esa-sceva/satcom-chunk-collection huggingface dataset:
# Entropy-Regularized Federated Optimization for Non-IID Data
## Appendix A Proof of Theorem 1 (Linear Convergence of ERFO)  
### Explicit Lipschitz Constant for the Entropy Gradient  
**Lemma A1** (Explicit Lipschitz bound).: _Let \(w,w^{\prime}\in\mathbb{R}^{d}\) and let \(w^{(t)}\) be the fixed reference model at the start of a round. Define_  
\[\delta_{j}=|w_{j}-w^{(t)}_{j}|,\quad\delta^{\prime}_{j}=|w^{\prime}_{j}-w^{(t) }_{j}|,\quad Z=\sum_{j=1}^{d}\delta_{j},\quad Z^{\prime}=\sum_{j=1}^{d}\delta^ {\prime}_{j}.\]  
_Then the entropy gradient_  
\[g_{H}(w)\ =\ \
abla_{w}\Bigl{[}-\sum_{j=1}^{d}\frac{\delta_{j}}{Z}\ln\frac{ \delta_{j}}{Z}\Bigr{]}\]  
_satisfies_  
\[\bigl{\|}g_{H}(w)-g_{H}(w^{\prime})\bigr{\|}_{2}\ \leq\ \frac{2}{Z_{\min}}\ \bigl{\|}w-w^{\prime}\bigr{\|}_{2},\quad Z_{\min}=\min\{Z,Z^{\prime}\}. \tag{1}\]  
Boundedness During Local Epochs  
After the first mini-batch update of size \(\eta\), each coordinate satisfies \(\delta_{j}\geq\eta|g_{j}|\), so  
\[Z\ =\ \sum_{j}\delta_{j}\ \geq\ \eta\|g(w^{(t)})\|_{1}\ \geq\ \eta\|g(w^{(t)})\|_{2}.\]  
With a bounded-gradient assumption \(\|g(w^{(t)})\|_{2}\geq G_{\min}>0\), we obtain \(Z_{\min}\geq\eta G_{\min}\). Hence the Lipschitz constant in (1) is bounded above by \(2/(\eta G_{\min})=\mathcal{O}(1/\eta)\) for the entire first local epoch.
## 4 Results and Discussion
### Meso-Analysis of Blocking Behavior
#### 4.1.3 The Role of Water Flow  
Dry blockages, especially those with small particle size and small mass, will only move downwards under the action of gravity when they enter the template. It will move into the branch gap only after being impacted by water. The specific situation is shown in Figure 9.  
As you can see from the picture above, water flow plays an important role in the blockage process. Sometimes it will wash away the blockage and reduce the blockage, sometimes it will aggravate the blockage.
###### Abstract  
The Internet of Things (IoT) device scenario has several emerging technologies. Among them, Low-Power Wide-Area Networks (LPWANs) have proven to be efficient connections for smart devices. These devices communicate through gateways that exchange points with the central server. This study proposes an empirical and statistical methodology based on measurements carried out in a typical scenario of Amazonian cities composed of forests and buildings on the Campus of the Federal University of Para (UFPA) to apply an adjustment to the coefficients in the UFPA propagation model. Furthermore, an Evolutionary Particle Swarm Optimization (EPSO) metaheuristic with multi-objective optimization was applied to maximize the coverage area and minimize the number of gateways to assist in the planning of a LoRa network. The results of simulations using the Monte Carlo method show that the EPSO-based gateway placement optimization methodology can be used to plan future LPWAN networks. As reception sensitivity is a decisive factor in the coverage area, with \(-108\) dBm, the optimal solution determined the use of three gateways to cover the smart campus area.  
EPSO; IoT; LoRa; LPWANs; smart campus; gateway placement; propagation model Article
# Remote Sensing of the Water Quality Parameters for a Shallow Dam Reservoir
## 2 Data, Methods, and Techniques
### Area of Study  
The research was carried out in the area of Lake Dobczyce, located in the Myslenice poviat, in the Lesser Poland voisvodeship of Poland. It is a dam reservoir, constructed in 1986 by damming the Raba river with a 30 m high and 617 m long dam. The reservoir area is approximately 10.7 km\({}^{2}\), and its total capacity is 127 million m\({}^{3}\). The reservoir serves as a source of drinking water. The lake water quality is determined by the quality of the Raba river, which varies due to anthropogenic activities and precipitation. These factors contribute to significant fluctuations in the concentrations of nutrients, mineral and organic suspensions, and other substances. The depth of the lake is just over twenty-six meters (Figure 5).  
