esa-sceva/satcom-chunk-collection huggingface dataset:
# An Adaptive Data Rate Algorithm for Power-Constrained End Devices in Long Range Networks
## 3 Link State Evaluation
### Combined Weighting Theory
#### 3.3.3 VIKOR Method  
The VIKOR method is a multiattribute decision-making approach that evaluates alternatives by calculating their overall performance (S value), worst performance (R value), and overall index value (Q value) to optimize objective weights [37]. The specific steps are as follows:  
Step 1: Data standardization. Based on the data standardization in the improved CRITIC method, the standardized matrix \(X^{\prime}=\left(x^{\prime}_{ij}\right)_{m\times n}\) is obtained.  
Step 2: Calculate the \(S\) and \(R\) values. The comprehensive performance value \(S_{i}\) and the worst performance value \(R_{i}\) are calculated using the following formulas in Equations (16) and (17):  
\[\begin{split} S_{i}=\sum\limits_{j=1}^{n}\omega_{j}\frac{(r^{ \prime}_{j}-r^{\prime}_{ij})}{(r^{\prime}_{j}-r^{\prime}_{j})}\\ R_{j}=\max\limits_{1\leq j\leq n}\left(\omega_{j}\frac{(r^{ \prime}_{j}-r^{\prime}_{ij})}{(r^{\prime}_{j}-r^{\prime}_{j})}\right)\\ R_{j}=\max\limits_{1\leq j\leq n}\left(\omega_{j}\frac{(r^{ \prime}_{j}-r^{\prime}_{ij})}{(r^{\prime}_{j}-r^{\prime}_{j})}\right)\end{split} \tag{16}\]  
where \(\omega_{j}\) represents the objective weight calculated using the improved CRITIC method; \(r^{\prime}_{ij}\) represents the standardized value of the \(i\)-th alternative on the \(j\)-th indicator; and \(r^{+}_{j}\) and \(r^{-}_{j}\) represent the positive ideal solution and negative ideal solution for the \(j\)-th indicator, respectively.  
Step 3: Calculate the Q value. The Q value combines the comprehensive performance and the worst performance of each alternative. The calculation formula is as follows:  
\[Q_{i}=v\bigg{(}\frac{S_{i}-S^{-}}{S^{+}-S^{-}}\bigg{)}+(1-v)\bigg{(}\frac{R_{ i}-R^{-}}{R^{+}-R^{-}}\bigg{)} \tag{18}\]  
In Equation (18), \(S^{+}=\max\limits_{1\leq i\leq m}S_{i}\), \(S^{-}=\min\limits_{1\leq i\leq m}S_{i}\), \(R^{+}=\max\limits_{1\leq i\leq m}R_{i}\), \(R^{-}=\min\limits_{1\leq i\leq m}R_{i}\); \(v\) is the decision-making coefficient, and \(v\in[0,1]\).  
Step 4: Optimize objective weights. Update the objective weights \(\omega^{\prime}_{j}\) based on the \(Q\) value. The calculation formula is given as Equation (19).  
\[\omega^{\prime}_{j}=1-\frac{Q_{i}}{\sum\limits_{i=1}^{m}(1-Q_{i})} \tag{19}\]  
Finally, the combined weight of each indicator is calculated using the multiplicative comprehensive method. The subjective weight \(\omega_{i}\) is derived from the fuzzy analytic hierarchy process, and the objective weight \(\omega^{\prime}_{j}\) is obtained through the improved CRITIC method and the VIKOR method. The combined weight \(\omega_{k}\) of the \(k\)-th indicator can be determined by the following formula, as shown in Equation (20).  
\[\omega_{k}=\frac{\omega_{ik}\cdot\omega_{jk}}{\sum\limits_{k=1}^{n}\left(\omega_{ ik}\cdot\omega_{jk}\right)} \tag{20}\]
# Green Production and Biotechnological Applications of Cell Wall Lytic Enzymes
## 3 Degradation of Lignocellulose by CWLEs
### Pectinases  
Pectin is structurally one of the most complex families of polysaccharides constituting 35% of primary walls in dicots and nongraminaceous monocots and 5-10% in grass [35]. Pectin is the less-abundant polysaccharide in lignocellulosic biomass, accounting for 5% of total dry weight, but it represents a major component in agricultural scraps such as peels and pomace. During the plant-pathogen interaction, homogalacturonan, a linear polymer of \(\alpha\)-1,4-p-galacturonic acid, is the first polysaccharide target of degradation by invading microbes. Pectinolytic enzymes include endo-polygalacturonases, exo-polygalacturonases, and pectate lase (Figure 2c) [36]. Endopolygalacturonases cleave homogalacturonan in oligomers with different degrees of polymerization by releasing digalacturonic and galacturonic acid units from their reducing ends. Concomitantly, exo-polygalactuonases release galacturonic acid from the nonreducing end of galacturonyl-oligomers released by the action of the endopolygalacturonases [37]. Pectate lyase degrades homogalacturonan to produce oligosaccharides with 4-deoxy-\(\alpha\)-p-galact-4-neuronosyl groups at their nonreducing ends by transeliminative cleavage. In order to slow down the hydrolysis of homogalacturonan by pectinolytic enzymes, plants (i) can produce proteinaceous inhibitors against microbial endopolygalacturonases [38] and (ii) can modify pectin to different extents, for instance by adding methyl- and acetyl-groups in C6 and C2-C3 positions, respectively, of d-galacturonic acid residues. On the other hand, microbes have evolved endopolygalacturonases that elude recognition by plant proteinaceous inhibitors [39] and pectin methyl- and acetyl-esterases that remove modifications from the homogalacturonan chain, thus supporting the action of pectinolytic enzymes.
