esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Transport System Modelling and Optimization
### Stackelberg Approach  
In the case of transport systems where traffic flow distribution is determined by a central decision-maker (routed traffic), the total transport time may be treated as a decisive criterion for traffic flow distribution within a transport network. In the Stackelberg game, there are restrictions and conditions on the movement of traffic flows determined by the Equations (8)-(11). Therefore, given the above assumptions, the objective function with respect to the optimization of the transport system can be written as Equation (14) [26; 65]:  
\[T_{c}(X^{*})=min\Big{\{}T_{c}(X):X\in D^{dop}\Big{\}}, \tag{14}\]  
where:  
\[T_{c}(X)=\sum_{(a,b)\in E}\sum_{p\in p^{ab}}x^{p,ab}\times t^{p,ab}=\sum_{(i,j )\in L}x_{ij}t_{ij}\big{(}x_{ij}\big{)}, \tag{15}\]  
where: \(X\)--traffic flow distribution matrix, \(X^{*}\)--optimal traffic flow distribution matrix, \(T_{c}(X)\)--criterion function (total traffic flow movement time), \(D^{dop}\)--set of permissible solutions, \(x^{p,ab}\)--traffic flow volume on road \(p\) within a O-D pair \((a,b)\), \(t^{p,ab}\)-movement time for a traffic flow unit on road \(p\) within the O-D pair \((a,b)\).  
In road traffic engineering, optimization based on the minimum total time criterion is called the Stackelberg traffic flow distribution or system optimum (SO) solution. Optimization is carried out according to the criterion of equal end times in order to achieve this state of a transport system. For each pair of transport O-D \((a,b)\), the marginal times \(m^{p,ab}\) for all movement routes \(p\in P\) should be mutually equal, as described by the relationship (16) [25; 28; 29; 66]:  
\[\forall(a,b)\in E,\ \forall p,q\in P^{ab}\ m^{p,ab}(x)=m^{q,ab}(x). \tag{16}\]  
The marginal time for the route \(p\) is calculated as the sum of the marginal times of the links on the route \(p\), as presented in Equation (17):  
\[m^{p,ab}(x)=\sum_{(i,j)\in p}m_{ij}\big{(}x_{ij}\big{)}. \tag{17}\]  
And the marginal time for the link \((i,j)\) is equal to the derivative of the product of the average travel time through link \((i,j)\) and the size of the traffic flow on link \((i,j)\), which is described by the Equation (18):  
\[m_{ij}\big{(}x_{ij}\big{)}=\frac{d\big{[}t_{ij}\big{(}x_{ij}\big{)}\cdot x_{ ij}\big{]}}{dx_{ij}}. \tag{18}\]  
If the equality of marginal times in O-D pairs is satisfied, then a change of route by any vehicle would result in a loss for the whole system, moving the value of the objective function away from the optimal value.
# Enhanced Matching Game for Decoupled Uplink Downlink Context-Aware Handover
###### Abstract  
In this paper, we address the problem of cell association during a handover performed in a dense heterogeneous network, where the preference of a mobile user's equipment in terms of uplink traffic is not the same as for the downlink traffic. Therefore, since mobility is an intrinsic element of cellular networks, designing a handover from the perspective of the uplink and downlink is mandatory in the context of 5G cellular networks. Based on this arena, we propose a decoupled uplink-downlink handover scheme while making use of femtocells in order to maximize the overall network entity utilities and avoid overloading macrocells. However, the fact that the handover process is performed in a dense heterogeneous network makes the issue NP-hard. Therefore, taking into account the need for self-organizing solutions, we modeled the handover process as a matching game with externalities. Thus, we will provide an aspect of intelligence for the execution of the handover process to mobile user's equipment (UE). To make the proposition more efficient, we integrate an assignment step to assist the matching game. Hence, the base stations will be investigated and filtered, keeping only the helpful base stations as the players in terms of the quality of service for the uplink and downlink. The numerical results verify the superiority of the proposed context-aware algorithm over traditional downlink handover and traditional decoupled uplink and downlink handover schemes, by improving the load balancing, increasing rates and reducing delays.  
