esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy-Efficient Power Allocation in Non-Linear Energy Harvesting Multiple Relay Systems
## 3 Joint Optimization Algorithm for the Relay Transmit Power and PS Ratio
### Case 2
Then, the binary objective function is transformed into a one-variable objective function \(y_{2}(t_{i})=\frac{1}{2}\log_{2}\Big{(}1+\psi_{i}+\frac{t_{i}P_{3}h_{i}}{(t_{ i}+1)g_{i}}-x^{*}\cdot\big{(}\frac{t_{i}P_{3}h_{i}}{(t_{i}+1)g_{i}}+P_{Q}-P_{m} \big{)}\), which is only related to \(t_{i}\) by the substitution of \(P_{i}^{*}\). We let \(\frac{dy_{2}}{dt_{i}}=0\) and have \(t0=\frac{g_{i}\cdot-x^{*}\cdot 2\ln 2\cdot(1+\psi_{i})\cdot e^{2}}{x^{*} \cdot 2\ln 2\cdot(P_{3}h_{i}+(1+\psi_{i})\cdot e^{2})-g_{i}}\). Because \(\frac{d^{2}y_{2}}{dt_{i}}\) can be positive or negative, we have \(t_{i}^{*}\in\Big{\{}t_{a},t_{b},[t0]_{t_{a}}^{t_{b}}\Big{\}}\), and \(t_{i}^{*}\) is determined by the value of \(y_{2}(t_{i}^{*})\). Solution III can be expressed as \[\left\{ \begin{array}{l}P_{i}^{II*}=\frac{t^{II*}P_{3}h_{i}}{(t^{II*}+1)g_{i}} \\ P_{i}^{II*}=1-t_{i}^{III*}=1-\underset{t_{i}^{*}\in\{t_{a},t_{b},[t0]_{t_{a}}^{t _{b}}\}}{\arg\max}\left(\frac{1}{2}\log_{2}\Big{(}1+\psi_{i}+\frac{t_{i}^{*}P_{ 3}h_{i}}{(t_{i}+1)\sigma^{2}}\Big{)}-x^{*}\cdot\frac{t_{i}^{*}P_{3}h_{i}}{(t_{ i}+1)g_{i}}\right)\end{array} \right.\] (20) where \(t1=\frac{(\gamma_{\min}-\psi_{i})\cdot e^{2}}{P_{3}h_{i}-(\gamma_{\min}-\psi_{ i})\cdot e^{2}}\), \(t2=\frac{P_{2},g_{i}}{P_{3}h_{i}-P_{2},g_{i}}\) and \(P2=\min\big{(}P_{\max}-\varphi_{i},P_{m}-P_{Q}\big{)}\).
## 3 Results and Discussion
### Engineering Application and Discussion  
The coal samples from the 3302 fully mechanized caving face of the Longgu Coal Mine consist of 1/3 cooking coal and fat coal, which are prone to spontaneous combustion with a natural ignition period of 41 days. To validate the model's universality and accuracy, 10 sets of data were selected for SVC model prediction, as shown in Table 2. These datasets include routine tube bundle monitoring data and representative measurements from before and after spontaneous combustion events in the goat area.  
The analysis of the field data revealed no presence of C\({}_{2}\)H\({}_{4}\) or C\({}_{2}\)H\({}_{2}\) gases. Since C\({}_{2}\)H\({}_{4}\) is typically produced during high-temperature coal pyrolysis, its absence indicates that the coal had not reached pyrolysis temperatures, thus precluding a classification at the highest warning level.  
Based on the t-SNE method analysis, the warning levels demonstrated a clear progression of risk indicators. Under normal monitoring conditions, the system registered as \"Safe\". The risk level elevated to \"Low Risk\" when minor increases in CO concentration were detected alongside decreasing oxygen levels. As spontaneous combustion events developed, characterized by sharp increases in CO and CO\({}_{2}\) concentrations, significant oxygen depletion, and C\({}_{2}\)H\({}_{6}\) detection, the system escalated to \"High Risk\". Finally, the warning level reached \"Severe Risk\" when the fire spread intensified and the CO concentrations exceeded critical thresholds.  
The clustering and prediction results demonstrated reliable credibility, confirming that unsupervised learning methods are suitable for categorizing coal spontaneous combustion warning levels. When combined with the SVM classification model, this approach enables effective early warning predictions for coal spontaneous combustion in goat areas.
