, with less complex closed-form equations, perform better in comparison with ANN models.
# A Reconfigurable Framework for Hybrid Quantum-Classical Computing
## 4 Experimental Results
### Dataset Generation and Pre-Processing
#### 4.2.2 Dimensionality Reduction via Principal Component Analysis (PCA)  
Given the computational limitations and architectural requirements of quantum circuits, it is critical to map classical data efficiently into quantum states. To achieve this, we implement principal component analysis (PCA) [38], a linear dimensionality reduction technique designed to preserve maximal variance in the data while reducing feature space dimensionality. PCA transforms original features into a set of linearly uncorrelated principal components (PCs), capturing the most significant data variance.  
PCA is performed using the covariance matrix decomposition  
\[C=\frac{1}{n-1}(X-\mu)^{T}(X-\mu),\quad\text{where }\mu=\mathbb{E}[X] \tag{8}\]  
Eigen decomposition of the covariance matrix yields principal components:  
\[Cv_{i}=\lambda_{i}v_{i},\quad i=1,2,\ldots,d \tag{9}\]  
where \(\lambda_{i}\) are eigenvalues sorted in descending order and \(\mathbf{v}_{i}\) are corresponding eigenvectors. For our experiments, PCA reduces the data dimension from 784 to \(q\), where \(q\) directly corresponds to the number of qubits used (i.e., \(q\in\{8,9,10,11,12,13,14,15\}\)).
# Climate, CO2, and Anthropogenic Drivers of Accelerated Vegetation Greening in the Haihe River Basin
## 4 Discussion
### Impact of NDVI Change  
Vegetation projects have been proven to ameliorate land degradation [65; 66], and lower surface temperatures [67], but they also induce a strain on hydrology [68; 69; 32] and water resources [5]. Vegetation changes alter the surface thermal properties, namely surface albedo and emissivity, and the subsequent radiation budget [70]. [PERSON] et al. [71] identified that a 1% increase in NDVI decreased the albedo by \(-\)0.003 \(\pm\) 0.001%, and NDVI change contributed to 53.36% additional increase in net radiation globally. Vegetationgreening inevitably modulates the water cycle [32]. Water losses from the land surface to the atmosphere through evapotranspiration, including transpiration from leaves and evaporation from bare ground. Greening increases vegetation transpiration and reduces soil evaporation [72]. To a certain extent, planting trees will enhance the water retention capacity of the surface, but greening-induced ecological water consumption has been found to be the primary cause of streamflow reduction and soil drying [66; 73]. HRB, as an economic center, accounts for nearly 10% of the total population in China. Generally, human withdrawals are subtracted from the surface and groundwater. Water scarcity as a consequence of runoff reduction has emerged as an urgent and critical challenge for social and economic development.  
In this study, we simulated that if vegetation restoration is not continued in the future (2020-2100), NDVI will continue to rise under SSP245 and SSP585 scenarios. It indicated that if we have no afforestation activities in the future, the vegetation will be greener than the current level. Although afforestation can help slow climate warming by sequestrating carbon, the program and management of ecological conservation and restoration require more thought and trade-offs in terms of water resources and hydrology.
## 1 Introduction  
### Photovoltaic Trends in the World and in the European Union  
Due to today's energy trends, it is necessary to reorganize the existing energy systems, mainly due to the increasing greenhouse gas emissions of the world, the scarcity of fossil fuels, and the increasing demand for electricity. The electricity sector plays an important role in the reduction of anthropogenic greenhouse gas emissions. Therefore, the use of less carbon-intensive technologies is necessary to meet challenging emission reduction targets [1; 2; 3]. The renewable energies have an increasing role in the process of energy production in the 21 st century [4]. Besides numerous other benefits, energy production based on photovoltaic technologies can significantly contribute to sustainable energy management. By a one-time investment in photovoltaic technology, it is possible to produce CO\({}_{2}\)-free green energy without producing any waste for several decades ([PERSON] et al., 2018; [PERSON] et al., 2018).  
