esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Flexibility of Distribution Network with DG
#### 2.2.2 Constraints for the Multi-Objective Optimization Model
The sum of the optimal connected capacity of DG is the maximum accommodation capacity of the DG of the distribution network. Steps for Optimizing the Accommodation Capacity of DG Based on the CPLSO Algorithm and Mixed Strategy Nash Equilibrium  
Figure 2 shows the flow chat of the proposed optimization model. To optimize the accommodation capacity of the DG, the Pareto frontier solution set is obtained by CLPSO algorithm first in this paper. Then, the Pareto frontier solution set (i.e., the DG connection solutions) is stored in the external archives. At last, the optimal solution is found from the external archives through the mixed strategy Nash equilibrium. The optimal solution is the final optimization result of the maximum accommodation capacity of the DG of the distribution network. The process for optimizing the accommodation capacity of the DG based on the CPLSO algorithm and mixed strategy Nash equilibrium is as follows:  
* The Pareto frontier solution set (i.e., the DG connection solutions obtained by CLPSO algorithm) is stored in external archives. * The values of three optimization objective functions are normalized (i.e., maximizing daily power supply of DG, minimizing voltage deviation, and maximizing line capacity margin). * The weight of each object is solved using the entropy weight method. * The multi-objective, non-cooperative equilibrium decision-making model is solved according to Equations (14) and (15), and the optimal compromise solution is obtained. Figure 2: Flow chart of multi-objective optimization of accommodation capacity on distributed generation.
## 3 Experiments and Discussions
### Preparations
#### 3.1.2 The Filter and Parameters  
In FMDR, the primary concept revolves around describing the relationships between samples by leveraging the advantage of separating features and noise in the frequency domain. The selection and design of filters offer considerable flexibility, allowing for customization based on specific applications and research objectives. The Butterworth filter is selected in this study mainly because of its suitability for extracting morphological features from facial images. Its adjustable cut-off frequency and order parameters allow for tailored adjustments, making it particularly suited for this classification task.  
In practice, the distribution of valuable information in facial images varies across different datasets. There is no universally applicable filter design that can be suitable for all situations. Based on the experimental experience, the features of high-frequency texture components are relevant to the parameters of the filter. Specifically, the quantity of features is related to the cut-off frequency and the texture thickness is dependent on the order of the filter. When parameters are small, most of the useless information and interference are retained. As parameters increase, more low-frequency interference is filtered out and the texture components become more obvious. However, when parameters are set to be too large, most of the information is abandoned. Therefore, in practical applications, for optimal recognition performance, if there is significant interference in the image, it is advisable to increase the parameters appropriately to filter out more noise. Conversely, reducing the parameters would be preferable to retain more information. The different results extracted by different parameters setting are presented in Figure 4.  
Samples from diverse datasets contain noise with different distributions and structures. In order to achieve the best clustering performance, the tuning of the filter's parameters should be comprehensively considered in practice.  
Figure 4: Subfigure (**a**) presents the original image and the corresponding frequency spectrum. Subfigures (**b–d**) present the differences between the extracted texture components with the corresponding frequency response curve of the filter. With a bigger parameters setting, more low-frequency information is filtered out and the high-frequency texture component becomes more obvious. When the parameters are set to be too large, most of the information is abandoned, which is not sufficient for recognition. (**a**) The original image and the corresponding frequency spectrum. (**b**) Cut-off frequency is too small. (**c**) Appropriate setting. (**d**) Cut-off frequency is too large.
## 6 Discussion  
### Identifying Superfans: Classifying Heavy Users by Activity Types  
While traditional fan classifications from previous random studies have laid a valuable foundation, they are increasingly inadequate for understanding the complexities of digital random in the age of big data and real-time engagement. Today, fan activities leave behind a wealth of digital traces, offering more nuanced and multi-dimensional insights into fan behavior. To reflect this shift, a modern framework must go beyond one-dimensional thresholds and incorporate quantitative data that consider multi-activity engagement--particularly the content creation and interaction patterns that shape digital fandoms. Consequently, this study aims to develop a data-driven classification approach that better captures the reality of random in the digital age.  