# FPGA-Based Frequency Tracking Strategy with High-Accuracy for Wireless Power Transmission Systems
## 2 WPT System Coupling Circuit Model
### System Frequency Detuning State Analysis  
In the actual design of the experimental platform, due to the design error of the coil inductance and resonant capacitance values and the influence of environmental changes, the component parameter values change. It is challenging to keep the system transmitter and receiver circuits under resonance. Regardless of operating at any fixed frequency, the system does not resonate all the time, which leads to system frequency detuning.  
Figure 2: Relation curves of system frequency with transmission efficiency and output power when frequency splitting occurs.  
In order to further analyze the effect of frequency detuning on output power, transmission efficiency, and phase difference, the circuit model parameters of Figure 1 are brought into Equations (4), (5), and (7) for analysis. The coupling coils are \(L_{1}=L_{2}=275\) uH, the compensation capacitors are \(C_{1}=C_{2}=40\) nF, and the resonating frequency is 48 kHz. Considering the error of design parameters and the influence of the environment, it is assumed that the coil inductance of the transceiver shifts to 279 uH and 280 uH, and the compensation capacitance shifts to 41 nF and 40.5 nF. On this basis, the effects of different operating frequencies are investigated.  
Figure 4 shows the operating frequency versus transmission efficiency and output power for a system without the splitting phenomenon. The maximum output power of the system is 46.9 kHz, which deviates from the designed resonant frequency. The maximum efficiency is 47.1 kHz, which is close to the maximum output power. The power and efficiency at the fixed frequency of 48 kHz decline significantly.  
In Figure 3, the change in load and coil distance causes a change in the phase difference when the system frequency is detuned. As the load changes from small to large and from near to far, the phase difference changes from less than 0\({}^{\circ}\) to more than 0\({}^{\circ}\), and the system  
Figure 4: Relation curve of system frequency with transmission efficiency and output power when there is no frequency splitting.  
Figure 3: The relationship between the phase difference and operating frequency: (**a**) distance fixed, resistance increases; (**b**) resistance fixed, distance increases.  
changes from capacitive to inductive. When the coil is too close, or the load is too small, the phase difference is smaller, and the system becomes more capacitive. When the coil is too far away, or the load is too large, the phase difference increases, and the system becomes more inductive.  
The frequency detuning phenomenon will reduce the output power and transmission efficiency of the system and make it possible for the system to work under a capacitive state, increasing the loss and insecurity of the system.
## 3 Problem Formulation
### Data  
The considered optical network uses both coherent and noncoherent transponders. The coherent transponders belong to Ciena's 6500 family. The transponder transmission rates are 100 Gbit/s, 200 Gbit/s, and 400 Gbit/s. The modulation formats include Quadrature Phase Shift Keying (QPSK) and Quadrature Amplitude Modulation (QAM): 16 QAM, 32 QAM and 64 QAM. The noncoherent transponders belong to Nokia's 1626 family and have bit rates of 2.5 Gbit/s and 10 Gbit/s.  
A schematic of the data preparation process is shown in Figure 1. Figure 2 and in detail, in Figure 3 are provided to facilitate the understanding of the attributes used, i.e.,: hop, hop length, path, and transponder. The dataset contains 1070 optical channels (paths), for which 18 were unsuccessful paths (\"bad\"), and (1052) were successfully deployed (\"good\").  
Figure 1: Data preparation process.
## 3 Model and Problem
### Optimization Problem  
In this work, we aim to turn off a specific number of antennas while reducing the sidelobe of CCAAs. Based on the models and analyses above, we can carefully control \(\mathbf{B}\) and \(\mathbf{I}\) to optimize the maximum SLL. Specifically, our considered optimization problem can be expressed as follows:  
\[\min_{\{\mathbf{B},I\}} f=f_{sll}\] (5a) s.t. \[0\leqslant I_{l}\leqslant 1,\forall l\in N_{T} \tag{5b}\] \[b_{l}=[0,1],\forall l\in N_{T} \tag{5c}\]  
We mention that our considered problem is a very complex optimization problem. Specifically, controlling the excitation current weights of antennas to minimize the maximum SLL is already an NP-hard problem. Our problem is more complicated than that, which means that our problem is also a complex NP-hard problem. Moreover, most existing works only consider optimizing the excitation current weights, which only have continuous decision variables. Likewise, there are also some works that only consider determining whether the antennas should be switched on or switched off, which only have binary decision variables. Different from these works, we consider both excitation current weight and antenna switched-on optimizations. Thus, our formulated problem involves both continuous and binary decision variables, which is difficult to solve with the existing methods. In this case, a powerful high-performance algorithm is urgently needed to solve our problem.