Worldwide, the development of variable renewable energy (VRE) sources (wind, solar) is expanding rapidly because they are clean, safe, widely available, sustainable and economical ([PERSON] et al., 2018). Photovoltaic (PV) technologies use photovoltaic modules that transform the electromagnetic solar radiation from the Sun into electricity. A large-scale growth in photovoltaic energy production has been observed in recent years and in most countries the reasons are ([PERSON] et al., 2018):  
* Rapid technological development,
* Falling investment costs, and
* The introduction of state subsidies ([PERSON] et al., 2018).  
The share of renewable electricity production represented 26.5% of all electricity produced globally in 2017, of which PV systems accounted for 1.9% ([PERSON] et al., 2018). PV technologies have largely penetrated the global energy market. In 2018, the global built-in PV capacity amounted to 509.3 GW, with the largest proportions being China's 175.1 GW, Europe's 125.8 GW, the USA's 62.1 GW, Japan's 55.8 GW, and India's 27.3 GW ([PERSON] et al., 2018). Nowadays, in the energy sector, PV technology is considered to be a cost-competitive source for increasing electricity production and for providing energy access. Nonetheless, markets in most locations continue to be driven largely by regulations or government incentives ([PERSON] et al., 2018; [PERSON] et al., 2018; [PERSON] et al., 2018; [PERSON] et al., 2018). Germany (45.9 GW/2018), Italy (19.9 GW/2018), France (8.9 GW/2018), Spain (5.9 GW/2018), and the Netherlands (4.1 GW/2018) have a prominent role in the spread of photovoltaic technology in the European Union (EU). Based on the European Network of Transmission System Operators for Electricity (Entso-E) scenarios, by 2040, the net PV production capacity in the EU will be about 231-625 GW higher than in 2017 (108 GW) ([PERSON] et al., 2018; [PERSON] et al., 2018) (Figure 1).  
Figure 1: Net photovoltaic (PV) production capacity forecast in the EU based on Entso-E scenarios in 2040 based on ([PERSON] et al., 2018).
# User Experience of 5G Video Services in Indonesia: Predictions Based on a Structural Equation Model
## 2 Underlying Studies and Theories
### User Experience and QoE on 5G Technology  
The perspective of QoE can be formulated by combining QoS and UX. Combining factors of UX as part of QoE together with QoS is further discussed in [11]. Although the QoS factor from the technical side is still considered vital, alone, it is not enough to show the satisfaction value of service users. QoS factors such as delay, jitter, loss, error, speed, and good bandwidth do not necessarily result in a high QoE value because there are additional satisfaction factors associated with users that are very subjective, diverse, and unpredictable [4; 21], and these also affect UX. Moreover, QoE is not limited to the technology itself but is embedded and becomes active during the interaction between the user and the technology [5].  
If we look at Figure 1, it can be seen that QoE is formed from two aspects: QoS and UX. The QoS factors are related to the technical factors of a technology, such as delay, jitter, loss, error, speed, bandwidth, etc. Meanwhile, the UX factors are related to users' perspectives, such as the basic factors mentioned in [22], which mainly include attractiveness (overall impression: do users like or dislike the product?), perspicuity (is it easy to recognize and learn how to use the product?), efficiency (can users complete their tasks without extra effort?), dependency (does the user frequently use the product?), stimulation (is it exciting and motivating to use the product?), and novelty (is the product innovative and creative? Does the product interest users?).  
Figure 1: Elements of UX and QoE.  
These six basic UX factors can also be further developed by looking at UX as a \"human factor\"--that is, an element of subjectivity. Related to our case of video streaming for 5G, we further elaborate these six basic factors into 10 factors to be assessed in this research, as discussed in Section 3.
