esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Analysis of the State of the Art
### Users  
#### 2.1.1 A Chronological Review of the Evolution of User Models  
The following figure illustrates the evolution and different solutions by chronological order for the last 15 years. During these years different user characteristics have been taken into account considering the final purpose of the designed system.  
First user modeling systems started in the late eighties. [PERSON] [12], [PERSON] and [PERSON] [13, 14] and [PERSON] [15, 16] are examples of researchers whose works inspired next user modeling approaches. From there several authors started collecting different types of information about the users and exhibiting, for example, different kinds of adaptations to them [17].  
However, first, what is user modeling? There are at least two different perspectives which might answer this question. One of these is related to the Artificial Intelligence research area, and it considers user modeling as the process through which systems gather information and knowledge about users and their individual characteristics. Therefore, a user model is considered a source of information about the user of the system which contains several assumptions about several relevant behavior or adaptation data. However, in this paper the Human-Computer Interaction research perspective is taken, which is defined by [PERSON] [18] as follows:  
**Definition 2**.: _It refers to an a-priori model of the users of a computer system that the system designer has in mind, or to the assumed models that users will probably develop of the system and the tasks they can perform using the system._  
Nevertheless, this definition and the AI perspective coincide in the idea that every system must use information about the user to be able to see and react to their different problems and needs and improve the system purpose. This section analyses the most significant user models in the past 15 years.
# Towards Efficiently Provisioning 5G Core Network Slice Based on Resource and Topology Attributes
## 4 Heuristic 5G Core Network Slice Provisioning Algorithm Design
### Node Ranking Based on Network Resource Attributes and Topology Attributes
#### 4.1.2 Global Resource Attributes  
Considering only the local resources of a node can cause load imbalance and resource fragmentation in the physical network. To address this, we take the minimum bandwidth of the links in the shortest path of the node to all other nodes and the minimum computing capacity of the nodes along the shortest path as the global resource metric. The following formula is its normalized definition.  
\[GR(v_{i})=\frac{\sum\limits_{i\
eq j}[b(p(v_{i},v_{j}))+c(p(v_{i},v_{j}))]}{|V |-1}, \tag{13}\]  
where \(b(p(v_{i},v_{j}))\) is the minimum bandwidth of the links and \(c(p(v_{i},v_{j}))\) is the minimum CPU of the nodes in the shortest path between \(v_{i}\) and \(v_{j}\).
## 2 Propagation Analysis of MGSM Array Beams  
### Analytical Expressions of MGSM Array Beams  
Recalling the descriptions of MGSM beams, the spectral degree of coherence of the MGSM source takes the form as follows [49]:  
\[\mu(\mathbf{r}_{10},\mathbf{r}_{20})=\frac{1}{C_{0}}\sum\limits_{m=1}^{M}\left( \begin{array}{c}M\\ m\end{array}\right)\frac{(-1)^{m-1}}{m}\exp\left[-\frac{(x_{10}-x_{20})^{2}}{2 mo^{2}}-\frac{(y_{10}-y_{20})^{2}}{2 mo^{2}}\right], \tag{1}\]  
with  
\[C_{0}=\sum\limits_{m=1}^{M}\frac{(-1)^{m-1}}{m}\left(\begin{array}{c}M\\ m\end{array}\right), \tag{2}\]  
where \(C_{0}\) represents normalization coefficient, \(\sigma\) is the initial correlation coherence length, \(M\) is total number of terms of MGSM source, and \(\mathbf{r}_{0}=(x_{0},y_{0})\) is the position vector.  