5G; heterogeneous wireless networks; small cells; decoupled uplink and downlink association; network selection; matching game; handover process; cell association +
Footnote †: journal: Future Internet
# Solving Multi-Document Summarization as an Orienteering Problem
## 4 Ant Colony Optimization  
ACO is an SI meta-heuristic inspired from the collective behavior of real ant colonies. Ants use pheromone traces to communicate with each other to find the shortest path between their nest and food. There are several ACO algorithm variants for approximating solutions to optimization problems, such as the ant system (AS) and ACS [5].  
ACS was proposed by [PERSON] and [PERSON] [5] as an improvement of the AS algorithm for solving large instances of the TSP. ACS modifies the three updating rules of AS: the _state transition rule_, the _global updating rule_, and the _local updating rule_. An ant \(k\) chooses to move from city \(r\) to city \(s\) by using the following rule:  
\[s=\begin{cases}arg\ max_{u\in I_{k}(r)}\{[\tau(r,u)].[\eta(r,u)]^{\beta}\}&if \quad q\leqslant q_{0}\ \ (exploitation)\\ S&if\quad q>q_{0}\ \ (biased\ exploration)\end{cases} \tag{7}\]  
where \(J_{k}(r)\) is the set of all cities that can be visited by the ant \(k\), \(\tau\) represents the desirability measure (the pheromone), \(\eta\) stands for the heuristic value, \(q\) is a random number \(q\) uniformly distributed over \([0,1]\), and \(q_{0}\) is a parameter with a value between \(0\) and \(1\) (inclusive) to control the relative importance of exploration versus exploitation. The parameter \(\beta\) has a value greater than zero and controls the relative weight of the pheromone with respect to the heuristic. \(S\) is a randomly selected city chosen according to the following probability distribution:  
\[P_{k}(r,s)=\begin{cases}\frac{[\tau(r,s)].[\eta(r,s)]^{\beta}}{\sum_{u\in I_{k }(r)}[\tau(r,u)].[\eta(r,u)]^{\beta}}&if\quad s\in J_{k}(r)\\ 0&\text{otherwise}\end{cases}. \tag{8}\]  
The state transition rule of ACS, formulated by Equations (7) and (8), is called _pseudo-random proportional_. The global updating rule is applied once all the ants complete their tours. In ACS, only the ant that produces the best tour, so far, is allowed to add an amount of pheromone according to the following equation:  
\[\tau(r,s)\leftarrow(1-\alpha).\tau(r,s)+\alpha.\Delta\tau(r,s) \tag{9}\]  
where  
\[\Delta\tau(r,s)=\begin{cases}(L_{gb})^{-1}&if\ (r,s)\in global\ best\ tour\\ 0&\text{otherwise}\end{cases}.\]  
\(\alpha\) is the pheromone decay parameter whose values range between \(0\) and \(1\), and \(L_{gb}\) is the cost of the best solution generated from the beginning of the trial. The local updating rule is applied during the construction of the solutions. The amounts of pheromone of the visited edges are updated as follows:  
\[\tau(r,s)\leftarrow(1-\rho).\tau(r,s)+\rho.\Delta\tau(r,s) \tag{10}\]where the value of the parameter \(\rho\) is between 0 and 1 (exclusive). A possible value for \(\Delta\tau(r,s)\) is the initial pheromone value \(\tau_{0}\).