In the case of Dobczyce Lake, it is difficult to determine its range, especially in the western shallow region. This problem was solved by using information on the area elevation of the water surface and lidar altitude data (ALS), which, due to its high density, was subjected to resampling from the 5 \(\times\) 5 m network using the MIN method, suggested by [PERSON] et al., 2022 [36], as the most suitable method for this type of task.
# Analysis of Maritime Wireless Communication Connectivity Based on CNN-BiLSTM-AM
## 2 The System Model
### Wireless Channels  
Wireless channels between any two nodes experience both large-scale path loss and small-scale fading. It is assumed that small-scale fading follows either a Rayleigh distribution or a Rician distribution, resulting in the received signal-to-noise ratio (SNR) following an exponential distribution. However, due to the ocean's movement and weather variations, the channel model and its parameters are not stable, making the accurate estimation and collection of channel state information difficult.  
The channel parameter \(\lambda\) is used to characterize specific properties of the channel, such as fading and noise, and its value undergoes dynamic variations. To approximate the maritime wireless channel, it is assumed that the parameter of the exponential distribution \(\lambda\) follows a certain random distribution, a uniform distribution, for instance, i.e., \(\lambda\sim U(\lambda_{1},\lambda_{u})\). This assumption effectively simulates the stochastic and bounded variability in maritime channels. The formulation of the channel model is presented as follows:  
\[Y(t)=h(t)\cdot\beta(t), \tag{1}\]  
where \(h(t)=\frac{1}{D^{n}}\) signifies large-scale fading, \(m\) refers to the path loss exponent, \(D\) indicates the distance, and \(\beta(t)\) signifies small-scale fading.
## 4 Discussion  
In the region of a weak external electric field \(E_{0}\), the specific conductivity of the semiconductor does not depend on \(E_{0}\), while at higher values of \(E_{0}\), an exponential increase in the specific conductivity begins. The boundary value of the electric field strength between the region of weak and strong fields is called critical, the estimate of which is given by the expression \(E_{cr}\approx 3k_{B}T/2e\ell\), where \(\ell\) is the mean free path of an electron and \(k_{B}\) is the Boltzmann constant. If we take \(T=300\) K, \(\ell\approx 10^{-8}\) m, the value of the critical field \(E_{cr}=10^{6}\) V/m. Thus, at \(E_{0}<<E_{cr}\), we can consider the specific conductivity and consequently, the dielectric constant of the semiconductor to be independent of the strength of the external electric field. The values \(E_{0}\) used in the work are determined by the chemical potential \(\mu\) (Fermi energy \(E_{F}\)) of the graphene monolayer. The dependence of the chemical potential of graphene on the external field \(E_{0}\) is determined by the expression [19; 20]:  
\[E_{0}=\frac{e}{\tau\hbar^{2}V_{F}^{2}\varepsilon_{b}}\int_{0}^{\infty}\varepsilon [f(\varepsilon)-f(\varepsilon+2E_{F})]d\varepsilon, \tag{6}\]  
where \(f(\varepsilon)=[\exp[(\varepsilon-E_{F})/k_{B}T]+1]^{-1}\) is the Fermi-Dirac distribution, \(\varepsilon\) is the energy of the electron, the Fermi speed is \(V_{F}=10^{6}\) m/s and \(\varepsilon_{b}\) is the permittivity of the substrate.  
Figure 7 shows the dependence of the chemical potential of graphene on the electric field strength for the permittivity of the substrate \(\varepsilon_{b}=17.8\), obtained on the basis of (6). It follows from the above dependence that, for the used values of the chemical potential, the strength values \(E_{0}\) vary within (\(10^{4}\)-\(10^{5}\)) V/m, which can be attributed to the region of weak fields. The influence of a magnetic field on the properties of a semiconductor is taken into account by the resonant dependence (4) of the components of its permittivity on the magnetic field strength, which enters into the expression for the cyclotron frequency. The magnetic field applied in the plane of the graphene layers (namely that such a configuration is considered in the work) has no effect due to their transverse monoatomicity.  
Figure 7: Dependence of the chemical potential of graphene (Fermi energy) \(E_{F}\) on the external electric field strength \(E_{0}\) at the permittivity of the barrier layer \(\varepsilon_{b}=17.8\).
