esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrated Information in Relational Quantum Dynamics (RQD)
## 8 Limitations and Future Directions
### Key Limitations  
* **Exponential complexity.** By construction, \(\Phi(\rho)\) requires a minimization over all bipartitions of the \(n\) subsystems. In the worst case, this entails evaluating \[\binom{n}{\lfloor n/2\rfloor}\sim\frac{2^{n}}{\sqrt{\pi n/2}}\] candidate splits, which is exponential in \(n\). Although our hierarchical dendrogram reduces the search to \(n-1\) successive bipartitions, each step can still involve an exponentially large subset when the block size is \(\approx n/2\).
* **Partition-dependence.**\(\Phi(\rho)\) is defined relative to a fixed tensor-factorization of the global Hilbert space. Different choices of subsystem delineation (e.g., qubits vs. modes, or grouping qubits into registers) will in general yield different values of \(\Phi\). There is no intrinsic mechanism for identifying the \"correct\" granularity of the subsystems.
* **No directional or causal information.** By focusing on symmetrized QSD, \(\Phi\) quantifies the strength of _holism_ but not the _direction_ of information flow across the cut. Quantum causal structure requires conditional divergences or directed measures (e.g., Petz-based conditional JSD), which are not captured here.
* **Sensitivity to mixed-state noise.** Although \(\Phi\) is 1-Lipschitz in the metric \(\delta(\rho,\rho^{\prime})\), highly mixed states with low rank can drive \(\Phi\to 0\) even when significant classical correlations remain. Thus in noisy or thermally mixed regimes, \(\Phi\) may under-report residual structure.
# Design of Capacitive Paddy Moisture Sensor Based on Electrical Impedance Spectroscopy Analysis
## 4 The Paddy Moisture Sensor  
### Hardware and Software of a Paddy Moisture Sensor  
The hardware of the paddy moisture sensor mainly comprises a coaxial cylindrical capacitor, a capacitance detection integrated chip AD5933, digital temperature sensors DS18B20, a 4.3-inch LCD display, and a control chip STM32F407 ZGT6, as shown in Figure 7. AD5933 applies the excitation frequency to the coaxial cylindrical capacitor and then detects the capacitance of the coaxial cylindrical capacitor containing the paddy sample and transmits it to the control chip STM32F407 ZGT6 via IIC. The probe of DS18B20 is placed inside the capacitive sensor, monitoring the paddy temperature in real time during the detection process. DS18B20 converts the paddy temperature into a digital signal and transmits it to the control chip. The control chip STM32F407 ZGT6 substitutes the capacitance value obtained by AD5933 and the temperature value obtained by DS18B20 into Equation (1), and then the current paddy moisture can be calculated and shown on a 4.3-inch LCD display. The control chip STM32F407 ZGT6 was programmed by Keil uVision5 (MDK5) to communicate with AD5933 (initialize AD5933 and read capacitance), read temperature, and display the paddy moisture. The detection flowchart is shown in Figure 7. During the initialization of AD5933, the excitation frequency was set to 2 kHz based on the results of paddy electrical impedance spectroscopy analysis and the detection time was set to 50 for detection accuracy and speed.
## 1 Introduction
[16] combined feature selection and subspace learning simultaneously in a joint framework, which is based on using \(\ell_{2,1}\) norm on the projection matrix and achieves the goal of feature selection. The \(\ell_{2,1}\) norm penalty term encourages row sparsity as well as the correlations of all the features. Recently, some researchers proposed \(\ell_{1/2}\) norm [17] regularized NMF [18; 19], and low-rank regularized NMF [20; 21] with improved performance for special purposes. The \(\ell_{1/2}\) norm can usually induce sparser solutions than its \(\ell_{1}\) counterpart, but it is usually unstable. The limitation of the low rank constraint is that it is not suited to feature selection in general. What's more, discriminative information is very important for learning a better representation. For example, by exploiting the partial label information as hard constraints of NMF, [PERSON] [22] developed a semi-supervised Constrained NMF (CNMF), which obtained better discriminating power. [PERSON] et al. [23] proposed robust structured NMF a semi-supervised NMF learning algorithm, which learns a robust discriminative data representation by pursuing the block-diagonal structure and the \(\ell_{2,p}\) norm loss function. But under unsupervised scenario, we cannot have the label information. In fact, we could add approximate orthogonal constraints to obtain some discriminative information under unsupervised conditions. Unfortunately, standard NMF ignores this important information. To address these flaws, a novel NMF algorithm, called \(\ell_{2,1}\) norm and Hessian Regularized Non-negative Matrix Factorization with Discriminability (\(\ell_{2,1}\)HNMFD), is developed in this paper, which is designed to include local geometrical structure preservation, row sparsity and to exploit discriminative information at the same time. Firstly, Hessian regularization is introduced in the framework of NMF to preserve the intrinsic manifold of the data. Then, \(\ell_{2,1}\) norm constraints are added on the coefficient matrix to ensure that the representation vectors are row sparse. Furthermore, approximate orthogonal constraints are added to capture some discriminative informational in the data. An optimization scheme is developed to solve the objective function. The rest of the paper is organized as follows: In Section 2, we give a brief review of related works. In Section 3, we introduce our \(\ell_{2,1}\)HNMFD algorithm and the optimization scheme. Experimental results are presented in Section 4. Finally, we draw a conclusion and point out future work in Section 5.