In this study, we applied a k-means clustering approach to identify user groups and found that superfans on digital random platforms can be categorized into two distincttypes of heavy users based on posting and commenting behaviors. We also observed that a small subset of heavy users contributes a disproportionately large share of overall activity, aligning with the power-law distributions observed in previous research on Wikipedia and other platforms [20; 23; 51; 52]. Traditional approaches to identifying heavy users have generally relied on threshold-based or percentile-based metrics, such as labeling the top 1% or 5% of users as superusers. For instance, [PERSON] et al. [52] defined the top 5% of Quora users by follower count as \"super users\" and later compared their voting and content contributions with those of general users. While effective, these methods often treat activity as a single dimension (for example, the number of edits or total votes).  
In our study, we moved beyond this by adopting a clustering-based method (\(k\)-means) to capture multiple dimensions of activity. Specifically, we distinguished between posting and commenting behaviors instead of relying on an arbitrary cutoff. This approach also addressed the complexities of multi-criteria thresholds, which would otherwise require separate cutoffs for each type of activity. By using two primary activity types, posts and comments, we successfully identified three distinct groups: general users, post-heavy users, and comment-heavy users. These clusters exhibited significant differences in both behavioral and linguistic characteristics.  
Our results suggest that treating posting and commenting simply as a single \"engagement score\" could mask meaningful subgroups. Instead, clustering illuminated how users predominantly gravitate toward one form of participation or another. This finding underscores the importance of considering the platform's structural features and user interaction patterns. It also complements prior work (e.g., [PERSON] et al. [24]), where different engagement tiers were identified based on activity level, but without explicitly separating posting and commenting.
# Novel Service Efficiency Evaluation and Management Model
## 1 Introduction
Through the radar chart, it can be obviously seen at a glance whether the point estimate of each indicator falls into the control block of the radar chart. When the point estimate of the indicator falls into the control block of the radar chart, it means that the point estimate of the indicator is smaller than the MV, showing that the service operation efficiency of the workstation has not reached the required level, so it needs to be improved. In contrast, when the point estimates of all indicators do not fall into the radar chart control block, it means that the point estimates of all indicators are larger than the MV, demonstrating that the performance of the multi-workstation service operation process has reached the required level. As noted above, the advantages of the novel service efficiency evaluation and management model include: (1) the method has a simple and easy-to-use point estimate which can be maintained, (2) this model can evaluate the performance of the multi-workstation service operation process as well as directly monitor whether the service operation efficiency of each workstation needs to be improved at the same time, (3) the risk of misjudgment caused by sampling errors can be reduced as well, (4) this model is helpful for the service industry to move towards the goal of intelligent innovation management, and (5) this method is not only applied to the performance evaluation of the multi-workstation service operation process but also applicable to the performance evaluations of other service operations. The other sections of this paper are organized as follows. In Section 2, we propose a multi-workstation service efficiency index and discuss its characteristics. In Section 3, we derive the upper confidence limit of the service efficiency index based on [PERSON]'s inequality and [PERSON]'s theorem. Subsequently, according to the upper confidence limit and the required value of the index, we deduce the MV of the index estimator. In Section 4, we employ a case study to construct a radar chart which evaluates the multi-workstation service operation efficiency and explain its application. Finally, we make conclusions in Section 5 and limitations and future research in Section 6.
## 5 Pre-Treatments for Anaerobic Digestion of Food Waste
### Mechanical and Physical Pre-Treatment  
Mechanical and physical pre-treatments are based on separation of large and unwanted materials from the OM, and reducing its particle size, as increased contact surface allows for a better contact and interaction between substrate and microorganisms [34; 45]. If particle size is sufficiently reduced, methane yield can be improved up to 40%. The most common procedures to reduce particle size are milling, screw press, disc screen, or shredder with magnet methods. As an example, researchers found an improvement a biogas yield improvement of 9-34% when grinding FW previously to its anaerobic treatment [45], an increase in biogas yield of 20% if substrate was commune [92] or a 94% increment in both, biogas and methane yield, if FW is previously grinded and sonicated [236; 237]. However, an excessive reduction of particle size can lead to a hydrolysis overloading and the subsequent VFA accumulation. This has been reported when treating milled FW [95], causing a collapse in the methanogenic reaction.  
Another mechanical pre-treatment gaining ground is sonication, consisting in disrupting cell structure and floc matrix of particulate matter by means of ultrasonic waves [250]. In this case, biogas enhancement is due to an improved solubilization [237] that can increase the methane yield up to 90% [236]. However, this pre-treatment is not yet feasible to use in-situ in big-scale plants.  