## 2 Modeling and Analysis of Carrier-Phase Differential Positioning
### Testing and Correction Strategies  
To solve the problem of low fault tolerance and no feedback of the commonly used LAMBDA, we propose to use the TEC test and station prior coordinate (position) accuracy to test the identified integer ambiguity. After the integer ambiguity is fixed, the difference between the calculated coordinate value and the prior coordinate will be calculated. If the difference exceeds the prior threshold value, the identified integer ambiguity is judged to be an invalid solution. By combining the dual-frequency observation data, a wavelength of 86 cm can be obtained, which can lead to much more deviation than the standard 19 cm and 24 cm wavelength. Therefore, the coordinate deviation calculated by the incorrect integer ambiguity is also enlarged. Generally, the coordinate deviation of the correct integer ambiguity solution is 1 cm grade, while the coordinate deviation of the incorrect integer ambiguity solution can reach more than 10 cm grade. Therefore, if the deviation exceeds the median value of more than 10 cm, the result is considered incorrect.Because the carrier signal propagation path has a strong correlation in time and space, the TEC values of adjacent epochs are very similar. Under the condition of fixed integer ambiguity, the double-difference ionospheric delay can be written as [22]:  
\[\Delta_{ion}=\lambda_{1}\
abla\Delta\phi_{L1}-\lambda_{2}\
abla\Delta\phi_{L2 }-\lambda_{1}\
abla\Delta N_{L1}-\lambda_{2}\
abla\Delta N_{L2} \tag{27}\]  
where \(\lambda_{1}\), \(\Delta\phi_{L1}\) and \(\Delta N_{L1}\) are the wavelength, phase and integer ambiguity of \(L1\) carrier; \(\lambda_{2}\), \(\Delta\phi_{L2}\) and \(\Delta N_{L2}\) are the wavelength, phase and integer ambiguity of \(L2\) carrier respectively.  
If the integer ambiguity is fixed correctly, the value of the double-differential ionospheric delay changes little and steadily. Otherwise, there will be a big jump. By these two steps, most of the fixed ambiguity errors can be eliminated, and then appropriate corrective measures can be selected to correct the error solution. In this paper, Tikhonov regularization and LAMBDA are first used to identify the parameters of the integer ambiguity. Then, the TEC test and the prior coordinate accuracy test are used to analyze the validity of the integer ambiguity identification results. Finally, the GWO algorithm is used to correct the solution units that fail the test. The flowchart is shown in Figure 1.  
Figure 1: Parameter identification scheme of integer ambiguity.
# Modeling of Cotton Yield Estimation Based on Canopy Sun-Induced Chlorophyll Fluorescence
## 3 Results
### Correlation Analysis between Canopy SIF and Cotton Yield at Different Growth Periods  
#### 3.2.1 Correlation Analysis of Cotton Yield with AGB and LAI at Different Growth Periods  
The correlation between cotton yield and biometric parameters was analyzed, and the results are shown in Table 3. In the four key growth periods, cotton yield was significantly positively correlated with AGB and LAI. The correlation coefficient increased first and then decreased with the progress of growth periods and reached the maximum value during the flowering and boll period. The correlation coefficient between yield and AGB reached the maximum value of 0.868 during the flowering and boll period, followed by 0.858 in the flowering period and 0.556 in the boll period. In these three periods, there were significant correlations at the \(p<0.05\) level. The correlation coefficient between yield and LAI reached the maximum value of 0.819 in the flowering and boll period, followed by 0.691 in the flowering period, while the lowest value was observed during the budding period. The results showed that cotton yield was significantly correlated with both AGB and LAI from the flowering period to the boll period.  
Figure 3: Significance analysis of AGB, LAI, and SIF of cotton at different growth periods and under different treatments. (a–e) indicates that at the significant level of 0.05, different letters represent significant differences, and the same letters represent significant differences.
# Reinforcement Learning and Physics
## 2 Reinforcement Learning and Physics
### Standard Reinforcement Learning for Physics Research  
The use of RL has spread across different physics applications in recent years, particularly in quantum physics. One of the first remarkable approaches proposed the application of RL to adaptive quantum metrology [15], in which RL-based control achieved a better control of of quantum processes than standard greedy approaches. In [24], RL demonstrates to be able to find the ground state and describe the unitary time evolution of complex interacting quantum systems. The ability of RL to optimize quantum-error-correction strategies, thus protecting qubits against noise is shown in [25]. In another original work [26], control based on RL shows a similar performance to optimal control methods in many-body quantum systems of interacting qubits.  