# Mars to Earth Data Downloading: A Directory Synchronization Approach
## 3 Mars to Earth Scenario  
The Mars to Earth scenario considered here derives from that used in the ESA/ESOC project \"Delay tolerant network for flexible Communication with Earth Observation satellites\", described in [14; 15]. Given the different aim of the present paper, this scenario has been slightly simplified to consider only one Mars orbiter instead of two.
# Enhanced Spatial Stream of Two-Stream Network Using Optical Flow for Human Action Recognition
## 3 Proposed Methodology
### Avoiding Overfitting  
Overfitting is mainly caused by having a small amount of data available to train a deep network. To tackle this issue, we adapt some helpful techniques to minimize overfitting in the spatial and temporal streams.
# Hydrogen Materials and Technologies in the Aspect of Utilization in the Polish Energy Sector
## 7 Polish Prototype Installations for Production and Storage of Hydrogen  
As part of ongoing research and development projects, many prototype installations are being created. One of the most important is the world's first small-scale and bidirectional green hydrogen production installation based on solid oxide cells with a power of 10 kW, operating in Elblag at the Energa Kogeneracja heat and power plant, cooperating with the BB20 biomass unit of the heat and power plant. It is the result of a project called RSOC [94], conducted by the Faraday Research and Development Centre of the Energa Group and the Institute of Fluid-Flow Machinery of the Polish Academy of Sciences in cooperation with the Institute of Energy. The employed solid oxide electrochemical cells can operate alternately in the electrolyser mode, producing hydrogen, or in the fuel cell mode, converting hydrogen into electricity. The pilot installation is based entirely on Polish patents, technology, and know-how that can be used in hydrogen production in addition to high-power energy storage installations. There is no doubt that the RSOC project is one of the Polish technological achievements for the development of the hydrogen economy.  
In turn, a research team from the Faculty of Technical Physics and Applied Mathematics of the Gdansk University of Technology developed a reactor for the pyrolysis of unsorted municipal and industrial waste W2H2 [95]. In one device, waste is pyrolysised and then the resulting reaction products are purified. The final product is char with a carbon content of approximately 60% and energy recovered in the form of gas with a high hydrogen content.  
An important pilot project on a national scale is the green hydrogen production installation based on electrolysis implemented by the Patnow-Adamow-Konin power plant complex (ZE PAK SA, Konin, Poland), co-financed from European funds [68]. The system will consist of two proton exchange membrane (PEM) electrolysers powered by renewable energy, both from a local photovoltaic power plant (60%) and from external suppliers (40%). The initial power of this unit is 2.5 megawatts, with the possibility of expansion to five megawatts. The hydrogen produced in the installation is already supplied to refuelling stations for passenger cars and buses throughout Poland.  
In turn, the aim of the VETNI project implemented by Grupa Lotos together with the Institute of Energy and AGH University of Science and Technology in Krakow is to construct a pilot installation for the production of high-purity hydrogen based on solid oxide electrolysers (SOEs) powered by energy from renewable sources, integrated with refinery processes. The use of a new group of materials with a reduced Co content in the construction determines high efficiency (16 kg/day) in addition to operational stability of the electrolyser [96].
