esa-sceva/satcom-chunk-collection huggingface dataset:
# UPGCN: User Perception-Guided Graph Convolutional Network for Multimodal Recommendation
## 4 Experiments
### Evaluation Metrics  
We employed the widely recognized metrics Recall@K and NDCG@K, abbreviated as R@K and N@K, to assess the recommendation effectiveness of UPGCN and baseline models. In the experiments, we present empirical results for K values of 10 and 20 on the test set.  
Recall@K is a metric in recommendation systems that evaluates the proportion of actual relevant items in the top-K recommended items. It measures the model's effectiveness in retrieving items that a user is likely to find interesting among the initial K suggestions. The formula for calculating Recall@K is presented below:  
\[\text{Recall@K}=\frac{|Rel_{u}\cap Rec_{u}|}{|Rel_{u}|} \tag{27}\]  
where \(Rel_{u}\) denotes the set of items that user \(u\) has interacted with, \(Rec_{u}\) denotes the set of the top K recommended items for user \(u\).  
NDCG@K is a metric employed by recommendation systems to assess the quality of the top-K recommended items. The first step involves calculating the Discounted Cumulative Gain (DCG), which is based on the rankings and relevance scores of pertinent items found in the recommendations. The formula is as follows:  
\[DCG_{u}\text{@K}=\sum_{i=1}^{K}\frac{2^{r(i)}-1}{\log_{2}(i+1)} \tag{28}\]  
where \(r(i)\) denotes the relevance score of the \(i\)-th recommended item. If the recommended list matches item \(i\), then \(r(i)\) is 1, otherwise it is 0. Subsequently, the calculation of \(IDCG_{u}\text{@K}\) will be conducted:  
\[IDCG_{u}\text{@K}=\sum_{i=1}^{K}\frac{1}{\log_{2}(i+1)} \tag{29}\]  
The definition of NDCG@K is as follows:  
\[NDCG_{u}\text{@K}=\frac{DCG_{u}\text{@K}}{IDCG_{u}\text{@K}} \tag{30}\]  
\[NDCG\text{@K}=\frac{\sum_{u\in Ult^{te}}NDCG_{u}\text{@K}}{|U^{te}|} \tag{31}\]  
where \(U^{te}\) is the set of all users in the test dataset, and \(\left|U^{te}\right|\) is the number of users in the test dataset.
# Research on Rolling Bearing Fault Diagnosis Method Based on ECA-MRANet
## 3 Fault Diagnosis Method Based on ECA-MRANet
### Efficient Channel Attention  
Efficient Channel Attention (ECA) [23] is a local cross-channel interaction strategy that does not require dimensionality reduction. This is efficiently implemented by one-dimensional convolution, which allows the network to pay more attention to the important channels and to realize the allocation of weights to the channels, thus improving the representation of the feature map. Its structure is shown in Figure 8.  
The ECA first performs global average pooling on the input feature maps to obtain \(1\times 1\times\text{C}\) feature maps. Subsequently, it adaptively selects the size of the 1D convolution kernel and uses 1D convolution for local cross-channel interactions. This process yields the weight matrix of each channel, which is then activated using the Sigmoid function. Finally, the input features are multiplicatively combined with the weight matrix to adaptively assign the weights of each channel. In this paper, we use ECA to fuse the features extracted from the three channels to enhance the discriminative and generalization ability of the network.