RL has also been applied in the field of quantum computing for dynamic non-convex optimization in ultra-cold-atom experimentation [14] and measure control in order to facilitate the access to quantum states [13]. Other relevant works to deal with the issue of smart and efficient quantum measures make use of active learning [27; 28].  
The advent of Deep Learning (DL), that has allowed the resolution of data-driven problems that were unapproachable just a few years ago, has also produced an impact on RL by means of deep policies and DL-based function approximations, leading to the so-called Deep Reinforcement Learning (DRL) [29]. DRL has already been used for efficient measuring of quantum devices [30], for control optimization in quantum state preparation [31], for gate control [32] or for robust digital quantum control to break adiabatic quantum control [16].  
Although this subsection has focused on RL to different quantum problems, its application to classical physics is also common. In [33], evolutionary RL was applied to estimate the likelihood of dynamical large deviations, thus showing the suitability of ML in path-extensive physics problems. An interesting review of RL approaches to solve fluid mechanics problems is provided in [34]. RL has also found its application in other fields of physics like optics, e.g., for an adaptive control of astronomy systems [35], or in thermodynamics, to optimize thermodynamic trajectories [36], thus learning previously unknown thermodynamic cycles. An interesting RL-based solution in dynamics is presented in [37], where an efficient sampling of rare trajectories is achieved; in particular, the idea is to make rare events typical so that dynamical behaviors that appear with very low probability in non-equilibrium systems can be accessed in a statistically significant way.  
Therefore, we can conclude that standard RL is a common choice for optimization and control in different physics problems, particularly within the quantum realm. Recent RL approaches, like DRL, that enhances RL with DL have rapidly been applied to a number of physics control problems, with some relevant results, as shown in this subsection. Next, the quantum version of RL will be presented in Section 2.2.
## 1 Introduction
Because of free and random requests of accessing resources and fuzzy system configurations, it is impossible to find an optimal set of roles that cover all permissions for a given access request. The authorization-query problem [27] proved to be satisfiable, strictly complies with the separation-of-duty constraint, and attempts to derive a role set that can cover as many permissions as possible. Maximal satisfiability (MAX-SAT) is a core research problem of theoretical computer science that has had successful applications in various fields, such as artificial intelligence and model detection [28]. Nevertheless, conventional role-mining methods lack security detections for authorizations in the constructed RBAC system. The following problems exist: on the one hand, different results may conflict with each other and violate the mutually exclusive constraint; on the other hand, different results may include permissions outside the permission request and violate the least-privileges principle. To address the above problems, this paper proposes a novel method called role-mining optimization with separation-of-duty constraints and security detections for authorizations (RMO_SODSDA). The main contributions of this paper are as follows:  
1. The separation-of-duty constraint was proved to be effectively enforced by mutually exclusive constraints [24]. In order to satisfy these constraints, we presented a constrained-role optimization approach, we constructed several variants of mutually exclusive constraints to correctly implement the given separation of duty, and we evaluated the effectiveness of RMO_SODSDA. 2. As a branch of MAX-SAT, the partial MAX-SAT problem [29] aims to satisfy all hard clauses and as many soft clauses as possible. It is clearly more versatile. In order to ensure system security, based on the previously constructed RBAC system, we converted the authorization-query problem into the partial MAX-SAT and evaluated RMO_SODSDA efficiency. The rest of the paper is organized as follows. In Section 2, we discuss related work and present preliminaries and basic problem descriptions to understand the rest of the paper. Section 3 proposes a novel method (RMO_SODSDA), including two aspects: (1) presenting role optimization with separation-of-duty constraints and demonstrating how to construct different kinds of mutually-exclusive-role constraints, and (2) reducing the authorization-query problem to partial MAX-SAT and demonstrating how to ensure system security. We show the results of the experimental evaluations in Section 4 and present discussions in Section 5. Section 6 concludes the paper and discusses future work.
# Application of Machine Learning Ensemble Methods to ASTRI Mini-Array Cherenkov Event Reconstruction
## 3 Machine Learning Techniques
### Random Forest  
Random Forest is a Machine Learning algorithm that is used for classification and regression tasks [18]. It uses multiple decision trees to make predictions.  
A decision tree is a tree-like model in which an internal node represents a feature (or attribute) and the branches represent the decisions based on that feature. Each leaf node represents a class label. The decision tree algorithm works by recursively partitioning the data into smaller and smaller subsets, based on the value of the features.  