# Geometric Methods and Computational Tools for Switching Structured Linear Systems
## 1 Introduction
The study of such properties was first approached with _geometric methods_ in [14], where the notion of controlled invariance was introduced in connection with disturbance decoupling, and in [19], where the notion of conditioned invariance was presented in connection with unknown-input state observation. The geometric notions introduced in [14; 19] for switching structured linear systems were also illustrated by _directed graphs_. As a matter of fact, graph theory has often been used to analyze the properties of structured linear systems, and its extension to switching structured linear systems is quite natural, although not completely straightforward. Several of the classic control problems addressed in the papers mentioned earlier [1; 2; 3; 4] were modeled and solved by graph-theoretic methods, and those methods have been developed further with the broad employment of structured linear systems in the study of networked systems [20; 21; 22; 23; 24]. This work is organized as follows. In Section 2, the class of dynamical systems dealt with is introduced. A state-space representation and a graph representation are given and the main assumptions are elucidated. Section 3 delves into the geometric notions of invariance, controlled invariance, and conditioned invariance for switching structured linear systems. In Section 4, these notions are shown to be essential to the solution of the disturbance decoupling problem and of the unknown-input state-observation problem. The proofs of the solvability conditions given for disturbance decoupling by state feedback and for unknown-input state observation can be found in [14; 19], where those problems were tackled first. The discussion of the disturbance decoupling problem by output feedback is new in the literature, although it was included in a preliminary version, by the same authors, of [14]. Section 5 contains some concluding remarks. _Notation._\(\mathbb{R}\) and \(\mathbb{R}^{+}\) stand for the sets of real numbers and nonnegative real numbers, respectively. Linear maps and matrices are denoted by slanted capital letters like \(A\). Let\(A=\left[a_{ij}\right]_{j=1,\ldots,n,\,j=1,\ldots,m}\), where \(a_{ij}\) is either a fixed zero or an unknown real parameter; the notation \(a_{ij}\
ot\equiv 0\) means that the entry \(a_{ij}\) of matrix \(A\) is not a fixed zero. Given two sets \(A\) and \(B\), the symbol \(A\setminus B\) denotes the set difference between \(A\) and \(B\): i.e., \(A\setminus B=\{a\in A;\;a\
ot\in B\}\). Given a set \(A\), the notation card \(A\) stands for the cardinality of \(A\).
## 3 Results and Discussion
### Characterization of the Prepared SPME
#### 3.1.2 Thermal Stability of Coating  
TGA was conducted to test the thermal stability of SPME coatings [25]. If the synthesized material has a higher decomposition temperature than the analyte, then the material is suitable for SPME coatings. The TGA curve is shown in Figure 2a. Weigh loss between 60 to 120 \({}^{\circ}\)C can be explained by the evaporation of small molecules such as water and methanol, which reside in DVB/Carbon/PDMS during synthesis. The weight loss at 250 \({}^{\circ}\)C for 60 min was merely 0.558%. It thus follows that such coating material bears sound thermal stability and is able to meet the thermal desorption standard of SPME analytes. Figure 2b depicts the change in the percentage of the weight of the material, which does not undergo decomposition until 320 \({}^{\circ}\)C.
###### Abstract  
This paper presents a comprehensive survey about the fundamental components of the artificial pancreas (AP) system including insulin administration and delivery, glucose measurement (GM), and control strategies/algorithms used for type 1 diabetes mellitus (T1 DM) treatment and control. Our main focus is on the T1 DM that emerges due to pancreas's failure to produce sufficient insulin due to the loss of beta cells (\(\beta\)-cells). We discuss various insulin administration and delivery methods including physiological methods, open-loop, and closed-loop schemes. Furthermore, we report several factors such as hyperglycemia, hypoglycemia, and many other physical factors that need to be considered while infusing insulin in human body via AP systems. We discuss three prominent control algorithms including proportional-integral- derivative (PID), fuzzy logic, and model predictive, which have been clinically evaluated and have all shown promising results. In addition, linear and non-linear insulin infusion control schemes have been formally discussed. To the best of our knowledge, this is the first work which systematically covers recent developments in the AP components with a solid foundation for future studies in the T1 DM field.  