## 3 Results and Discussion
### Structural and Morphological Characterization
Figure 3: Solid-state \({}^{13}\)C CP/MAS NMR spectra of P(_bis_MEP), pure BC, and P(_bis_MEP)/BC_1 and P(_bis_MEP)/BC_2 nanocomposite membranes. Figure 5 displays the SEM images of the surface and cross-section of the two P(_bis_MEP)-based nanocomposites. Both membranes maintained the three-dimensional nanofibrillar network and lamellar microstructure representative of the BC morphology, particularly the P(_bis_MEP)/BC_1 nanocomposite that contains the higher BC content (79 wt %). So, these membranes present a highly anisotropic microstructure because of the alternating nanofibril layers of BC and the P(_bis_MEP) phase. This morphological anisotropy was recently demonstrated to originate differences in the protonic conductivity in the through- and in-plane configurations; nevertheless, the impact of this anisotropy in the fuel cell performance is lessened near RH saturated conditions, i.e., 98% RH [23]. The homogeneous distribution of P(_bis_MEP) inside the BC nanofibrous network was further corroborated by EDS analysis as illustrated in Figure 6. This technique certified the presence of phosphorus element as shown in the EDS spectrum (Figure 6c) of P(_bis_MEP)/BC_2, as well as the homogenous distribution of P(_bis_MEP) within the BC network as depicted in the SEM/EDS mapping of phosphorus element (Figure 6a,b, cross-section view) of the nanocomposite membrane containing 50 wt % of P(_bis_MEP). Figure 5: SEM images of the surface and cross-section of nanocomposite membranes. Figure 6: (**a**) SEM cross-sectional micrograph; (**b**) EDS map of phosphorus (P) element, and (**c**) EDS spectrum of nanocomposite membrane P(_bis_MEP)/BC_2.
## 2 Overview of the Issue
studied the effect of the grain orientation on of the hardness gradient in the FGM interlayer between CuCrZr cooling component and W coating deposited by plasma spraying in Ar-H2 atmosphere. The preferred grain orientation as a result of nucleation and growth processes during the impact of droplets and solidification give rise to the observed hardness gradients [67]. The contribution of [PERSON] et.al [69] is dedicated to such line of research as well, following a new approach. A thermomechanical engineered interlayer design, made with a functionally graded material, of a PFC is described in his work. The excellent mechanical properties of such solution are discussed and it is shown how the suggested solution can effectively reduce the thermal stresses due to the mismatch of the coefficient of thermal expansion between the armor and the heat sink material. As stated above, the structural components of the future fusion devices will need to withstand high neutron fluxes and have to show a low radiation activation. Considering neutron loads then, [PERSON] et al. [70] performed a detailed comparative neutronic analysis, regarding the nuclear load's spatial distributions on the Divertor PFCs targets of the EUDEMO1-2017 design. Authors have assessed the main nuclear loads in terms of radiation damage, nuclear heating density and spatial distribution, He production and comparison in different types of materials (W-monoblock, Cu/CuCrZr and PFC-CB in EUROFER97). Such important contribution confirms even more the importance of material sciences for the design and realization of the next generation of devices. Considering the low activation properties, the good weldability and mechanical performances in the range of temperature between 350 \({}^{\circ}\)C and 550 \({}^{\circ}\)C, the RAFM steels are widely considered as a candidate for nuclear fusion application [71; 72]. To extend the upper limit above 550 \({}^{\circ}\)C, EUROFER97 steel can be reinforced by dispersion of oxide (ODS). The improvement of the mechanical performances of EUROFER97 without compromising its ductility by the refinement of microstructure is under study [73; 74; 75; 76; 77; 78; 79]. [PERSON] et al. presented preliminary results of the microstructure and mechanical properties characterization of the cold-rolled EUROFER97 steel plates fabricated by cold rolling with different cold reduction ratios (CR) and heat treatment's temperatures. The effect of heat treatments on the mechanical proprieties of the samples deformed with the greater CR ratio (80%) is also shown. The EUROFER97 steel could be strengthened without its ductility compromising were evidenced by this work. To conclude, the most effective identification of the process parameters is under study [80].
## 4 Experiments
### Experimental Settings
#### 4.1.2 Evaluation Metrics  
To measure the performance of the ATKGRM, this section uses the values of _RMSE_, \(F1\), area under curve (_AUC_), and recall as evaluation indicators.  