In a Random Forest, multiple decision trees are trained on different subsets of the data and with different subsets of the features and the predictions made by each tree are then combined to make a final prediction. This is one of the main advantages of this method, since it reduces the chance of overfitting to any particular pattern in the data. Additionally, Random Forest can handle missing values and outliers in the data more effectively than a single decision tree.
## 2 Materials and Methods
### Datasets Used in the Study
#### 2.1.3 TCCON  
TCCON, a global network of ground-based Fourier transform spectrometers established for measuring atmospheric XCO\({}_{2}\) and other trace gases [35]. It has a high accuracy with approximately 0.25% error in the retrieved XCO\({}_{2}\), which has been extensively used for validation and calibration of satellite observations, including ACOS-GOSAT retrievals [36]. In this study, the 2014 release version of the TCCON data (GGG2014) for 12 sites (with at least 2 years of coincidental measurements and within the mapping area; as shown in Figure 1) is used. Measurements within the period from June 2009 to May 2014 were selected for comparison. The 12 selected sites include Park Falls [37; 38], Orleans [39], Garmisch [40], Karlsruhe [41], Bremen [42], Bialystok [43; 44], Tsukuba [45; 46], Lamont [47], JPL [48], Saga [49], Darwin [50], and Wollongong [51].
## 3 Methodology
### Calibration Methods
#### 3.2.3 Beta Calibration  
Beta Calibration generalizes logistic calibration by modeling miscalibration through a parametric family of Beta distributions. This method extends Platt Scaling's two-parameter sigmoid to a three-parameter function, enabling correction of asymmetric miscalibration patterns. The calibration function is defined as:  
\[\phi_{\text{Beta}}(\hat{p}_{i})=F(a\cdot\text{logit}(\hat{p}_{i})+b;\,\alpha, \beta), \tag{8}\]  
where \(F(\cdot;\alpha,\beta)\) is the cumulative distribution function (CDF) of a Beta distribution with shape parameters \(\alpha,\beta>0\), and \(a,b\in\mathbb{R}\) scale and shift the log-odds scores. The additional parameters \(\alpha\) and \(\beta\) provide flexibility to model skewed or heavy-tailed deviations from calibration.  
Parameters \(\{a,b,\alpha,\beta\}\) are jointly optimized on \(\mathcal{D}_{\text{cal}}\) via maximum likelihood estimation:  
\[\{a^{*},b^{*},a^{*},\beta^{*}\}=\underset{a,b,\alpha,\beta}{\arg\min}\sum_{( \mathbf{x}_{i},y_{j})\in\mathcal{D}_{\text{cal}}}\bigl{[}y_{j}\ln\phi_{\text {Beta}}(\hat{p}_{j})+(1-y_{j})\ln\bigl{(}1-\phi_{\text{Beta}}(\hat{p}_{j}) \bigr{)}\bigr{]}. \tag{9}\]Beta Calibration addresses limitations of Platt Scaling when miscalibration is non-sigmoidal. While more flexible than Platt Scaling, Beta Calibration requires larger \(\mathcal{D}_{\text{cal}}\) sizes to robustly estimate four parameters. Overfitting risks emerge when calibration data is sparse, often mitigated via Bayesian priors on \(\alpha\) and \(\beta\).
# An Overview on Deep Learning Techniques for Video Compressive Sensing
## 7 Compressive Sensing: Research Challenges and Opportunities  
Data today is generated at exponentially growing rates which creates unbearable demands on the sensing, storage and processing devices. Indeed, thousands of data centers are built worldwide to store this huge amount of data which leads to extremely high power that is consumed on acquiring and processing. As long as we generate more data there is an urgent need for novel data acquisition and processing concepts such as compressive sensing.  
Obviously, there is a tremendous intellectual progress in compressive sensing and sparse representation systems. Therefore, many mathematical concepts such as probability theory, convex optimization and reconstruction algorithms become an essential toolbox for many researchers and engineers to design and develop real-world applications.  
Hence, in the future, we are going to talk about designing hybrid systems that integrate hardware and software, where these two systems are implemented simultaneously from the beginning using the mathematical concepts described above.  
Also, a new research direction has appeared with deploying a video compressive sensing system with edge computing to optimize the memory storage and bandwidth [89]. In addition, theoretical studies on detection algorithms directly from the snapshot compressed measurement have already started [90]. Finally, we can say that compressive sensing allows us to think about data, complexity, algorithms and hardware at the same time. In a nutshell, the answer will be an algorithm with better flexibility, accuracy and speed.