type-1 diabetes mellitus; insulin; closed loop and open loop schemes; artificial pancreas; PID controller; \(\beta\)-cells; model predictive controller; and fuzzy logic controller
## 2 Materials and Methods
### Support Vector Regression  
The SVR surrogate model is given as follows:  
\[\hat{g}(\mathbf{x})=\mu+\sum_{i=1}^{n}\omega_{i}\psi(\mathbf{x},x_{i}) \tag{3}\]  
Faced with a linear function approximation, SVR can be recalculated as follows:  
\[\hat{y}(\mathbf{x})=\mu+\left\langle\mathbf{\omega},\mathbf{x}\right\rangle \tag{4}\]  
where \(\mu\) is the threshold and \(\left\langle\cdot,\cdot\right\rangle\) represents the dot product. Find the weights that minimize the margin \(\varepsilon\) corresponding to solving the optimization problem and introduce slack variables \(\xi_{i}\) and \(\xi_{i}^{*}\) to keep \(\hat{g}(x)\) as flat as possible:  
\[\begin{split}\min\frac{1}{2}\|\mathbf{\omega}\|^{2}+\gamma\sum_{i=1 }^{n}\left(\xi_{i}+\xi_{i}^{*}\right)\\ \text{s.t.}\left\{\begin{array}{l}y_{i}-\left\langle\mathbf{\omega },x_{i}\right\rangle-\mu\leq\varepsilon+\xi_{i},\ \ i=1,2,\cdots,n\\ \left\langle\mathbf{\omega},x_{i}\right\rangle+\mu-y_{i}\leq\varepsilon+\xi_{i}^{* },\ \ i=1,2,\cdots,n\\ \xi_{i},\xi_{i}^{*}\geq 0,\ \ \ i=1,2,\cdots,n\end{array}\right.\end{split} \tag{5}\]  
where \(\gamma\) represents a regularization parameter that controls the trade-off between the complexity of the model and the level at which errors are allowed to exceed \(\varepsilon\). The following formula can be used to obtain the weight coefficient:  
\[\mathbf{\omega}=\sum_{i=1}^{n}(\alpha_{i}-\alpha_{i}^{*})x_{i} \tag{6}\]  
where \(\alpha_{i}\) and \(\alpha_{i}^{*}\) are the Lagrange multipliers. The SVR model obtained by inserting Equation (6) into Equation (4) is as follows:  
\[\hat{g}(\mathbf{x})=\mu+\sum_{i=1}^{n}(\alpha_{i}-\alpha_{i}^{*})\langle x_{i},\bm {x}\rangle \tag{7}\]  
For nonlinear approximation, the kernel function can be used to replace the dot product of the input vectors to obtain the nonlinear transformation, and the resulting SVR model is expressed as follows:  
\[\hat{g}(\mathbf{x})=\mu+\sum_{i=1}^{n}(\alpha_{i}-\alpha_{i}^{*})k(x_{i},\mathbf{x}) \tag{8}\]
## 3 Results
### Biodiversity Analysis  
The results of the selected biodiversity coefficients analysis are presented in Table 2. Excessive levels of biostimulation (NP3) contribute to a significant decrease in biodiversity (OTU number, Simpson's index, and phylogenetic diversity). In other experimental variants, no significant differences are noted.  
The analysis of the taxonomic structure of bacterial populations shows significant differences between the variants characterized by a low level of nitrogen compounds (NP0 and NP1), and the variants in which the amount of these compounds is at the optimal, and over-optimal, level (NP2 and NP3). In the samples with the levels of nitrogen compounds below the optimal concentration, the domination of the Sphingobacteria (26% for NP0 and 25% for NP1) and Alphaproteobacteria (24% for NP0 and 27% for NP1) is noted. The variants with a lower ratio of carbon to nitrogen show a much higher abundance of the Gammaproteobacteriaabacteria (39% for NP2 and 41% for NP3) and Betaproteobacteriaabacteria (24% for NP2 and 28% for NP3). A detailed taxonomic analysis is presented in Figure 4. It should be mentioned that the share of the orders Sphingobacteriales and Xanthomonadales decreases significantly in variants with optimal and excessive levels of nitrogen compounds. The Burkholderiales order shows the opposite trend. Moreover, excessive biostimulation contributes to a large decrease in the share of the Pseudomonas and Rhizoles orders, compared to the trials with optimal, or suboptimal, supplementation.  