# Current Progress in Femtosecond Laser Ablation/Ionisation Time-of-Flight Mass Spectrometry
## 3 LIMS System: fs-Laser Ablation Ion Source and Time-of-Flight Mass Analyser
### Femtosecond Laser Radiation
#### 3.1.3 Femtosecond Laser Optics  
Refractive optics (i.e., a lens) are a common way to focus light on a spot and the lenses are widely used in the setup for laser ablation source. To achieve a focal spot size of about 10 \(\upmu\)m or less, a telescope system is often used to expand the beam to the required diameter before focusing [21; 114]. Nevertheless, refractive optics are subjected to chromatic and spatial aberrations, which typically increase the complexity of the optical design. Femtosecond-laser radiation is more sensitive to employed optical elements compared to other lasers with longer pulse widths. The spectral width of a 100 fs pulse (\(10^{-13}\) s) corresponds to about 10 nm in wavelength (\(\Delta\)v = 4.41 * \(10^{12}\) Hz) and can suffer from group velocity dispersion [101]. IR femtosecond pulses suffer less from group velocity dispersion compared to the UV pulses while propagating through thick transparent materials. Dispersion effect also takes place when light is reflected from a multi-layered dielectric mirror, such as those used in laser cavities. Reflection of light from such mirrors is the result of constructive interference between rays that have been partially reflected at the interfaces of \(\lambda\)/4 dielectric layers. Inherently, group velocity dispersion occurs in these mirrors and can affect the overall beam profile. The wide spectral width of femtosecond optical pulses requires a large enough bandwidth for every optical element used in an experimental setup to accommodate all the frequency components of the applied pulses. Femtosecond radiation is a delicate entity, such that any spectral narrowing induces a broadening in the time domain that can be critical to the ablation process. To avoid spurious phase distortions and dispersion while transferring femtosecond radiation from one point to another, aluminium- or silver-protected broad band mirrors are recommended. Moreover, reflective optics with aluminium- or silver-protected spherical or parabolic mirrors are often more advantageous for femtosecond radiation focusing over refractive optical elements. By applying such elements, high-quality focusing spots without aberration or astigmatism can be obtained [24].
# Ion Current Rectification in Extra-Long Nanofunnels
## 3 Results and Discussion
### Accumulation
The ion depletion for positive voltage (+1 V) also increases at low C\({}_{\rm Bulk}\), but in this case, the effect is charge sign dependent. While the concentration of K\({}^{+}\) ions is well below C\({}_{\rm Bulk}\) in the funnel, it increases in the nanochannel by a factor of 18 with respect to C\({}_{\rm Bulk}\) (Figure 9c); there are only a few Cl\({}^{-}\) ions in both the funnel and the nanochannel (Figure 9d). A small increase in the Cl\({}^{-}\) ion concentration only occurs in the cathodic reservoir around the nanochannel end (enrichment area). These results are also visible in the plots in Figure 7 for a voltage applied of +1 V. The ion accumulation effect in the whole funnel is shown in the plot in Figure 10. Here, the K\({}^{+}\) (Figure 10a) and Cl\({}^{-}\) (Figure 10b) concentrations in the nanofluidic device at C\({}_{\rm Bulk}\) = 10\({}^{-3}\) M are plotted with \(-\)10 V applied between the reservoirs. It is clear that the accumulation effect is independent of the ion charge sign. It should be noted that the colored scale of the two plots is different. Curves with a similar trend are reported by [PERSON] [43] and [PERSON] [35]. The maximum concentration of positive ions is higher than the  
Figure 7: K\({}^{+}\) (**a**) and Cl\({}^{-}\) (**b**) concentrations in the nanochannel at C\({}_{\rm Bulk}\) 10\({}^{-5}\) M (10\({}^{-2}\) mol/m\({}^{3}\)), with +1 V applied between the reservoirs. one of negative ions and is reached at the negatively charged walls of the funnel and nanochannel (not visible in this plot). In addition to C\({}_{\text{Bulk}}\), the accumulation (for negative voltages)/depletion (for positive ones) phenomena are strongly dependent on the surface charge on the walls of the funnel and nanochannel. As mentioned before, with no surface charge, the ion concentration is constant and equal to C\({}_{\text{Bulk}}\) in the whole device (see Supplementary Materials). Figure 8: K\({}^{+}\) (**a**) and Cl\({}^{-}\) (**b**) ion concentration along the transversal section at the middle of the nanochannel as a function of the applied voltage between the reservoirs (C\({}_{\text{Bulk}}\) = 10\({}^{-5}\) M = 10\({}^{-2}\) mol/m\({}^{3}\)). Figure 1:0 C/C\({}_{\text{Bulk}}\) (%) for K\({}^{+}\) (**a**,**c**) and Cl\({}^{-}\) (**b**,**d**) along the longitudinal section of the funnel and nanochannel, calculated for different C\({}_{\text{Bulk}}\) with \(-\)1 V (**a**,**b**) and \(+\)1 V (**c**,**d**) applied between the reservoirs. (**e**) Zoom-in of the graph in (**c**). Note the different scale on the ordinate axis. C\({}_{\text{Bulk}}\): 1 M (black line), 10\({}^{-1}\) M (dark yellow), 10\({}^{-2}\) M (red), 10\({}^{-3}\) M (green), 10\({}^{-4}\) M (blue), and 10\({}^{-5}\) M (magenta).