Considering the diagram of a \(N\times D\) array beam in rectangular symmetry, the cross-spectral density (CSD) of a rectangular \(N\times D\) MGSM array beam generated by a MGSM source is described by the following:  
\[\begin{array}{c}W_{ND}(\mathbf{r}_{10},\mathbf{r}_{20},0)=\frac{1}{C_{0}} \sum\limits_{m=1}^{M}\left(\begin{array}{c}M\\ m\end{array}\right)\frac{(-1)^{m-1}}{m}\sum\limits_{m=1}^{-\frac{N-1}{2}}-\frac {N-1}{2}\sum\limits_{m=1}^{\frac{D-1}{2}}\frac{D-1}{2}\\ \exp\left[-\frac{(x_{10}-x_{1}X_{0})^{2}+(x_{20}-n_{2}X_{0})^{2}}{w_{0}^{2}} \right]\exp\left[-\frac{(y_{10}-d_{1}Y_{0})^{2}+(y_{20}-d_{2}Y_{0})^{2}}{w_{0}^ {2}}\right]\\ \exp\left[-\frac{(x_{10}-x_{20})^{2}}{2 mo^{2}}-\frac{(y_{10}-y_{20})}{2 mo^{2} }\right]\end{array}, \tag{3}\]  
where \(N\) and \(D\) are odd numbers and represent the orders of individual off-axis MGSM beams along the \(x\)- and \(y\)-axes, \(X_{0}\) and \(Y_{0}\) denote separations distances along the \(x\)- and \(y\)-axes, and \(w_{0}\) represents the beam waist. The contour graphs of \(N\times D\) MGSM array beams in source plane \(z=0\) are plotted in Figure 1, which shows that the distance between the different beamlets will increase as the off-axis parameters \(X_{0}\) and \(Y_{0}\) increase.
## 4 Discussion
### A Need for Dense High-Resolution Forest Monitoring, and Future Research Trends
Whether tall or short trees and natural or planted forests are more affected by drought depends on different site conditions, and does not follow a general rule. The increasing challenges posed by climate change require rapidly adaptable forest management strategies which take into account forest disturbance interactions at the landscape scale in order to maintain resilience, resistance, enhance biodiversity, and enable sustainability. Many scientific findings from remotely sensed forest formations are already being used for forest management strategies [58; 149], which, in the case of satellite-based observations, can offer the possibility of near real-time monitoring [59; 103]. The accelerating development of methods, especially in the field of artificial intelligence, and the increasing availability of high-resolution spaceborne data will noticeably influence the trends of future research in satellite-based forest science, enabling continuous detailed forest monitoring.
# Investigating the Impact of Wildfires on Lake Water Quality Using Earth Observation Satellites
###### Abstract  
A study was carried out to investigate the effects of wildfires on lake water quality using a source dataset of 2024 lakes worldwide, covering different lake types and ecological settings. Satellite-derived datasets (Lakes_cci and Fire_cci) were used and a Source Pathway Receptor approach applied which was conceptually represented by fires (burned area) as a source, precipitation/drought representing transport dynamics, and lakes as the ultimate receptor. This identified 106 lakes worldwide that are likely prone to be impacted by wildfires via a terrestrial pathway. Satellite-derived chlorophyll-a (Chl-a) and turbidity variables were used as indicators to detect changes in lake water quality potentially induced by wildfires over a four-year period. The lakes with the largest catchment areas burned and characterized by regular annual fires were located in Africa. Evidence for a strong influence of wildfires was not found across the dataset examined, although clearer responses were seen for some individual lakes. However, among the hydro-morphological characteristics examined, lake depth was found to be significant in determining Chl-a concentration peaks which were higher in shallow and lower in deep lakes. Lake turbidity responses indicated a dependence on lake catchment and weather conditions. While wildfires are likely to contribute to the nutrient load of lakes as found in previous studies, it is possible that in many cases it is not a dominant pressure and that its manifestation as a signal in lake Chl-a or turbidity values depends to a large part on lake typology and catchment characteristics. Assessment of lake water quality changes six months after a fire showed that Chl-a concentrations either increased, decreased, or showed no changes in a similar number of lakes, indicating that a lake specific ecological and hydro-morphological context is important for understanding lake responses to wildfires.  