The linear discriminant analysis effect size (LEfSe) allows for the selection of the most differentially abundant taxa between a deficient nitrogen level and a sufficient nitrogen level in experimental variants. (Figure 5). The taxa particularly responsive to changes in the supply of nitrogen compounds include Gammaproteobacteria and Betaproteobacteria, as well as the orders Xanthomonadales, Burkholderiales, Sphingomonadales, Flavobacteriales, and Sphingobacteriales.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Diversity Index** & **NP0** & **NP1** & **NP2** & **NP3** \\ \hline OTU number & 96 \(\pm\) 4 & 101 \(\pm\) 5 & 100 \(\pm\) 2 & 85 \(\pm\) 4 \\ Simpson’s index & 0.85 \(\pm\) 0.02 & 0.84 \(\pm\) 0.02 & 0.84 \(\pm\) 0.01 & 0.79 \(\pm\) 0.02 \\ Phylogenetic diversity & 4.41 \(\pm\) 0.07 & 4.57 \(\pm\) 0.11 & 4.43 \(\pm\) 0.08 & 4.26 \(\pm\) 0.07 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Microbial biodiversity coefficients in the analyzed variants after 168 h of the biodegradation process.  
Figure 4: The relative abundance of the bacterial orders in the experimental variants after 168 h of the biodegradation process. Taxa with relative abundance below 1% are excluded from the analysis.
## 2 Related Work
### ResNet  
ResNet was developed by [PERSON] et al. (2015) in 2015 and has significantly contributed to the learning and performance of deep learning models. In deep learning, deeper layers  
Figure 2: System flowchart.  
are preferable for learning more complex patterns and high-level features, but this can lead to gradient vanishing or exploding issues during backpropagation. To address these challenges, the authors introduced residual connections to mitigate the gradient vanishing problem in very deep neural networks and to maximize the learning benefits of deep networks. ResNet has shown that performance can improve continually as network depth increases, offering various depth versions, including 18, 32, 50, 101, and 152. The skip connection, also known as a residual connection, is a mechanism in ResNet that adds the input of a specific layer directly to its output. This mechanism is expressed by the following equation:  
\[y=f(x)+x \tag{1}\]  
The input \(x\) refers to either the initial value or the output of the previous layer, while \(f(x)\) represents the transformation applied to \(x\) by the layer, with \(y\) indicating the final output value. In ResNet, skip connections enable the direct transfer of the input \(x\) to the next layer, thus preserving essential information that may be lost during training. This structure allows the network to learn new features \(F(x)\) while maintaining the original input \(x\), thereby preventing issues like vanishing gradients during backpropagation and ensuring stable training. It also reduces redundant computations, aiding in faster convergence of the network. If the residual is zero, it simply performs the identity mapping of \(x\), passing the input unchanged.  
Unlike ResNet-18 and ResNet-34, which utilize a basic block structure, the ResNet-50 model and beyond utilize a bottleneck block to mitigate gradient issues as network depth increases. Figure 3 depicts the distinctions between basic and bottleneck blocks and describes the formula for skip connections based on network depth. The basic block in (a) consists of two \(3\times 3\) convolution layers with a skip connection, whereas the bottleneck block in (b) includes three convolutional layers. The first \(1\times 1\) convolution layer reduces the number of input channels, thereby reducing computational cost, while the \(3\times 3\) convolution preserves the spatial dimensions of the input, performing critical feature extraction. The second \(1\times 1\) convolution restores the dimensions to their original size. The formula for the bottleneck block structure is presented below.  
\[y=W_{3}(W_{2}(W_{1}x)) \tag{2}\]  
\(W_{1}\), \(W_{2}\), and \(W_{3}\) represent \(1\times 1\), \(3\times 3\), and \(1\times 1\) convolution operations, respectively.  