Prediction error is measured by Root Mean Squared Error (_RMSE_). The formula for _RMSE_ is shown in (25).  
\[\textit{RMSE}=\sqrt{\frac{\sum_{i=1}^{S}(\mathbf{r_{i}}-\tilde{\mathbf{r}_{i}})^{2}}{S}} \tag{25}\]  
_Recall_ is the ratio of the intersection of the item set recommended by the model for the user and the item set that the user actually likes; it indicates the retrieval rate of the recommended model. The larger the recall value is, the higher the retrieval rate of the recommendation model. The formula for _Recall_ is shown in (26).  
\[\textit{Recall}=\frac{\sum_{\textit{net}}\lvert\textit{Rec}(\mathbf{u})\cap\textit {Fav}(\mathbf{u})\rvert}{\sum_{\textit{net}}\lvert\textit{Fav}(\mathbf{u})\rvert} \tag{26}\]  
where \(\textit{Rec}(\mathbf{u})\) is the candidate item set for user \(\mathbf{u}\), and \(\textit{Fav}(\mathbf{u})\) is the set of items that user \(\mathbf{u}\) actually likes.  
The \(\mathbf{F1}\) value is shown in Formula (27).  
\[\textit{F1}=\frac{2\times\textit{Precision}\times\textit{Recall}}{\textit{ Precision}+\textit{Recall}} \tag{27}\]  
where precision and recall are the accuracy rate and the recall rate, respectively. The \(\mathbf{F1}\) value comprehensively considers the two indicators of _precision_ and _recall_.  
The _AUC_ is used to evaluate the performance of recommender systems to distinguish between user likes and dislikes. Each time the recommendation system compares the score of the item that the user likes and the item that the user does not like, \(\mathbf{m}\) is the total number of comparisons. \(\mathbf{m^{\prime}}\) is the number of times that the user's scores for their favorite items are greater than the user's scores for their disliked items, and \(\mathbf{m^{\prime\prime}}\) is the number of times that the user's scores for their favorite items are equal to the user's scores for their disliked items. The calculation of _AUC_ is shown in Formula (28).  
\[\textit{AUC}=\frac{\mathbf{m^{\prime}}+\mathbf{0.5}\mathbf{m^{\prime\prime}}}{m} \tag{28}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Users** & **Items** & **Interactions** & **Relations** & **Triplets** \\ \hline MovieLens-1M & 6036 & 2347 & 753,772 & 12 & 20,195 \\ MovieLens-10M & 67,788 & 8704 & 9,945,875 & – & – \\ MovieLens-20M & 138,159 & 16,954 & 13,501,622 & 32 & 499,474 \\ Book-Crossing & 19,676 & 20,003 & 172,576 & 18 & 60,787 \\ Last. FM & 1872 & 3846 & 42,346 & 60 & 15,518 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of the datasets.
## 4 Current Status and Identified Needs
### Level 2 Bio-Geophysical Land Products
#### 4.3.5 Ground-Based Observations for Validation (GBOV)  
The GBOV [53] project is currently leveraging the data collected from this ensemble of existing ground based networks to populate a multi-years database of high quality in-situ measurements to validate seven core land products, which are generated in the frame of CGLS [8], namely: Top-of-canopy reflectances, Land surface albedo, fAPAR, LAI, fCover, LST and Soil Moisture. As part of GBOV, an initial effort was undertaken to improve interoperability across these networks by compiling and maintaining a centralised repository of Cal/Val data using a common format definition and quality metadata approach. Furthermore, in order to facilitate the practical use of GBOV database, common upscaling procedures were implemented to provide reference values representative of areas large enough to cover several pixels of typical coarse resolution satellite imagers.  
In the frame of GBOV, a detailed review of the state-of-the-art was performed to identify gaps and needs for the validation of the considered geophysical variables. The most obvious drawback at the moment is the geographical data gap over Africa, South America and Asia, currently preventing a consistent validation of CGLS vegetation products at global scale [3]. Within GBOV, a dedicated task is on-going to start filling these data gaps, notably with the set-up of six new Cal/Val sites. On the other hand, it is clear that an international effort will be required in the coming years involving all Space Agencies in coordination with CEOS-LPV sub-group to enhance geographical coverage and representativeness of ground-based measurements for land products validation.
# Dynamic Low-Emission Zones for Urban Mobility: A Systematic Review
## 4 Results
### Pricing Strategies  
Pricing strategies provide economic incentives and regulatory frameworks that influence driver behavior and environmental outcomes. Thus, they may be fundamental to the design and effectiveness of dynamic LEZs; applying dynamic pricing, which adjusts tolls based on real-time traffic, emissions, and vehicle type; and ensuring flexibility and responsiveness to urban mobility needs. However, challenges arise in balancing fairness, social acceptance, and enforcement, as excessive fees may disproportionately impact lower-income drivers while insufficient pricing may fail to reduce congestion.