## 4 Simulation Results
### Case 9: Fuel cost with voltage stability improvement during contingency condition  
We consider the previous case with disruption of line (2-6) to simulate N - 1 contingency. Best solutions for the fuel cost and the L index using HIC-GWA are 802.00 ($/h) and 0.11 (p.u), respectively. Compared with MSA, MDE, MPSO, FPA, and MFO approaches illustrated in Table 14, HIC-GWA has significantly reduced the fuel cost and L index during contingency condition.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Solutions** & **MSA** & **MDE** & **MPSO** & **FPA** & **MFO** & **HIC-GWA** \\ \hline Fuel cost (\$/h) & 801.22 & 802.10 & 801.70 & 801.15 & 801.67 & 797.80 \\ Emission (t/h) & 0.36 & 0.35 & 0.36 & 0.37 & 0.34 & 0.37 \\ \(P_{\text{loss}}\) (MW) & 8.98 & 9.06 & 9.20 & 9.32 & 8.56 & 8.75 \\ VD (p.u.) & 0.93 & 0.89 & 0.83 & 0.88 & 0.84 & 1.98 \\ \(L\) index & 0.14 & 0.14 & 0.14 & 0.14 & 0.14 & 0.11 \\ Total cost & 814.94 & 815.84 & 815.44 & 814.91 & 815.43 & 808.38 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Comparison of the proposed approach with different approaches for case 8.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Solutions** & **MSA** & **MDE** & **MPSO** & **FPA** & **MFO** & **HIC-GWA** \\ \hline Fuel cost (\$/h) & 803.31 & 803.21 & 803.98 & 803.66 & 803.79 & 802.45 \\ Emission (t/h) & 0.36 & 0.36 & 0.36 & 0.37 & 0.36 & 0.36 \\ \(P_{\text{loss}}\) (MW) & 9.72 & 9.60 & 9.92 & 9.93 & 9.87 & 9.95 \\ VD (p.u.) & 0.11 & 0.13 & 0.12 & 0.14 & 0.11 & 0.10 \\ \(L\) index & 0.15 & 0.15 & 0.15 & 0.15 & 0.15 & 0.13 \\ Total cost & 814.15 & 815.86 & 816.00 & 817.32 & 814.35 & 812.05 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Comparison of the proposed approach with different approaches for this case.  
**Case 10: Fuel cost, voltage deviation, emission, and power loss**  
The multiobjective function defined by Equation (24) combines three previous cases: 4, 5, and 7 to minimize fuel cost, voltage deviation, emission, and power loss simultaneously. Simulation results, illustrated in Table 15, show that HIC-GWA has significantly reduced the fuel cost, emission, power loss, and voltage deviation compared with MSA, MDE, MPSO, FPA, and MFO approaches  
In cases 6-10, the proposed metaheuristic showed a better exploration than recently published optimization methods that appear to be stuck at a local minimum.  
The total cost convergence curve for case 10 is displayed in Figure 3. The HIC-GWA approach converged in less than 50 iterations.  
Convergence curves of the fuel cost, voltage deviation, power loss,
## 3 Information and Communication Technology Solutions for Precision Agriculture
### Data Storage and Distribution
#### Blockchain and Smart Contracts  
Blockchain technology is gaining traction globally in the area of data management and storage. This technology can be rather beneficial for PA as well. Blockchain can be defined as the distributed ledger which contains an ever-growing list of data records. All of the participating nodes confirm the validity and the accuracy of the aforementioned data records [76]. The term block (of blockchain) refers to a record containing data, a value with the hash (digital fingerprint of a block's data) of the current block as well as a value with the hash of the previous block. The link between the hash of the current block and the hash of the previous block serves as a basis for a cryptographically-linked chain of blocks [77]. Precision irrigation encompasses the accurate calculation of the crop water requirements as well as the precise application of water volumes at the right time. [PERSON] et al. highlight the advantages of integrating blockchain technologies for ensuring trust and security in precision irrigation systems as well as for mitigating various attack vectors through a proposed framework [78]. After analyzing several data threats directly connected with PA (e.g., malicious attacks or distortion of environmental data stored in centralized servers), [PERSON] et al. underscore the role of blockchain technology and its cryptoeconomic security features in ensuring that the data and the technological infrastructure are protected from malicious attackers. The authors also provide an example of blockchain technologies utilization for a secure national distributed database which conforms to international agricultural standards and naming conventions [79].  
Smart contracts refer to fully digital contracts in the form of small script programs, which are used and stored in blockchains, thus providing a tamper-proof logic [77]. [PERSON] et al. introduce a decentralized traceability service platform, utilizing blockchain for ensuring data integrity and reliability as well as smart contracts for irreversible and trackable financial transactions among users.  
