esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Several studies have proposed some of the above approaches, mainly transfer learning, to address the problem of student dropout prediction between different instances of the same course [26] or from one course to a different, related or unrelated course [15; 27], while others addressed the problem with different universities involved [16]. The novelty of the current paper is specifically that it evaluates and compares the performance of all the previously proposed machine learning approaches when there are several, different-sized entities involved. In this paper, we explore all the previously mentioned approaches (individual, centralized, transfer learning, and federated learning) applied to the SDP problem. To our knowledge, this is the first study to compare all these approaches together. We present experimental results that highlight the strengths and limitations of each approach based on the results from applying the aforementioned collaborative approaches to datasets from three different-sized educational entities. Ultimately, our goal is to provide an initial understanding of how all these approaches can influence the creation of accurate predictive models in real-world situations with several different-sized entities. The main contribution of this paper is the proposal of a series of findings to guide practitioners and the research community toward informed decision-making when addressing predictive modeling from different entities. The rest of this paper is organized as follows: In Section 2, we examine the main approaches for generating prediction models starting from different entities. In Section 3, we present the materials and methods, including the datasets used and the neural network architecture we used. In Section 4, we present the results of our experiment findings and in Section 5, we discuss these results and explore practical implications arising from our discoveries. Finally, in Section 6, we conclude the paper by presenting our findings and discussing their significance for both EDM as a field and for student dropout prediction. Furthermore, we propose directions for future research that could further enhance our understanding in this domain.
## 2 The Novel Algorithms
### Directional Extension of Epidemic Routing  
Most data forwarding and routing protocols for opportunistic networks (oppnets) try to achieve a balance between message delivery ratio and resource consumption [16,17]. For the dissemination of EMs, which are very short in nature, a high delivery ratio and low message latencies are crucial. A fine example of opportunistic communication protocols is the epidemic routing [18], which disseminates multiple copies of a message over the network similar to the spread of an infectious disease used in previous work [19]. The main idea behind the use of the Epidemic Routing protocol was that each EM is intended for all CNs in the system.  
In our case it would be beneficial to somehow limit the number of disseminated messages. CNs are not interested in all EMs, only the ones that can change the evacuation path. An EM can change the path due to two reasons; (a) it updates the weights in the graph (\(D_{i}\) will be modified in the cost function) or (b) it changes the previously described fire vector in Equation (1) (\(\phi_{i}\) will be modified in the cost function). Since the proposed path finding algorithm uses directions to select the evacuation path (and not the shortest path), and it guides the civilian through a safer path (not through the shortest one), therefore the effect of (a) is less important than (b).  
The determination of the fire vector in Equation (1) contains all the received messages in chronological order, thus a threshold (\(\alpha_{t}\)) can be determined whether a newly received message can significantly modify the resultant vector or not. This threshold depends on the location of the exit, the layout of the area and other factors. Therefore, an appropriate threshold should be given for each scenario individually. The older a message is, it represents less weight, so only messages created after the timestamp of the currently received latest message should be taken into account.  
The presented novel protocol uses the same transmission mechanism as the Epidemic Routing protocol, but only those messages will be circulated among the nodes, which (a) are created later than the receiver's latest message timestamp; and (b) which determine a new direction (if \(\vec{v}_{sum}*\vec{v}_{sum^{\prime}}<cos(\alpha_{t})\)) for the evacuation path.