lake water quality; lake catchment; satellite remote sensing; wildfires; climate change +
Footnote †: journal: selected
## 1 Introduction  
### General Context of Traceability for Agri-Food Supply Chains  
Maximizing food safety throughout the entire supply chain is of great importance and constitutes a major challenge towards the development of reliable agri-food supply chains. Focusing on fresh produce supply chains, they deal with perishable commodities, such as fruits and vegetables, which continue to respire and metabolize after harvest [1]. As a consequence, the management of fresh food tends to be more complicated and costly compared to any other supply chain. This is mainly because of the products' short shelf lives and sensitivity to temperature and humidity as well as possible damage during harvesting, transportation, processing, packaging or handling and shipping [2, 3]. In essence, the longer the time period pertaining to the different phases within the supply chain, the larger the probability of the food quality to get deteriorated. A number of stages take place until the fresh food is supplied to the consumers. According to [PERSON] and [PERSON] [4], who reviewed the literature on food traceability, the initial stage includes in-field activities, ranging roughly from seeding or planting and harvesting to transportation and delivery to the food processing industry. There, various operations are carried out such as cleaning, chemical treating, packaging and delivery to distributors. Subsequently, perishable products need additional maintenance, which is vital for the purpose of delivering, to the customers, products of great quality. This implies the need for appropriately cooling the storage as well as modified atmosphere storage facilities and transportation means.
# A Matheuristic for Joint Optimal Power and Scheduling Assignment in DVB-T2 Networks +
## 2 DVB-T2 Network Design
Definition 1: The DVB Network Design Problem (DVB-ND): Given a set of stations \(S\), a set of TPs \(T\), the set of directions \(D\) and \(\delta(\sigma,t)\)\(\forall t\in T,s\in T\), the fading coefficients \(a_{ts}\)\(\forall t\in T,s\in T\), the testpoint population \(r_{t}\)\(\forall t\in T\), the maximum power emission \(P^{max}\) of each station, the system noise \(N\), the SIR threshold \(\delta\) and the antenna adjacent direction power threshold \(\Delta P\), the DVB network design problem consists of establishing the power emission of each station \(s\in S\) and the serving station of each TP \(t\in T\), so that the total population covered with service is maximized, while the SIR constraints of served TPS are satisfied, each TP is served by at most one station and the power limits of the stations and antenna technological constraints are respected. The DVB-ND problem can be modeled as a mixed integer linear programming of which we detail below the feasibility constraints and objective function. Besides the SIR constraints in Equation (4), we must ensure that each TP \(t\in T\) is served by at most one station, namely:  
\[\sum_{s\in S}x_{ts}\leq 1\qquad t\in T. \tag{5}\]  
Also, we need to model the power adjacency constraints of antennas, ensuring that adjacent directions stay under the power difference threshold (note that here we assume that \(d=36+1=1\), since, in an arc of 360 degrees, by summing the 10 degrees of direction \(d=1\) to the 360 degrees of \(d=36\) we obtain 10 degrees and thus direction \(d=1\)):  
\[p_{s\,d}\leq\Delta P\;p_{s\,d+1}\qquad s\in S,d\in D. \tag{6}\]  
Finally, the objective function expresses the maximization of total revenue obtained from serving testpoints, namely:  
\[\max\ \ \sum_{t\in T}\sum_{s\in S}r_{t}\cdot x_{ts} \tag{7}\]  
Resuming all the elements, we obtain the following MILP model that we shortly indicate as DVB-MILP:  
\[\max \ \ \sum_{t\in T}\sum_{s\in S}r_{t}\cdot x_{ts}\] (DVB-MILP) \[\sum_{\sigma\in\mathrm{U}(s,t)}a_{t\sigma}\cdot p_{\sigma\delta( \sigma,t)}-\phi\sum_{\sigma\in\mathrm{I}(s,t)}a_{t\sigma}\cdot p_{\sigma\delta (\sigma,t)}+M(1-x_{ts})\geq\phi N t\qquad t\in T,s\in S\] \[p_{s\,d}\leq\Delta P\,p_{s\,d+1} s\in S,d\in D\] \[\sum_{s\in S}x_{ts}\leq 1 t\in T\] \[0\leq p_{sd}\leq p^{\max} s\in S,d\in D\] \[x_{ts}\in\{0,1\} t\in T,s\in S\,.\]
## 1 Introduction  
### Background  
In recent years, generative artificial intelligence (AI) tools, such as Microsoft Copilot, Google Gemini, and ChatGPT, have begun to transform the educational landscape significantly [1]. These technologies assist in solving complex problems and promote a more personalized and adaptive learning experience [2]. Tools like Microsoft Copilot have proven particularly useful in programming by offering real-time code suggestions, facilitating understanding concepts, and resolving errors [3]. However, their impact on student learning and motivation has yet to be sufficiently explored, especially within university educational contexts.  