# Meat 4.0: Principles and Applications of Industry 4.0 Technologies in the Meat Industry
## 4 Industry 4.0 Technologies Applied for Meat Quality, Safety, and Authenticity
### Application of Industry 4.0 Technologies for Meat Safety
In the case of Tellspec Inc., it has launched a scanner based on reflective near-infrared (NIR) spectroscopy which reveals allergenic substances in food by using a low-power laser that analyses the reflected light waves with the help of a unique cloud-based algorithm and a simple smartphone app [117]. For their part, the company Nima Lab created a portable sensor that allows the amount of gluten in a food to be simply quantified. Concretely, this portable sensor was developed by adapting antibody-based chemistry employed for allergen detection and consists of a scanner, a capsule where the foodstuff is introduced and an application where the results are displayed and collected [42]. Another important reason for issues related to food safety is the incomplete, opaque, and asymmetric information that reaches the consumer about certain food products. To solve these problems, it is essential to establish a reasonable and reliable traceability system that guarantees food safety, the integrity of information, and restores trust between consumers and the market, in addition to optimizing the structure data storage. In this area, systems based on IoT, blockchain, and BD provide ideas and methods to solve these problems that are so present in traditional traceability systems. Specifically, the application of IoT technology could efficiently control the appearance of incidents related to meat safety, since it permits incidents and sources of danger to be identified in real time and more precisely. This is possible because the IoT platform interacts with the existing smart physical objects in the production chain and allows the status and any information associated with said smart devices to be consulted, being able to act accordingly [75]. For its part, the use of BD helps to guarantee the authenticity of the data of the food companies [76].
## 3 Results  
### Regression Models (Phase A)  
Residuals from the two models were found to be normal (\(p>0.05\)), independent (Durbin Watson = 1.47-1.73), and fulfilled the assumption of homoscedasticity and linearity. Furthermore, the two predictor variables (MPO and FI) significantly contributed to the goodness of the models (\(p<0.001\)), specifically MPO (\(\beta\geq 0.94\)), and no multicollinearity was found (VIF \(<1.07\)). Both the WAnT\({}_{15}\) (R\({}^{2}\) = 0.98, SEE = 12.9 W, F-value = 1042.5, \(p<0.001\)) and WAnT\({}_{20}\) (R\({}^{2}\) = 0.99, SEE = 9.4 W, F-value = 1963.2, \(p<0.001\)) regression models were a good fit to the 30 s MPO (Table 1).
# Path Planning of Mobile Robots with an Improved Grey Wolf Optimizer and Dynamic Window Approach
## 3 Improved Gray Wolf Optimization Algorithm
### Nonlinear Convergence Factor  
In the gray wolf optimization algorithm, the parameter \(A\) is the key factor that controls the balance between global and local search. When \(|A|>1\), the wolf pack searches in distant regions to enhance its global search capability, and when \(|A|<1\), the wolf pack gradually converges towards the prey and performs local search [34].  
From Formula (3), \(a\) is linearly reduced from 2 to 0 during the iteration process, and the value of \(A\) changes accordingly; this linear decay often leads to local optima in practical applications. In this paper, we introduce a nonlinear convergence factor based on the adjustment factor \(n\), which allows for changing the decay of \(a\) by adjusting the size of \(n\) based on practical application requirements.  
\[a=\begin{cases}-\frac{t^{2}}{n^{2}}+2,&t<\frac{2n^{2}}{t_{m}}\\ \frac{2(t-t_{m})^{2}}{t_{m}^{2}-2n^{2}},&\frac{2n^{2}}{t_{m}}\leq t\leq t_{m} \end{cases} \tag{13}\]  
where \(t_{m}\) is the maximum number of iterations, and \(n\) is in the range \((0,\;t_{m})\).  
Figure 1 shows the effect of different values of \(n\) on the decay of the convergence factor a when the number of iterations is 1000. The dashed horizontal line labeled \"baseline\" corresponds to \(a=1\), serving as a key reference point for the GWO. As discussed earlier, in GWO the parameter \(A\) is given by \(A=2 ar_{1}\), and the threshold \(|A|=1\) marks the transition between global and local search. Consequently, when \(a\) crosses 1, the search behavior shifts from primarily global to more local exploitation.  
As can be seen in Figure 1, smaller \(n\) values cause \(a\) to approach 1 more quickly, thereby triggering local search earlier and reducing the duration of the global search phase. Conversely, larger \(n\) values slow the convergence to 1, extending the global search phase.  
By selecting an appropriate \(n\), one can, thus, balance the algorithm's global and local exploration more effectively according to the specific optimization problem.