# An Islanding Signal-Based Smooth Transition Control in AC/DC Hybrid Micro-Grids
## 1 Introduction
This kind of control performs well with the small amount of power exchange between the micro-grid and utility grid. In [18], a diesel generator is used to compensate for power changes during transition period. In contrast to electronic converter-based generators, it takes time for rotary machines to output the required power. Therefore, the way to minimize the exchange power may take effect in a smooth transition. On the other hand, a control strategy may be modified to satisfy a smooth transition. In [19], an adaptive droop coefficient method is proposed to mitigate voltage deviation, thereby reducing the transient impact. In [20; 21], the focus is on a smooth transition control method based on a regulation state follower when switching from grid-connected mode to islanding mode. There are two controllers for two different working modes. The proposed smooth control method makes use of the free controller to follow the state of the working one, ensuring that the output of two controllers stay equal. This does work in some situations. However, it takes a long time to realize the state following. In addition, the control parameters are hard to be set to fit for different working modes, or even worse, it happens that no parameters are available. As a result, during the transition period, a smooth control that can both deal with the various exchanges of power between micro-grid and utility grid, and respond instantly with easy controller configuration, is expected. To this end, this paper proposes a smooth transition approach based on the islanding signal. It focuses on the regulator of the control system instead of power exchange of the whole system. In the proposed control strategy, the state of the controller is renewed if an islanding signal is detected during the transient period. In this way, the state step of the controller will be completely suppressed, which usually exists in the traditional state follower-based transition control. Furthermore, setting the control parameters becomes much easier than that in the state follower-based approach. Since the state step has been suppressed, it takes a short time to realize the smooth transition. The rest of this paper is organized as follows. Section 2 builds a model of AC/DC hybrid micro-grid for a small plant. In Section 3, the transition control strategy is described in detail. Simulation results and impacts on the proposed approach are discussed in Section 4, and conclusions are drawn in Section 5.
## 2 Technical Backgrounds
### Security Considerations and Requirements
#### 2.4.3 Auditability  
5G networks must be able to monitor and manage the access of heterogeneous devices to data networks and services. The 5G network provides various types of services through various paths, and in the process, the environment may cause vulnerabilities that use inappropriate authentication and communication protocols [7]. These vulnerabilities can be mitigated by controlling the network through tiered and centralized architectures. The tiered architecture allows the network to efficiently understand and manage the configuration and behavior of each environment, while centralized architecture facilitates the coordination of the entire network [9; 18; 23; 24]. Since 5G's security architecture uses two mutual authentication processes, it is necessary to manage the operation of authentication data efficiently [15].
## 4 Control Analysis Based on Sensitivity
### Validation  
A set of simulations are performed to validate the previous analysis. Two configurations are considered. Firstly, an open-circuit configuration is evaluated to analyse the controller's performance in optimal conditions. Secondly, the controlled voltage is disturbed by different harmonic current components.  
Figures 11 and 12 show the error time evolution under different voltage references with no load. This voltage reference is set to be \(v_{c}(t)=1\text{sin}(h100\pi t)\) considering different harmonics components (with \(h\) being the corresponding first, second, third, and eleventh harmonic component). It can be deduced that PRHC is better at regulating the harmonic frequencies designed for (third and fifth); refer to Figure 12b. Out of these specific tones, the FPR with \(\alpha\) below 1 is the best option.  
Figure 13a-d show the regulated voltage time response when a current disturbance at different frequencies (first, second, third, and eleventh harmonics component) is applied. No feedforward is considered for the disturbance rejection analysis, and the disturbance is set to 1 A peak. Any controllers under consideration can reject the disturbance significantly, but the FPR with \(\alpha=0.75\) is faster and responds to the option that excites the disturbance the least. Thus, in terms of comparison, the FPR with \(\alpha\) below 1 is the best option for rejection.  
\begin{table}
\begin{tabular}{l l l l l} \hline \(\alpha\) & \(\mathbf{M_{t}}\) (dB) & \(\mathbf{M_{t}}\) & \(\mathbf{\Delta G_{\text{Mt}}}\) (\%) & \(\mathbf{\Delta P_{\text{Mt}}}\) (\({}^{\circ}\)) \\ \hline PRHC & 4.61 & 1.7 & 21.7 & 12.45 \\
0.75 & 4.19 & 1.62 & 23.9 & 13.71 \\
1 & 2.71 & 1.366 & 36.9 & 21.26 \\
1.5 & 1.78 & 1.227 & 56.2 & 32.63 \\ \hline \end{tabular}
\end{table}
Table 5: Tuned uncertainty parameters, based on Table 3.  