One of the main challenges in adopting technological systems in education is measuring the motivation and enjoyment that users experience when interacting with these tools. The theory of cognitive absorption suggests that immersion and enjoyment during technology use can significantly influence adoption and effective learning [4]. However, measuring these emotional and cognitive dimensions remains challenging, requiring validated instruments and rigorous methodological approaches [5]. In the case of generative AI tools, it is crucial to understand how they impact the learning experience and whether they generate positive emotional effects that enhance knowledge retention.  
Learning to program web systems, particularly in languages like PHP, represents a significant challenge for university students [6; 7; 8]. The complexity of the concepts, the need for logical skills, and the steep learning curve contribute to the difficulty [9]. Universities face the challenge of designing effective pedagogical strategies that enable students to overcome these barriers. Traditionally, instructional videos have been a common tool for teaching programming, but their effectiveness compared to more interactive methods, such as generative AI, has not yet been sufficiently studied [10].  
In Latin America, research on the impact of AI tools in education is still in its early stages [11; 12]. Students in this region exhibit levels of digital literacy that differ from those in Europe, Asia, and the United States, which may influence the adoption and effectiveness of these technologies [13]. Additionally, there is a significant gap in the scientific literature addressing these differences and exploring how AI tools can be adapted to specific educational contexts. This study aims to contribute to closing this gap by evaluating the impact of Microsoft Copilot on the learning and motivation of university students in the Latin American context.
## 5 Taxonomy of Technology-Enabled Smart City Applications in 6G Networks
### Internet of Things (IoT)
#### 5.1.3 Internet of Robotic Things (IoRT)  
The IoRT is a distributed network of intelligent robot devices that can monitor events, integrate sensors' data from multiple heterogeneous sources, and use local/distributed intelligence to take actions [87]. The IoRT market is expected to reach $1.44 billion by 2022, growing at a CAGR of 29.7% from 2016 to 2022 [88]. The IoRT has several applications in several domains such as agriculture, construction, logistics, transportation, banking, healthcare, home automation, and industrial automation [89]. Robotics and automation require control in real time to avoid oscillatory movements, with a maximum tolerable latency of 100 us and round-trip times of 1 ms [17]. Moreover, industrial robotic automation requires a reliability of 99.999% [79].
# Enhanced Multiple Speakers' Separation and Identification for VOIP Applications Using Deep Learning
## 3 Model Description
### Speaker Identification Process
#### 3.3.2 Identification Training Objective  
False Rejection Rate (FRR) and False Acceptancy Rate (FAR) are two factors that heavily influence how well a speaker identification system performs [29]. FRR occurs when the target speaker is mistakenly identified as a non-target speaker. FAR refers to an error made when identifying a non-target speaker as the intended speaker. In a closed set speaker identification system, however, the top-N correctness [30] and accurate recognition rate (accuracy rate) are often employed to assess the system's effectiveness. The speech could be identified accurately after being matched with the appropriate speaker from the target set and is called the recognition rate. The speech that must be recognised is often identified as coming from the speaker who sounds the most like the target speaker set; the top-1 recognition accuracy rate is another name for this recognition accuracy ratio. The top-N recognition accuracy rate is another evaluation technique: if the right speaker is among the N recognition speakers with the highest degree of similarity. Thus, it is determined that the recognition result is accurate [31].