Other physical type of pre-treatment aims to increase surface area and pore size by disrupting biomass structure and increasing solubilization [235; 251]. This is the case of pressure-depressure pre-treatments that can increase biogas yield up to 35% [235] when substrate is first under pressure (10 bar), and finally depressurised, achieving the breakup of cell walls. Irradiation can also be considered a successful pre-treatment disrupting biomass cell structure and decreasing crystallinity of cellulosic components [252; 253].
## 3 Results and Discussion
### Numerical Results
#### 3.2.5 Influence of Interface Friction on Calibrated Elastic Parameters  
Following the assumptions of Section 2.3.4 and the observations made in Section 3.2.4, the influence of an arising friction at the interface between the sample and platens on the generated strain distributions at the central cross section for samples with \(\beta=0^{\circ}\) and \(90^{\circ}\), and thus on the calibration results for parameters \(E,E^{\prime},v\) and \(v^{\prime}\), is considered negligible.  
Table 5 shows the analytical and numerical values for the radial strain components \(\varepsilon_{x}\) in the \(x\)-direction as received at the central cross section for samples with \(\beta=45^{\circ}\), considering an interface friction of \(\varphi=15^{\circ}\) and the corresponding elastic parameter sets. Further, the back-calibrated values for the transverse shear modulus \(G^{\prime}\) from the numerical solutions and the percentage differences between the analytically and numerically derived strains \(\varepsilon_{x}\), as well as the back-calibrated and applied values for \(G^{\prime}\), are listed.  
The results indicate that, for moderately anisotropic rocks (\(E/E^{\prime}\)\(\sim\)2), the generated elastic radial strains in the \(x\)-direction under realistic UCS test conditions, i.e., with friction between samples and platens, are mainly influenced by the inherent transverse shear  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{6}{c}{**Elastic Parameter Sets**} & & & & & **Results** & \\ \hline \(E^{\prime}\) & \(v^{\prime}\) & \(E/E^{\prime}\) & \(G^{\prime}\) & \(v/v^{\prime}\) & \(\varepsilon_{x}\) (Equation (2)) & \(\varepsilon_{x}\) (Num.) & \(G^{\prime}\) & \(\Delta\varepsilon_{x}\) [\%] & \(\Delta G^{\prime}\) [\%] \\ \hline  
stiffness of the material and the degree of anisotropy of the Poisson's ratios (\(\
u/\
u^{\prime}\)). The largest differences between analytical and numerical strains exist for samples with high inherent shear moduli and low ratios \(\
u/\
u^{\prime}\). However, while the numerical strain values at a load level of 10 MPa, associated with simulated friction at the sample-platen contact, show deviations from the analytical solutions of up to 15.04 %, the effects on the back-calibrated values for the shear moduli are only in the range of maximum \(\sim\)3%. This deviation can be considered as practically irrelevant, and higher errors might be associated with material heterogeneity and the natural variability between multiple samples.
## 2 Theoretical Analysis
### Nonlinear Volterra Equalizer  
The principle of the NLVE is based on the theory of the Volterra series, which is an important tool for the analysis of nonlinear systems and provides a complete description of the channel nonlinearity [19; 20]. The realization can be done either in the frequency domain or entirely in the time domain, as will be shown here. A general discrete Volterra filter input-output relation is given by [4]  
\[y_{n}\ =\ \sum_{v\ =\ 0}^{N_{1}-1}e_{v}x_{n-v}+\sum_{v=0}^{N_{2}-1}\sum_{l=v} ^{N_{2}-1}e_{vj}x_{n-v}x_{n-l}+\sum_{v=0}^{N_{3}-1}\sum_{l=v}^{N_{3}-1}\sum_{m =0}^{N_{3}-1}e_{vJ,m}x_{n-v}x_{n-l}x_{n-m}^{*}, \tag{7}\]  
where \(x_{n}\) and \(y_{n}\) are the complex-valued filter input and output of the equalizer at the time index \(n\), \(N_{i}\) is the memory length of the \(i\)-th order and \(e_{v}\), \(e_{vJ}\), \(e_{vJ,m}\) are the equalizer coefficients. The first term of Equation (7) represents a linear filter, whereas the others are nonlinear.  
The coefficients can be estimated using the minimum mean square error (MMSE) criterion. The dimension of the model grows rapidly, as can be seen from the total number of coefficients given by  
\[N_{t}\ =\ N_{1}+N_{2}(N_{2}+1)/2+N_{3}^{2}(N_{3}+1)/2. \tag{8}\]  
According to Equation (7) we choose the notation NLVE[\(N_{1}\),\(N_{2}\),\(N_{3}\)] as full description of the Volterra filter. In this case \(N_{1}\)-\(N_{3}\) represents the memory length of the 1 st-3 rd order of the NLVE.