# Laser Technology in Photonic Applications for Space
## 1 Introduction  
The combination of coherent light, such as that of a laser, and the stringent requirements to implement new technologies in space allows for several applications in optical communication, illumination, target designation, and active remote sensing to demonstrate unprecedented results compared to previously used technologies. Moreover, the scientific advances of laser emitting technologies allow for the implementation of new devices and applications. In the past, only giants, such as the National Aeronautics and Space Administration (NASA), the Soviet Union (now Russia), the European Space Agency (ESA) and, more recently, the Japan Aerospace Exploration Agency (JAXA), China, and India, had the capacity to launch technology into space. Israel has now also entered the race, as have private companies, such as SpaceX, Virgin, Blue Origin, and SpaceIL. The recent boom of micro and nanosatellites, with volumes as small as one litre and weights below 1.5 kilograms [1], has enabled new actors to launch new devices into space [2] for Earth observation, communication, Internet of Things (IoT), and geolocation, among others [3]. Due to the market demands and new active players, the number of devices deployed in space is rising significantly. For example, the UK alone plans on launching 2000 small satellites by 2030 [4]. Thanks to the intrinsic advantages of photonic applications (bandwidth, mass, power consumption, and immunity to electromagnetic interference), many of the new deployments in space will include many new laser devices [5]. In this publication, we will review the principal laser-based photonic applications for space, focusing on their technological aspects.
# Assessment of Soft Computing Techniques for the Prediction of Suspended Sediment Loads in Rivers
## 2 Methodology  
### Sediment Rating Curve  
Suspended sediment load is considered the main part of the transported sediments in rivers. The measurement of the daily-suspended sediment concentration in rivers is a cumbersome and expensive process as compared to the daily river discharge. Hydrologists proposed different approaches for the estimation of missing SSL, with some associated uncertainty [50; 51]. The missing SSL estimation is based on the available measured river discharges and sediment concentrations and also on the development of the regression relationship of river discharge (Q) and SSL. Hydrologists often use an empirical relation such as sediment rating curves (SRCs), which is a reliable method for the estimation and prediction of SSL. The sediment rating curves normally describe a functional association of SSL and \(Q\), as shown in Equation (1)  
\[SSL=a\cdot Q^{b} \tag{1}\]  
where SSL is the suspended sediment load in metric tons per day (t/day), \(C\) is the suspended sediment concentration in parts per million (PPM) and \(Q\) is the flow discharge in cubic meters per second (m\({}^{3}\)/s). \(a\) and \(b\) are the constant.  
Further, the efficiency of the SRCs for the assessment of long-term catchment sediments can be improved by adding a correction coefficient (\(C_{f}\)) to the equation of SRC. Correction factors (CF) are applied in the SRC equation as follows:  
\[SSL=C_{f}a\cdot Q^{b} \tag{2}\]  
In the present study, the Ferguson correction factor and the Smearing correction factor were applied, among different correction coefficients suggested by different scholars.
## 4 Implementation
### Frequency Range Analysis and Feature Extraction  
In spectral analysis, distinct frequency ranges are meticulously examined to uncover fault-related signatures within the signal. This process involves dividing the frequency spectrum into specific bands, within which the amplitude and frequency of dominant peaks are meticulously analyzed. By focusing on these bands, fault-related frequencies can be isolated and their severity characterized. Critical spectral features, including peak amplitudes, peak frequencies, and spectral bandwidths, are then extracted from these frequency-domain representations. These features play a crucial role as discriminative parameters for machine learning algorithms, empowering them to achieve precise fault diagnosis and prognosis. Through this detailed spectral analysis, the methodology gains insights into the underlying fault mechanisms, enabling effective monitoring and maintenance strategies for the DC motor. Figure 4 illustrates the spectral features of a single sample within the frequency range of 0.1 to 0.2 Hz and 0.2 to 0.3 Hz, showcasing important fault-related parameters such as peak amplitudes and frequencies. In the frequency range of 0.1 to 0.2 Hz, an increase in the amplitude of the peaks correlates with a higher severity of faults. Conversely, in the frequency range of 0.2 to 0.3 Hz, a decrease in peak amplitude is associated with a more pronounced fault occurrence. These findings indicate a direct  
Figure 3: FFT analysis results with logarithmic scaling.  
relationship between fault severity and the strength of specific frequency components within each frequency range.