## 2 Materials and Methods  
### Theory of SWAN Spectral Model  
SWAN is a spectral phase averaged wave model that integrates the action balance equation in time, in the geographical space and in the spectral space, which is defined by the relative frequency (\(\sigma\)) and the wave direction (\(\theta\)) [29; 30]:  
\[\frac{\partial N}{\partial t}+\
abla(N)+\frac{\partial}{\partial\sigma}\dot{ \sigma}N+\frac{\partial}{\partial\theta}\dot{\theta}N=\frac{S}{\sigma}, \tag{1}\]  
The wave action (\(N\)) is considered in the above equation since in the presence of the currents the action density is conserved while the energy spectrum is not. The wave action is equal to the energy density (\(E\)) divided by the relative frequency. For larger scale, the geographical space is represented in spherical coordinates, longitude (\(\lambda\)) and latitude (\(\varphi\)), and the operator (\(\
abla\)) has the expression:  
\[\
abla_{\text{Sph}}(N)=\frac{\partial}{\partial\lambda}\dot{\lambda}N+\frac{1 }{\cos\varphi}\frac{\partial}{\partial\varphi}\dot{\varphi}N, \tag{2}\]For coastal applications the Cartesian coordinates (\(x\)) and (\(y\)) are mostly used and the operator (\(\
abla\)) becomes in this case:  
\[\
abla_{\text{Cart}}(N)=\frac{\partial}{\partial x}\dot{x}N+\frac{\partial}{ \partial y}\dot{y}N, \tag{3}\]  
The left side of the governing Equation (1) represents the kinematic part, which indicates the propagation of the wave action in time, geographical and spectral spaces considering also the effect of some relevant phenomena as wave diffraction or refraction. On the right hand side is the source (\(S\)) expressed in terms of energy density. In deep water, three components are more relevant, corresponding to the atmospheric input (\(S_{in}\)), nonlinear quadruplet interactions (\(S_{nl}\)) and whitecapping dissipation (\(S_{diss}\)). In intermediate and shallow water some additional terms, corresponding the finite depth effects (\(S_{fd}\)) and including phenomena as bottom friction, depth-induced wave-breaking or triad nonlinear wave--wave interactions, may become significant and in this case the total source becomes:  
\[S=S_{in}+S_{nl}+S_{diss}+S_{fd} \tag{4}\]  
As regards the wave power components (expressed in W/m, i.e., energy transport per unit length of wave front), they are computed in the spectral wave models, with the relationships [31]:  
\[P_{x}=\rho S\iint c_{x}\ E(\sigma,\theta)d\sigma d\theta \tag{5}\]  
\[P_{y}=\rho S\iint c_{y}\ E(\sigma,\theta)d\sigma d\theta\]  
In the above equation \(x\), \(y\) are the problem coordinate system and \(c_{x}\), \(c_{y}\) are the propagation velocities of the wave energy in the geographical space (absolute group velocity components) defined as:  
\[c_{x}=\frac{dx}{dt},\ c_{y}=\frac{dy}{dt} \tag{6}\]  
Thus, the absolute value of the energy transport (denoted also as wave power) will be:  
\[P=\sqrt{p_{x}^{2}+p_{y}^{2}}, \tag{7}\]
# Artificial Intelligence of Things for Solar Energy Monitoring and Control
## 3 AIoT Applications in PV Systems: A Comprehensive Review
### Applications of AI in PV Systems
#### 3.1.3 Energy Forecasting and Optimization
\begin{table}
\begin{tabular}{p{113.8 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline
**Study** & **Approach** & **Forecasting Horizon** & **AI Technique** & **Data Inputs** & **Key Contributions** & **Limitations** \\ \hline [PERSON] and [PERSON] [112] & A hybrid method for 24 h ahead PV energy forecasting. & Day-ahead & GRNN, FFNN, MLR & Meteorological data (temperature, irradiance, wind) & RMSE range: 2.65-3.22; (SR-FFNN), up to 6.7\% (MLR). & Model simplicity may limit \\ \hline [PERSON] et al. [113] & Multi-horizon solar power forecasting & Multi-horizon (15 min-7 days) & LSTM & Historical PV power output, meteorological data & Enhanced grid stability and reduced reliance on non-renewable energy sources; 95\% Confidence intervals. & Computationally intensive for multi-horizon scenarios. \\ \hline [PERSON] et al. [114] & Classification of weather changes & Short-term (5 min) & ANN with Fuzzy Logic & Cloud cover, temperature, wind speed, solar irradiance & MAPE improvement: Up to 52.48\% on specific days; low-data capability. & Relies on high-quality weather data for meaningful classification. \\ \hline [PERSON] et al. [115] & Weather data augmentation and solar forecasting & Day-ahead & GAN + CNN & Weather data (temperature, irradiance, cloud patterns) & Overall Accuracy up to 76.9\%; Data augmentation gain: accuracy increased 4.2–21.3\% across models. & Computational cost due to GAN and CNN training processes. \\ \hline [PERSON] \& & Hybrid model for day-ahead PV forecasting & Day-ahead & RBFNN + Grey Theory & Meteorological data (temperature, radiation, cloud cover) & MAPE = 3.71\%; RMSE = 4.65\%; More accurate computational efficient than all baseline models. & Preprocessing steps may limit real-time applicability. \\ \hline [PERSON] et al. [117] & Real-time solar energy management & Multi-horizon & HCISEM-GAN & Meteorological data (irradiance, temperature, cloud cover) & Quicker decision-making; 96\% user response accuracy; 98\% anticipation efficiency; 97\% Implementation Success. & Relies heavily on GAN training quality and user input for adjustments. \\ \hline [PERSON] et al. [118] & Short-term & Short-term & DRNN + LSTM & Aggregated residential PV output, load data, & Enabled energy dispatch with ESS and EV & Limited to microgrid setups with specific scheduling, reducing peak loads by 8.97\%; MAPE = 7.43\%. & Limited to microgrid setups with specific configurations. \\ \hline \end{tabular}
\end{table} 
Table 7: _Cont._
## 4 Model Construction and Simulation Experiment Results
### Model Construction
The BP neural network algorithm also belongs to a single hidden layer feed-forward neural network algorithm. The weight of the algorithm is utilized to judge the experimental accuracy of the entire algorithm [25]. However, its diagnostic effect on the faults of the marine fuel system is far worse than that of SaDE-ELM. The BP neural network algorithm and the conventional single hidden layer feedback neural network algorithm in the actual application process algorithm is easy to fall into a local minimum, resulting in the poor generalization of the algorithm. Combining the DE algorithm with ELM algorithm solves the limitations above of the single hidden layer feedback neural network algorithm. At the same time, it obtains the parameters of the DE algorithm through an adaptive approach and also addresses the problem that the algorithm itself sets rely on empirical parameters. Therefore, it is illustrated that the method proposed in this paper can better solve the limitations of the existing research lacking the high accuracy of fault diagnosis method for the marine fuel system, and also solve the problem of the low initialization precision of the ELM algorithm and improve the accuracy of the algorithm. The accuracy of the SVM algorithm of fault diagnosis for marine fuel system is worse than the method proposed in this paper because the SVM algorithm is more difficult to construct the hyperplane to distinguish different types of faults in the face of more types of faults. So the accuracy is lower.
## 3 Results
### General Scenario  
In some cases, it may happen that not selling the item in some locations may increase revenue. As an example, let us consider two consumers with \(v_{1}=10,\ v_{2}=4\), and \(\delta_{12}=\delta_{21}=1\). Thus, \(\mathbf{p}=(5,4)\) is the optimal clearing envy-free pricing with revenue 9, but \(\mathbf{p}^{\prime}=(10,10)\) is an envy-free pricing with revenue 10, where consumer 2 does not buy the item.  
It is clear that Proposition 1 also holds in the general scenario (since higher valuations produce higher prices), with the following modification:  
**Proposition 2**.: _Every buyer makes the purchase in their own location._  
Instead of using brute force to check all of sub-allocation combinations of the allocation defined in Section 3.1.2, it is easy to see that we just need to look at allocations to higher-valued consumers. Thus, if \(v_{1}\geq v_{2}\geq\ldots\geq v_{n}\), we can compute the prices and revenue of the leading principal submatrices of the valuation matrix \(V\), and then we choose the allocation and prices with the highest revenue. The procedure is described in the following Algorithm 2.  
```
1: Perform a permutation \(\sigma\) such that \(v_{\sigma^{-1}(1)}\geq v_{\sigma^{-1}(2)}\geq\ldots\geq v_{\sigma^{-1}(n)}\)
2: Use Algorithm 1 to compute the prices in the \(k\)-by-\(k\) matrix of consumers \(\sigma^{-1}(1),\ldots,\sigma^{-1}(k)\) for all \(1\leq k\leq n\). If \(k<n\), \(p_{i}\gets v_{\sigma^{-1}(1)}\) for all \(k<i\leq n\)
3: Choose the allocation and prices that generate the maximum revenue
```  
**Algorithm 2** Find an optimal envy-free pricing.  
This second algorithm allows us to state the following theorem.  
**Theorem 2**.: _The UDEFP with metric substitutability can be solved optimally in \(O(n^{3})\) time._  
Proof.: It is easy to see that the highest computational cost is in Line 2. This line performs \(n\) times \(O(n^{2})\) operations with Theorem 1, from which the result follows.