This solution uses IoT sensors for the collection of environmental data regarding the crops. These data are stored using a peer-to-peer (P2P) network. As the specific platform has the potential of being scalable by applying data tracking to each component on the agriculture production chain, it can enable farm-to-fork traceability [80]. [PERSON] et al. demonstrate a solution for the collection and the distribution of robot sensor data in a trusted information space using smart contracts and blockchain technologies. In addition, utilizing IoT and fuzzy logic technologies, the proposed solution enables forming coalitions among robots with trust from all participants in order to perform tasks in PA which require cooperation [81].
# Performance of the Dual-Chamber Fungal Fuel Cell in Treating Tannery Wastewater
## 2 Materials and Methods
### Analytical Methods  
The cell voltage (\(E\), V) was monitored on-line via a data acquisition system (Agilent 34970A; Agilent Technologies, Santa Clara, CA, USA) and recorded automatically every 20 min. The anodic potential (V) was measured against a silver-silver chloride reference electrode (+224.5 mV vs. SHE), which was inserted into the anode chamber. The anodic pH was continuously monitored using a pH meter (PHS)-5; INESA Instrument, China) and automatically recorded every 30 s. The current (\(I\), A) was calculated according to the following equation [43]:  
\[I=\frac{E}{R}\]  
where \(E\) is cell voltage (V) and \(R\) is the external resistance (1000 \(\Omega\)).  
Figure 1: (**A**) Designative scheme of the FFC, and (**B**) lab scale model of the FFC.  
The power output of the cells (\(P\), mW) was calculated according to the following equation [43]:  
\[P=IE\]  
Variable external resistances were used to obtain the polarization curve in which each resistance was changed. Power density (mW/m\({}^{2}\)) and current density (mA/m\({}^{2}\)) were based on the cathode surface area (m\({}^{2}\)) [43].  
\[Power\ Density\ (PD)=\frac{U^{2}}{A\ *\ R}\]  
\[Current\ Density\ (CD)=\frac{U}{A\ *\ R}\]  
where \(U\) is the voltage output (V), \(R\) is the total resistance (\(\Omega\)) used and \(A\) is the electrode area (m\({}^{2}\)).  
The COD removal efficiency (\(Ef\)) % was calculated according to the following equation:  
\[Removal\ Efficiency\ (Ef)\ \%=\frac{\Delta S}{S}\times 100\]  
where \(\Delta S\) is the removal of COD concentration (g/L) and \(S\) is the initial COD concentration (g/L).
# Forward and Inverse Studies on Scattering
## 2 Forward Problem by Modified BEM
### Verification of Reflection Coefficients  
For verification of the reflection coefficients obtained from Equation (7), we adopt the scattering equation in half-space, which uses the half-space Green's function to calculate the far-field wave pattern, as shown in [36]:  
\[u_{n}^{\text{sca}}(X)=\int_{\Gamma_{1}^{-}\cup\Gamma_{1}^{+}}C_{ijkl}n_{j}(x) \frac{\partial G_{ln}^{\text{H}}(x,X)}{\partial x_{k}}u_{n}^{\text{inc}}(x)d \Gamma(x)\quad(i,j,k,l=1,\,2) \tag{11}\]  
where \(C_{ijkl}\) is the stiffness tensor, \(n_{j}\) is the unit normal vector pointing outward the flaw area, and \(G_{ln}^{\text{H}}(x,X)\) represents the Green's functions in the half-space. In the far-field, the half-space Green's function can be expressed in the Rayleigh wave form [37]:  
\[G_{a\beta}^{\text{H}}(x,X)=f(\xi_{0})Ap_{n}(x_{2})Bp_{\beta}(X_{2})e^{+i\xi_{0 }(x_{1}-X_{1})},(\alpha,\,\beta=1,\,2). \tag{12}\]  
where  
\[\begin{array}{c}A=\left\  
We perform a Fourier transform to the incident wave signal in time-domain to obtain its frequency spectrum, and calculate the displacement field and reflection coefficients at each frequency point by the modified BEM. As an example, the whole displacement field along the surface at frequency \(\Omega=2\pi\) is plotted in Figure 6, and is compared with the results obtained by Kawase [10]. It is seen that the BEM displacements show good agreement with the previous results.  
Figure 4: Numerical examples: (**a**) a half-space with an arc-shaped canyon flaw; (**b**) a half-space with double canyons.  