Figure 10: Uncertainties’ superior thresholds.  
Figure 11: Time\(-\)response comparison when different references are present, being \(\omega_{0}=100\pi\) rad/s (50 Hz). Harmonics 1 and 2.  
Figure 12: Time\(-\)response comparison when different references are present, being \(\omega_{0}=100\pi\) rad/s (50 Hz). Harmonics 3 and 11.
# Malicious Cognitive User Identification Algorithm in Centralized Spectrum Sensing System
## 1 Introduction
This method combines the game theory to establish the behavioral game model between honest cognitive users and malicious cognitive users, and based on this model quantitatively calculating the caution of the microblogging users, this method strengthens the microblogging user behavior characteristics' ability to distinguish honest users and malicious users. Reference [8] proposed a single channel centralized spectrum sensing algorithm based on the \(\beta\)-reputation (\(\beta\)-SCCSA) system [9]. This algorithm determines the cognitive user's reputation and weight by knowing the cognitive user's historical performance and can react effectively against malicious cognitive users. However, when a malicious user reduces the attack probability (to improve chances of remaining undetected), the algorithm takes a long time to identify malicious users, or simply cannot identify malicious users. Based on existing research results and the characteristics of collaborative spectrum sensing, we propose using the \(\beta\)-MIAMC algorithm to solve the problem of malicious user identification when the malicious user attack probability is small. By analyzing the simulation results in the case of a small attack probability, the proposed algorithm can reduce the false alarm probability of malicious users by more than 20%. When the attack probability is greater than 7%, the proposed algorithm can distinguish malicious users with 100% certainty.
## 2 Modeling of InGaP/GaAs Tandem Solar Cells
### Electrical Characteristics  
Analyzing the fundamental electrical properties of a solar cell is an essential method for evaluating its performance. During the process of optimizing a specific design, considering these parameters proves to be the crucial first step. The main electrical characteristics on which this study is focused are the short-circuit current density \(J_{SC}(\text{mA}/\text{cm}^{2})\), the conversion efficiency \(\eta(\%)\), the filling factor \(FF(\%)\), and the open circuit voltage \(V_{OC}(\text{V})\). Their definition is based on the main known variables such as [9; 31] the current generated by the light or the photo-generated current \(I_{L}(\text{A})\), the dark current or reverse saturation current \(I_{0}(\text{A})\), the Boltzmann constant \(K\) (\(1.380649\times 10^{-23}\) J \(\cdot\) K\({}^{-1}\)), the ideality factor of the diode \(n\), the temperature of the cells \(T(\text{K})\), the elementary charge \(q\) (\(1.602\times 10^{-19}\) C), \(P_{max}\) the maximum power \(P_{max}(\text{W})\), and the incident power \(P_{in}(\text{W})\). The current in the solar cell is defined as follows [29]:  
\[I=I_{0}\left[\exp\left(\frac{qV}{nKT}\right)-1\right]-I_{L} \tag{2}\]  
The open circuit voltage \(V_{OC}\) is expressed as follows [29]:  
\[V_{OC}=\frac{nKT}{q}ln\left(\frac{I_{L}}{I_{0}}+1\right) \tag{3}\]  
And the fill factor \(FF\) equation is given as follows [29]:  
\[FF=\frac{P_{max}}{J_{SC}V_{OC}} \tag{4}\]  
The maximum power conversion \(P_{max}\), which can be achieved by transforming solar energy coming from the solar spectrum into electricity, can be expressed as follows [29]:  
\[P_{max}=J_{SC}V_{OC}FF \tag{5}\]  
Hence, the conversion efficiency can be calculated as follows [29]:  
\[\eta=\frac{P_{max}}{P_{in}}=\frac{J_{SC}V_{OC}FF}{P_{in}} \tag{6}\]  
These equations provide a solid theoretical basis for in-depth analysis of the electrical performance of a solar cell, thereby effectively guiding the optimization process of double-junction solar cell design.