## 4 Terrain Matching Search Strategy Based on the F-WAPSO  
### Search Area  
In the process of TAN matching, the search area is determined according to the current position of INS. If the search area is too small, the real position of the carrier will be missed, which will almost certainly result in a matching failure. If the search area is too large, the calculation amount will increase and the matching speed will suffer. In this paper, the size of the search area is determined according to the position error ellipse in inertial navigation. It is assumed that the inertial navigation position error follows the standard normal distribution, where \(\sigma_{x}\) and \(\sigma_{y}\) are the east and north standard deviations of INS location system, respectively, and \(\sigma_{x}^{2}\), \(\sigma_{y}^{2}\), \(\sigma_{xy}\), and \(\sigma_{yx}\) are the variances and covariances of the positioning system. Hence, the INS error ellipse calculation formula can be written as shown in Equation (19):  
\[\left\{\begin{aligned} & a=\vartheta_{0}\sqrt{\frac{1}{2}(\sigma_{x}^{2}+ \sigma_{y}^{2}+\sqrt{\left(\sigma_{x}^{2}-\sigma_{y}^{2}\right)^{2}+4\sigma_{ xy}^{2}})}\\ & b=\vartheta_{0}\sqrt{\frac{1}{2}(\sigma_{x}^{2}+\sigma_{y}^{2}- \sqrt{\left(\sigma_{x}^{2}-\sigma_{y}^{2}\right)^{2}+4\sigma_{xy}^{2}})}\\ &\varphi=\frac{\tau}{2}-\frac{1}{2}\text{arctan}(2\sigma_{xy}/ (\sigma_{x}^{2}-\sigma_{y}^{2}))\end{aligned}\right. \tag{19}\]  
\begin{table}
\begin{tabular}{l l} \hline \hline
**F-WAPSO** \\ \hline
1. & set \(\tau_{\text{max}}\), number of particles, \(\omega\), \(c_{1}\), \(c_{2}\) \\
2. & randomly initialize the swarm \\
3. & calculate the fitness value of each particle \\
4. & \(\tau=1\) \\
5. & while \(\tau\leq\tau_{\text{max}}\) do \\
6. & for every particle \(i\) do \\
7. & update \(x_{i}\) and \(v_{i}\) according to Equations (5) and (6) \\
8. & update _pbest_ and _gbest_ \\
9. & for every particle \(i\) do \\
10. & perform ABC search according to Equations (15)–(18) \\
11. & update _pbest_ and _gbest_ \\
12. & end for \\
13. & \(\tau=\tau+1\) \\
14. & end while \\
15. & return _gbest_ \\ \hline \hline \end{tabular}
\end{table}
Table 2: The pseudocode of F-WAPSO.  
where \(a\) and \(b\) are the major and minor axes of the ellipse, respectively; \(\varphi\) is the angle between the major axis of the ellipse and due north; and \(\hat{\sigma}_{0}\) is the expansion factor. For the convenience of calculation, an enclosing rectangle of the error ellipse is used, and its formula is shown in Equation (20):  
\[\begin{cases}x_{m}=2\sqrt{a^{2}\sin^{2}\varphi+b^{2}\cos^{2}\varphi}\\ y_{m}=2\sqrt{a^{2}\cos^{2}\varphi+b^{2}\sin^{2}\varphi}\end{cases} \tag{20}\]  
where \(x_{m}\) and \(y_{m}\) are the length and width of the enclosing rectangle, respectively. According to the \"3\(\sigma\)\" principle, when \(\hat{\sigma}_{0}\) = 3.03, the confidence degree of the error ellipse can reach 95%. Considering various uncertainty errors in practical application, \(\hat{\sigma}_{0}\) = 4 was used in the simulation experiment of this paper.
# Artificial Intelligence in Ophthalmology: Advantages and Limits
## 6 Ethics and AI: Pandora's Box
### Explainable AI (XAI) in Ophthalmology  
The opacity of automatic diagnosis posed by ML or DL systems can be successfully addressed using so-called _explainable AI models_.  