# Management of Scalp Biofilm in Local Dermatosis: From In Vivo Visualization to Optimal Treatments
###### Abstract  
The fungi and bacteria on the human scalp play important roles in both health and disease. Scalp biofilms have pathogenic effects on cutaneous tissues, such as seborrheic dermatitis. However, investigations into scalp biofilms and their physiological effects on scalp skin are limited. In this study, we suggest an evaluation method through which the scalp is stained a reddish color using erythrosine to visualize scalp biofilms, which strongly depends on the presence of bacteria and fungi. We found that the physiological properties of the scalp significantly differed between high and low levels of stained red areas (sRNAs) on the scalp. The sRA levels showed a strong positive correlation with IL-8 levels and sebum production. It is worth noting that the production of sebum has a dominant effect on the scalp microbiome via the growth of microbes, leading to the formation of a biofilm, as evidenced by changes in the sRA levels. Furthermore, the sRA levels could be reduced through the use of antimicrobial agents, such as climbazole and hexamidine distibionate (HD), as well as the manipulation of the physical properties of the scrubs used in scalp care products. These scalp care products could potentially disrupt the formation and accumulation of a biofilm on the scalp, providing strong evidence for the importance of considering the scalp microbiome and its interactions with sebum in the development of biofilms. Consequently, we suggested that the administration of anti-microbial agents, such as climbazole and HD, could be an effective strategy to alleviate biofilm accumulation for the maintenance of scalp health.  
scalp biofilm; antimicrobial agent; Cutibacterium acnes; Malassezia
## 5 Operation of the Switch
### Switch Algorithm
Thus, a fixed PS ratio is implemented for the switch, and the initialization of the ratio is to be performed by the user based on the specific environment under which it is operated. For the scope of this paper, we consider that PS ratio optimization mechanisms available in the present literature can be directly adapted [30; 31; 32]. The PS ratio is to be selected at the initialization stage of the system and though this parameter is not required by the algorithm (which is implemented to determine between ABCS and SWIPT based on the received signal conditions and expected performance), it is required by the switch under operation based on the SWIPT mechanism. The algorithm operates by utilizing physical layer parameters, specifically the received signal power and CSI, to determine the optimal transmission mode. During the relay-to-destination (\(R\) to \(D\)) transmission phase, the system employs standard packet transmission protocols. The algorithm's sole function is mode selection between SWIPT and ABCS, while actual data transmission follows conventional IoT network protocols. The security and privacy of the switch and the associated transmission network is considered to be based on IoT network security features. Gateways between different nodes can be provided with specific security features, and shielding practices such as end-to-end encryption algorithms can also be considered [36]. However, this may compromise the computational complexity and the overall energy requirement of the switch, which would therefore require a careful consideration of the ideal data security and privacy enhancement technique adapted in the switch.
# Analytical Model for Enhancing the Adoptability of Continuous Descent Approach at Airports
## 5 The Applied Queueing Model
### Probability of Aircraft Blocking  
In a queueing system of finite capacity, the probability of \"blocking\" is the probability that an arriving customer arriving at the queue finds it full and thus exits this system. In our context where aircrafts are the customers, the interpretation of the probability of blocking depends on whether CDA or SDA is assumed for all aircrafts during the brief time period being analyzed. If CDA is assumed for the time period, then a full queue suggests that CDA may not be used, and thus, SDA will be used instead. If SDA is assumed for the time period, then a full queue suggests that the aircraft will enter a holding pattern until the queue has available space to accommodate it. The probability of blocking is the percentage of time an aircraft's request to embark on CDA (or SDA) is denied principally due to safety and because the stacking space within the TMA is busy and congested. This probability is denoted by \(P_{k}\) and could be specified for an airport and its TMA to define a threshold beyond which CDA is unsafe to implement, or in the case of SDA, it is the probability that an aircraft will need to enter a holding pattern. Since the approach operations would be limited to the two profiles, namely CDA and SDA, then \(P_{k}\) should be determined for these two approach profiles. Theoretically, \(P_{k}\) is expressed based on the \(M\)/\(M\)/\(I\)/\(k\) queuing model, in which the arrival process is Poisson with rate \(\lambda_{\text{s}}\) and the service process is Poisson with rate \(\mu_{\text{s}}\), a single server (that is, the stacking space), and finite system capacity at \(k\) aircraft, as follows:  
\[P_{k}=\frac{1-\rho_{ss}}{1-\rho_{ss}{}^{k+1}}\rho_{ss}{}^{k} \tag{22}\]  
Because the Markov (Poisson) process distribution has high variability and is assumed for both arrivals and service, the resulting probability is likely to be higher than the value experienced in practice, and thus, the Markov assumption may be viewed as conservative.