## 3 Results and Discussion
### Product Circularity Dashboard  
Having applied the BIORADAR product circularity monitoring framework to five bio-based fertilizers, and after discussing the results, strengths, and limitations of each of the indicators, it is possible to summarize the results in a product circularity dashboard, as shown in Figure 7.  
The product circularity dashboard provides a comprehensive overview of the circularity performance of the studied products across the four indicators. This visual tool allows for the classification of the bio-based fertilizers into the following three groups:  
1. Those exhibiting a higher level of circularity based on the three indicators with available data--algae biomass and spent mushroom substrate.
2. Those with an intermediate level--feather meal and compost.
3. The product with the lowest circularity score--wood vinegar--primarily due to its performance in the NSRI indicator.  
The most significant differences among the products can be observed in the CIN. While all products originate from bio-based materials and require minimal--or no--non-renewable inputs, what truly differentiates them is the efficiency of their manufacturing processes in terms of nutrient recovery. According to the CIN results, algae biomass retains all its nutrients, as none are separated during processing. In contrast, spent mushroom substrate undergoes two composting phases and mushroom cultivation, which result in the loss of approximately half of the original nutrient content. Feather meal is subjected to extensive hydrolysis and chemical transformations, while compost involves multiple pretreatment steps, including anaerobic digestion, prior to the final composting stage. In this context, the CIN effectively reflects the core circular economy principle of \"doing more with less\".  
This article demonstrates how the BIORADAR product circularity monitoring framework enables the evaluation of multiple dimensions of circularity. Its streamlined structure--aligned with recent developments in CE indicators, such as those proposed by [PERSON] et al. [73]--makes it suitable for business adoption without imposing excessive burdens related to data collection and processing.  
This study represents the first validation of this specific set of indicators for bio-based fertilizers, applied to 20 scenarios across five distinct bio-based fertilizers. It opens avenues for further research, particularly in validating the framework with a broader range of bio-based fertilizers, exploring data from various sources, and testing its applicability within the operational context of fertilizer-producing companies.
## 1 Introduction
Since it was first proposed, different AM technologies have been developed and different kinds of materials have been introduced, such as metal [57] and polymer [58]. The 3D printing technology also has been introduced in the fabrication of different microwave components, such as waveguide [59], lenses [60] and antennas [61]. A 3D printed microwave termination with a tapered shape operating at X-band (8-12 GHz) is proposed in [62]. The 3D-printed absorber based on the filaments with enclosed metal particles is also investigated for microwave absorption at 10 GHz band in [63]. In this paper, a cylindrical-dielectric-resonator-based ultra-broadband absorber for microwave absorption, which is fabricated using 3D printing technology at a low cost, is studied. The carbon-loaded ABS material is introduced in the design of an all-dielectric microwave absorber for the first time. By exciting both the dielectric resonator (DR) mode and the grating mode of the dielectric resonator, near-unity absorption over an ultra-broad bandwidth can be obtained. Benefitting from the frequency-dependent permittivity of the ABS material in the band of interest, the dielectric resonator resonates in its DR mode over broadband at lower frequency with three absorption peaks, corresponding to two fundamental and one higher order magnetic dipole modes. In the higher frequency band, the grating mode of the resonator can be excited, generating one more absorption peak. By optimizing the dimensions of the dielectric resonator, an ultra-broad absorption band can be achieved over the range of 3.9 to 12 GHz with an absorptivity higher than 90%. The operation bandwidth of the designed absorber can reach over 100%. Fabricated by the commercial 3D printer, the prototype of the designed absorber is experimentally investigated, along with full wave simulation. Good agreement between measured and simulated results of the absorber can be observed. Additionally, the angular tolerance of the absorber in transverse electric (TE) and transverse magnetic (TM) mode are both discussed, showing high absorption under wide angles of incidence. All the excellent performance indicate that the designed absorber is promising for different applications, such as radar cross-section reduction, low-cost anechoic chambers, and stealth technology. The paper is organized as follows: In Section 1, a brief introduction of the work is presented, and the state-of-the-art microwave absorber is given; in Section 2, details of the proposed absorber are presented, including the design diagram of the absorber; in Section 3, the characteristics of the printed material are measured and modeled, followed by numerical simulation of the designed absorber. The working principle of the absorber is discussed in detail in this section. In Section 4, the measurement results are shown and the simulated absorptivity under different angles of incidence is presented. In Section 5, the work is summarized.