A suggestive illustration of this concept is made in [127], which presents an explainable AI (XAI) study for the detection of _geographic atrophy_ by means of color retinal photographs. XAI provides automated geographic atrophy (GA) screening using easily accessible color retinal images. Geographic atrophy (GA) is an advanced stage of age-related macular degeneration and is an important concern for global healthcare, as it is a main cause of blindness. This condition affects approximately 5 million people, and predictions suggest that this number will reach 10 million cases by 2040. The major problem in addressing GA lies in the poor understanding of its etiology and pathogenesis.  
Due to its high performance and explainability, the model shown in [127] can aid the clinical validation of AI for the diagnosis of GA (Figure 8). Through the early detection of GA, this AI method can improve patient access to effective treatments, increasing the chance of preserving sight.  
The model outperforms previous methods based on retinal imaging. Class activation mapping techniques are used to _explain_ the visual decision-making process of the AI model, thereby increasing transparency.
* _Outcomes_: A total of 540 color retinal images were collected. The data were used as follows: 300 images trained the AI model, 120 for validation, and 120 for testing. To distinguish GA from healthy subjects' eyes, the model demonstrated 100% sensitivity, 97.5% specificity, and 98.4% overall diagnostic accuracy. Other performance values: AUC-ROC = 0.988 and precision-recall = AUC-PR = 0.952, which are very good model values. When distinguishing GA from other retinal diseases, the DL network retained a diagnostic accuracy of 96.8%, a precision of 90.9%, and a recall of 100%, resulting in an F1 score of 0.952. The AUC-ROC and AUC-PR were 0.975 and 0.909, respectively. (The F1 score is the harmonic mean of precision and recall.) _Conclusions_
* The explainable AI model provides automatic GA screening, with very good results, using easily accessible retinal imaging.
* Because of high performance and explainability, the above architecture can aid clinical validation and propose AI acceptance for GA diagnosis.
* Due to optimization, early detection of GA can occur, and this method with AI can increase patient access to innovative, possibly vision-preserving treatments.
## 4 Discussion  
This study begins to address the dearth of model evaluations for boreal managed grasslands by presenting a comparison of model simulations for DNDC with eddy-covariance GHG flux, soil climate and biomass dry-matter production data. There are relatively few papers containing model evaluations for grasslands in boreal areas, but since model testing during their creation is necessarily limited to regions available to the model creators, it does not follow that they are applicable elsewhere and a careful assessment of regional conditions is necessary to understand how a model might behave in novel environments.  
A number of papers have assessed the DNDC model in cool weather regions in Canada [29; 31] and Northern Europe [27; 43; 44], making it an ideal candidate for evaluation with a view to using in the boreal agricultural landscapes of Scandinavia. Furthermore, the present study adds to this by demonstrating that the model can produce fair estimations of the key GHG fluxes and good estimations of soil climate conditions when compared to measured field data, but that there remain a number of uncertainties that would benefit from further elucidation, for example crop parameterisation and root processes, soil moisture simulations and GHG responses to changes in management.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Calibration/Evaluation Years** & **Measured** & **Simulated** & **Difference** \\ \hline \hline \multicolumn{4}{c}{**GPP (T C ha-1 yr-1)**} \\ \hline
2018 & 14.79 & 10.09 & 4.70 \\
2019 & 4.15 & 5.19 & 1.04 \\ Mean & 9.47 & 7.64 & –1.83 \\ \hline \multicolumn{4}{c}{**NEE (T C ha-1 yr-1)**} \\ \hline
2018 & –3.64 & –1.97 & 1.68 \\
2019 & 0.22 & –0.35 & –0.57 \\ Mean & –1.71 & –1.17 & 0.55 \\ \hline \multicolumn{4}{c}{**Reco (T C ha-1 yr-1)**} \\ \hline
2018 & 11.15 & 8.12 & –3.03 \\
2019 & 4.58 & 4.84 & 0.26 \\ Mean & 7.87 & 6.48 & –1.38 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of total annual GHG exchanges for calibration (2018) and evaluation (2019) years. The 2017 year is omitted as measurement did not begin until May.  
Figure 4: Comparison of simulated () and measured (\({}_{\times}\)) biomass production (DM kg/ha-1) for growing seasons 2017 to 2019. For 2017, the difference for cut 1 was 5.6% and cut 2 was 21.3%. For 2018 cut 1 was 4.0% and cut 2 was 30.5%, and for 2019 the simulated cut was 8.7% higher than measured DM and the black arrows (‘ploughing event’ and ‘tillage and spring sowing’).