## 3 The PID-CTOPC Approach
### Controller Design
Then, according to the necessary condition \(\frac{\partial J_{M}}{\partial\overline{u}}=0\), the Formula (18) can be expanded as:  
\[\left(\frac{\partial H}{\partial\overline{u}}\right)^{T}\left(B_{1}H_{0}+B_{ \rho}H\right)=0 \tag{19}\]  
Recall that \(\partial H/\partial\overline{u}\) is full rank, then formulation of \(H\) can be given by:  
\[H=-B_{\rho}^{-1}B_{1}H_{0} \tag{20}\]Denoting \(K_{M}\in\mathbb{R}^{1\times(n-1)}\) as the first row of matrix \(B_{\rho}^{-1}B_{1}\) and taking the first row of \(H\) yields:  
\[\overline{f}(x)+\overline{g}(x)\cdot u=-K_{M}H_{0} \tag{21}\]  
Then, the PID-CTOPC law can be given by:  
\[u(t)=-[\overline{g}(x)]^{-1}\Big{[}\overline{f}(x)+K_{M}H_{0}\Big{]} \tag{22}\]  
Proof of the theorem is accomplished. \(\square\)  
**Remark 1**. : _Since the modified approach is an advanced version of the PCTOPC, hence, tuning principles of the parameter \(t_{M}\) and the control order \(\varrho\) also follow the ones of the PCTOPC, which can be referred to [8]. Meanwhile, tuning of \(K_{p}\), \(K_{i}\), and \(K_{d}\) also follow the principles of the conventional PID controller, namely, (1) large \(K_{p}\) would accelerate the output response speed, which is beneficial to reduce the steady-state tracking error while increases the overshoot; (2) small \(K_{i}\) would reduce the overshoot while slow down the speed of eliminating the steady-state error; and (3) large \(K_{d}\) would accelerate the system response speed and reduce the overshoot while weakening the system robustness against disturbances._
# Generalization of ALMM Based Learning Method for Planning and Scheduling
## 7 Reinforcement Learning vs. ALMM Based Learning
### Aim of Learning  
Let us now review certain similarities to the indirect \"aim\" of the learning processes in both methods. First of all, both methods share a common assumption: the agent selects an action/decision that will maximize the reward in long term, not only in the immediate future. Such algorithms are known to have an infinite horizon, though in practice the heuristic philosophy is applied to a finite time range or finite trajectories (so-called episodic problems). The objective of an RL based algorithm is to find a \(\prod\) policy with a maximum expected return. A policy refers to mapping that assigns some probability distribution over actions with the actions selected by the policy. The objective of an algorithm utilizing ALMM Based Learning is to find coefficients \(a_{2},a_{3},\dots,a_{n}\) for a local criterion function \(q(u,s)\) in a way that would optimize the value of a global criterion \(Q\). The selection of a decision in a given state is performed based on the local criterion \(q\). Roughly speaking, a policy, or more precisely an action value function under policy \(\prod\), corresponds to a local criterion \(q\) for ALMM based Learning, while the expected reward criterion corresponds to the global criterion \(Q\).
## 3 The Influence of Structure Parameters
### The Height of nanodisk  
To study the impact of the height of nanodisk on the structural color, we gradually changed the height of nanodisk \(h\) from 30 nm to 220 nm with all other parameters kept at fixed values, and simulated again with Parameter Sweep function in FDTD. Simulation results show that the variation of the height of nanodisk has a large impact on the structural colors. When \(h\) is small, the reflection spectra have many separated peaks, as shown in Figure 4a. Namely, the reflected light is a less saturated red. As \(h\) increases, some peaks gradually decrease and finally vanish, while others grow higher and wider, as shown in Figure 4b. When the height of nanodisk \(h\) increases to 130 nm, the bandwidth of the red peak reaches the maximum, and the saturation of the reflected light is closest to the standard red. Following this, as the value of \(h\) continues to increase, the color saturation of the reflected light begins to decrease. This can be concluded by the distances from every chromaticity coordinate of different spectra to that of standard red. The chromaticity coordinates corresponding to different \(h\) have been marked in the chromaticity diagram, as shown in Figure 4c.