# An Enhanced Tree Routing Based on Reinforcement Learning in Wireless Sensor Networks +
## 4 Proposed Tree Routing Protocol  
As shown in Figure 2, the proposed tree routing protocol consists of several components. Specifically, each component interacts with others to build a tree topology and finds an optimal routing path via a parent selection. In this section, we first describe the basic operations of the proposed routing protocol and then specify the RL-based parent selection algorithm.  
Figure 2: Proposed protocol architecture.
## 2 Super-Resolution Methods  
An infrared focal plane detection system can capture essential intensity images from four different angles in a single exposure. Due to limitations imposed by the detector structure, the effective resolution of the four declination angle images is reduced by half of the original pixel size, expressed mathematically as follows:  
\[I_{\theta_{i}=1,2,3,4}^{M\times N/2}=I_{\text{out}}^{M\times N}*M\text{ask}_{\theta} \tag{1}\]  
where \(I_{\text{out}}^{M\times N}\) represents the cumulative light intensity recorded by the detector, analogous to an image obtained from a single exposure at a specific angle. \(M\text{ask}_{\theta}\) is an intensity extraction mask that biases the detection process to isolate the individual angle components within the overall light intensity. This mask is dependent on the pixel arrangement employed by the self-developed detector. Each superpixel within this arrangement consists of a tuple of four pixels. The resulting separated images for each declination angle, denoted as \(I_{\theta_{i}=1,2,3,4}^{M/2\times N/2}\), exhibit a reduced size of half the original dimensions. The Stokes notation is a widely utilized method for representing polarization states in engineering, and its formula is provided below:  
\[\mathbf{S}=[S0\quad S1\quad S2\quad S3] \tag{2}\]  
where \(S0\), \(S1\), \(S2\), and \(S3\) are parameters that describe different aspects of the incident light. \(S0\) represents the total incident light intensity. \(S1\) is the difference in intensity between the horizontal and vertical directions. \(S2\) is the intensity difference between polarized light at 45 degrees and 90 degrees concerning a reference direction. \(S3\) specifically relates to the detection of left-handed versus right-handed circular polarization. In typical projects, the detection of linear polarized light is generally carried out without considering the \(S3\) component. By using the above formula, it is possible to solve for the Stokes vector of the incident light inversely. This method allows for the retrieval of information about the polarization degree and polarization angle of the current scene.  
\[DP=\frac{\sqrt{S1^{2}+S2^{2}}}{S0}=\frac{\sqrt{\left(I_{0}-I_{90}\right)^{2}+ \left(I_{45}-I_{135}\right)^{2}}}{\left(I_{0}+I_{45}+I_{90}+I_{135}\right) \times\frac{1}{2}} \tag{3}\]  
\[AP=\frac{1}{2}\text{arctan}(\frac{S2}{S1})=\frac{1}{2}\text{arctan}(\frac{I_{4 5}-I_{135}}{I_{0}-I_{90}}) \tag{4}\]  
When \(I_{\theta_{i=1,2,3,4}}^{\text{M/2}\times\text{N/2}}\) is directly included in the aforementioned calculation formula, it becomes apparent that the dimensions of \(DP\) and \(AP\) calculated in this manner are \(M/2\times N/2\). Furthermore, due to the unique arrangement structure of the superpixel, only a single declining-angle light intensity of a given pixel can be obtained in a single exposure. This leads to an instantaneous field error in the calculated polarization signal. Hence, it is imperative to conduct super-resolution reconstruction of \(I_{\theta_{i=1,2,3,4}}^{\text{M/2}\times\text{N/2}}\).
## 2 Materials and Methods
### Feature Wavebands Selection Algorithms
#### 2.6.2 Successive Projections Algorithm  
SPA, a wavelength screening method utilizing vector projection analysis, employs an iterative projection process to identify the most representative variables. This approach effectively addresses issues related to data redundancy and multicollinearity. Starting from the initial wavelength, each iteration needs to calculate the orthogonal projection residual between the selected wavelength and the selected wavelength, and select the wavelength that maximizes the new information until the optimal wavelength selection number is achieved. In this way, SPA can significantly reduce the feature dimension while retaining the key information of spectral data, thereby improving the prediction efficiency and robustness of the model [30].