###### Abstract  
The digital revolution, characterized by rapid technological advancements, presents a unique opportunity to accelerate progress towards the United Nations' Sustainable Development Goals (SDGs). This research explores the transformative potential of cutting-edge digital technologies--including artificial intelligence, big data analytics, cloud computing, and the Internet of Things--in fostering sustainable development across economic, social, and environmental dimensions. Our study employs a rigorous empirical approach to quantify the impact of digital innovation on SDG achievement within the European Union. Utilizing the Digital Economy and Society Index (DESI) as a comprehensive measure of technological progress, we apply structural equation modeling to emphasize the complex interplay between digital advancement and sustainable development indicators. A key focus of our analysis is the mediating role of economic performance, measured by GDP per capita, in the relationship between digital technology adoption and SDG progress. This nuanced examination provides insights into how economic factors influence the effectiveness of digital solutions in addressing global challenges. Our findings underscore the need for adaptive policies that harness the power of digital technologies while addressing potential challenges and ensuring inclusive growth.  
emerging digital technologies; sustainable development goals; economic performance; European Union; structural equation modeling 2020 IEEEexample: Digital  
risks such as workforce displacement, the need for large-scale reskilling, and the potential exacerbation of unsustainable consumption patterns [7]. Countries that effectively navigate these challenges through adaptive policies and education initiatives tend to make more remarkable strides in SDG achievement [8; 9].  
This research aims to explore the impact of digital technologies on SDG progress in EU countries, using the Digital Economy and Society Index (DESI) as a measure of technological advancement. We also investigate how economic performance, measured by GDP per capita, mediates this relationship. The study uses structural equation modeling to address two key questions. How do digital technologies, measured by DESI, influence SDG progress in EU countries? To what extent does GDP per capita mediate the relationship between digital technologies and SDG achievement? This paper aims to fill critical research gaps in understanding emerging technologies' collective impact on SDGs and economic performance's mediating role. The findings provide a more nuanced understanding of leveraging technological innovations to create a more sustainable and equitable future.  
The paper is structured to provide a comprehensive exploration of this topic, beginning with an introduction to the SDGs and digital technologies, followed by a literature review and hypothesis development, methodology explanation, presentation of results, discussion of findings, and concluding remarks.
# Robust Image Watermarking Algorithm Based on ASIFT against Geometric Attacks
## 5 Experimental Results and Discussion
### Performance Evaluation  
The performance of the watermarking algorithm can be evaluated in aspects of imperceptibility and robustness mainly by two indexes: peak signal-to-noise ratio (PSNR) and normalized correlation  
Figure 8: Test images: (**a**) Airplane; (**b**) Elaine; (**c**) Lena; (**d**) Mountain; (**e**) Bank; (**f**) Peppers; (**g**) Milk drop; (**h**) Gold hill.  
Figure 7: Flowchart of watermark embedding and extraction: (**a**) watermark embedding; (**b**) watermark extraction.  
(NC) coefficients. Generally, a larger PSNR value represents a better visual quality and imperceptibility, and watermarked image whose PSNR is greater than 35 dB can be acceptable. The definition of PSNR for the image with size of \(M\times N\) can be  
\[\text{PSNR}=10\text{lg}\frac{255^{2}}{\text{MSE}}=10\text{lg}\left[\frac{255^{ 2}MN}{\sum\limits_{i=0}^{M-1}\sum\limits_{j=0}^{N-1}\left[\mathbf{I}(i,j)-\mathbf{I}_{ \text{w}}(i,j)\right]^{2}}\right](\text{dB}), \tag{8}\]  
where \(\mathbf{I}(i,j)\) is the pixel coordinate in the original image, and \(\mathbf{I}_{\text{w}}(i,j)\) is that in the watermarked image. MSE is the short form for mean square error.  
The robustness of the algorithm can be determined by NC with the value range from 0 to 1, which evaluates the similarity between original watermark and extracted watermark. Similarly, the NC should be 1, but the value greater than 0.7 is acceptable.  
\[\text{NC}=\frac{\sum\limits_{i=1}^{m}\sum\limits_{j=1}^{n}\mathbf{W}(i,j)\times\bm {W}_{\text{e}}(i,j)}{\sum\limits_{i=1}^{m}\sum\limits_{j=1}^{n}\mathbf{W}(i,j) \times\mathbf{W}(i,j)}, \tag{9}\]  
where \(\mathbf{W}(i,j)\) and \(\mathbf{W}_{\text{e}}(i,j)\) are original watermark and the extracted watermark, respectively. The size of the watermark is \(m\times n\).