Figure 3: ResNet block diagram. (**a**) Basic block for ResNet-18, ResNet-34 (**b**) Bottleneck block for ResNet-50, ResNet-101, and ResNet-152.
## 1 Introduction  
Due to their low power consumption, great brightness and long lifetime, GaN-based light-emitting diodes (LEDs) have been employed in solid state lighting applications such as lamps, headlights, plant growth, and lighting communication [1]. Although various research has offered ways to improve the efficiency of light-emitting devices, lifetime degradation owing to heat generation and leakage current remains an issue [2, 3, 4, 5, 6]. Due to the different resistances of the electrode and devices, heat is generated around the electrode contact in the LED current injection, and the device's operational temperature steadily rises, resulting in a shorter lifetime. Passivation is one of the methods for reducing leakage current and increasing reliability, and insulator materials such as AlN\({}_{x}\), SiN\({}_{x}\) and SiO\({}_{2}\) are commonly utilized [7, 8]. However, passivation of these insulating materials necessitates a plasma-enhanced chemical vapor deposition (PECVD) approach with a high temperature process. This produces poor p-type ohmic contact between metal and LED surfaces, which might result in device performance impairment during the PECVD process [6]. New technologies or materials with little effect on the device surfaces or contacts are required to achieve high LED reliability.  
Recently, researchers used hexagonal boron nitride (h-BN) as a passivation layer to increase the performance of high electron mobility transistors and solar cells [9, 10, 11]. One of the most notable advantages of using h-BN as a passive layer over other insulators is that the process of applying it to the device does not require high temperatures. Moreover,h-BN, sometimes known as white graphene, is a two-dimensional material with a structure similar to graphene, a III-V compound containing covalently bound boron and nitrogen atoms. High flexibility, high transmittance, high strength, high thermal conductivity, high temperature stability, and insulator qualities are all characteristics of an h-BN thin film [12; 13; 14; 15].  
In this study, we introduce h-BN as a passivation layer to improve the reliability of GaN-based LEDs. For material properties, scanning electron microscopy (SEM), transmission electron microscopy (TEM), and Raman spectroscopy were used to examine the produced h-BN film. The performance of the LEDs with h-BN passivation was compared to that of normal LEDs by measuring leakage current, electroluminescence, and light output power. Temperature measurement using an infrared camera validated the heat dispersion effect of the h-BN layer.
# Evaluation of 6 LoWPAN Generic Header Compression in the Context of a RPL Network
## 6 Discussion
When using 6 LoWPAN-GHC, the total energy consumption would be equal to 6.05 mJ, with 332 \(\upmu\)J for compression and processing and 5.72 mJ for transmission. At a data rate of 10 kbit/s, the total energy consumption is reduced by 1.74 mJ (\(-\)22%) because of the compression. For a data rate of 40 kbit/s, the total reduction is 0.28 mJ (\(-\)14%). At a data rate of 100 kbit/s, there is no change in the total energy consumption. The transmission energy reduction is nullified by the energy spent in 6 LoWPAN-GHC compressing. \begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt}} \hline \hline  & 6 LoWPAN-GHC Proc. delay & 6 LoWPAN-GHC Transm. delay \\  & 6 LoWPAN Proc. delay & 6 LoWPAN Transm. delay \\ \hline
**Packet Type** & **Data Rate (kbit/s)** & **Total Delay (Processing + Transmission Delay)** \\ \hline DIS & 10 40 100  
Note that the results obtained pertain specifically to the energy consumption and transmission delay associated with frame transmission. We observed through measurements that 6 LoWPAN-GHC has a negligible impact on the decompression and processing of received packets. Furthermore, the energy consumed for creating the dictionary is negligible compared to the energy spent for compressing the packet. Creating the dictionary is a simple copy operation of the IPv6 addresses from the packet.