Figure 5: Time-domain signal of the incident wave.  
Figure 6: Comparisons of the surface displacements obtained by the modified BEM and those in Kawase [10].  
Displacement fields at other frequency points have also been calculated in the same manner. An inverse Fourier transform is then applied to the frequency-domain displacements to attain time-domain wave signals for different points along the surface, as shown in Figure 7a,b, from which we can see that the spurious reflection waves are not generated.  
**Example 2**.: _Similarly with Example 1, an artificial arc-shaped canyon is located on the surface of an isotropic elastic half-space, whose depth-radius ratio \(d_{max}/r=0.2\). The Poisson's ratio is 1/3. We calculated the reflection coefficients in frequency domain within range \(\Omega=\Omega r/c_{T}\) from 0.2 to 5.0 at an interval of 0.2._  
The circular dots in Figure 8 shows the reflection coefficients obtained directly from the modified BEM program, and the rectangular dots represents those derived from half-space scattering Equation (11). The good agreement illustrates the validity of the forward analysis.  
Figure 7: Displacement at different surface points in time-domain (**a**) \(x_{1}\) component; (**b**) \(x_{2}\) component.  
This example will be used in the following inverse reconstruction.
# ISAR Image Quality Assessment Based on Visual Attention Model
## 2 Proposed Method
### Channel Attention Block  
In regular self-attention (SA), the key-query dot product operation promotes feature interactions between patches in the image, thus extracting the features across space. However, the information embedded in channels is neglected. The CAB increases feature interactions among channels. We concatenate the feature maps of the {7,8,9,10}th layer of Gram-T to obtain output features \(F^{N\times C\times L}\), where N is batch number, \(L=H\times W\). \(Q\in\mathbb{R}^{N\times C\times L}\), \(K\in\mathbb{R}^{N\times C\times L}\) and \(V\in\mathbb{R}^{N\times C\times L}\) are achieved by passing output features \(F^{N\times C\times L}\) of the Transformer encoder through three independent projections. The CAB performs matrix multiplication with \(Q\in\mathbb{R}^{N\times C\times L}\) and \(K^{T}\in\mathbb{R}^{N\times L\times C}\) to obtain correlations between channels and then pass them through a softmax layer to obtain a normalized attention weight. The correlation results are obtained by multiplying the attention weight by V. The process is outlined as follows:  
\[F=\text{Attn}(Q,K,V)+F \tag{2}\] \[\text{Attn}(Q,K,V)=V\cdot\text{Softmax}(Q\cdot K^{T}/\beta) \tag{3}\]  
where \(\beta\) is a tunable parameter. The CAB module has two advantages: Firstly, it integrates four different sizes of layer feature maps in Gram-T and assigns different weights to the channels according to their relevance to the quality score. Secondly, the CAB encodes semantic information among diverse channels, which facilitates the extraction of global information (Figure 2).
## 5 Results and Discussion
### Instantaneous Throughput
#### 5.1.2 Malicious Network  
In this case, the malicious node (black hole-attack node) attacks the healthy network to check the system's behavior. Node 4 is an attacking node that is used to make the black hole attack network. Figure 14 shows that throughput did not even increase up to 72 s. The reason is that the malicious node pretends to be an efficient relay, attracts more packets, and then immediately drops those packets instead of forwarding them to the destination. Moreover, it creates network congestion, as other nodes continuously attempt to resend the lost packets that lead to bottlenecks and cause a reduction in throughput. Additionally, the malicious node misdirects the incoming traffic into an alternative or longer path that does not lead to the desired destination, causing delays and latency in data transmission and decreasing the throughput. However, the trend changes abruptly after 72 s; the graph shows the increase in throughput; the reason behind this is rerouting. The system finds the optimal path where the malicious node is unavailable. The malicious network under the black hole-attack node gives a throughput of 13,000 kbps at 200 s because the attacking node drops many packets during routing, unlike a healthy network, where that value reaches 20,000 kbps.
# Resilience Quantification of Smart Distribution Networks--A Bird's Eye View Perspective
## 3 Taxonomy of Resilience Evaluation Methods
### Resilience Metric Type
#### 3.4.1 Operational Metrics  
Metrics that use performance as described in terms of functional service (electric, telecoms) and associated monetary costs. Expected lost load [24], supplied energy [117], and recovery duration [14] are examples of performance measures used by this type of resilience metrics.