## 3 Results  
The studied textile wastewaters were produced in a NE Romanian textile company that manufactures cotton fabrics colored from yellow to red during the period time of this study. The main textile wastewater (WW) characteristics treated in the SD system and their maximum admissible values are presented in Table 1.  
As shown in Table 1, some physical-chemical quality indicators of the tested WW are exceeding the maximum admissible limits, especially color (more than 17-89 timefolds), suspended solids (more than 11-26 timefolds), turbidity (more than 36-80 times considering a reference value of  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Quality Indicators** & \begin{tabular}{c} **Measured** \\ **Value [mg/L]** \\ \end{tabular} & \begin{tabular}{c} **M.A.C. * \\ **[mg/L]** \\ \end{tabular} & \begin{tabular}{c} **Quality Indicators** \\ **Value, [mg/L]** \\ \end{tabular} & \
\begin{tabular}{c} **Measured** \\ **[mg/L]** \\ \end{tabular} &
\begin{tabular}{c} **M.A.C. * \\ **[mg/L]** \\ \end{tabular} \\ \hline pH & 7.12–7.89 & 6.50–8.50 & Total \(p\) & 5.70–6.45 & 1 \\ Colour, [HU] & 865–4450 & 50 & Extractible & 25.50–31.80 & 20 \\ (A\({}_{\mathrm{Q6}}\)) & (1.200–2.620) & & substances & & \\ Suspended solids & 382–930 & 35 (60) & Total \(n\) & 8.30–10.00 & 10 \\ Turbidity, [FTU] & 180–815 & - & Ammonia & 2.0–2.75 & 2 \\ Fixed residues & 3580–4020 & 1000 & Sulfates & 780–850 & 600 \\ COD, [mg O\({}_{2}\)/L] & 560–655 & 125 & Chlorides & 95–150 & 70 \\ CBO\({}_{5}\), [mg O\({}_{2}\)/L] & 320 & 25 & Phenol index & 2.60–3.50 & 0.30 \\ Synthetic detergents & 1.70–2.50 & 0.50 & Total heavy metal ions & \textless{}4 & \textless{}2 (max 5) \\ \hline \hline \end{tabular}  
* * M.A.C.—maximum admissible concentration, according to the Romanian Government Decision No. 352/2005: Technical Norms for Treated Wastewater Discharged in Natural Water Resources (NTPA 001).  
\end{table}
Table 1: The principal characteristics of the studied textile wastewater.  
5-10 FTU), total organics content (more than 3-5 timefolds for COD, or 12 timefolds for BOD), and fixed residues (more than 3-4 timesfolds), therefore the WW treatment is obviously necessary. In any wastewater treatment process, one first step is the separation of settable and course or even fine particulated solids to obtain a WW load reduction and to provide the easy operation of the following steps used for dissolved contaminants' removal/elimination. Thus, the main WW quality indicators evaluated to underline the SD technology performance are the color and solids content (as suspended solids and colloids), without any chemicals used for the chemical treatments or activated sludge used for biological treatment.  
All our series of experiments were performed at the laboratory room temperature (\(20\pm 1\)\({}^{\circ}\)C), working with no pH adjustment at the collected textile wastewater pH (7.12-7.89).
## 3 Enabling Technologies for Smart and Resilient Grids  
Smart grids (SGs) aim at using digital technology to increase the grid controllability, efficiency and reliability. Empowering the grid with modern telecommunication infrastructures and power converters enables the necessary grid resilience for a heavily distributed energy production which is the major key to fully maximize the penetration of renewable energies given their decentralized nature. Furthermore, increasing distributed energy systems will result in a grid dominated by inverters creating even more challenges for the grid such as stability, inertia, short circuit protections [8; 9; 10], reactive power control and coordination between different controllers for different types of distributed generation unit [11; 12]. This raises power quality (PQ) concerns that may require short term energy storage systems as in [13], where a flywheel and a 3 \(\times\) 3 direct matrix converter using a predictive optimal controller are proposed to compensate voltage sags and swells while minimizing the voltage total harmonic distortion (THD).  
The need to overcome these challenges has motivated research on the topic of smart transformers [14; 15] since conventional distribution transformers were mainly designed for unidirectional power flow with little to no control capabilities over the grid parameters. Power electronics can enable increased functionalities in the conventional transformers. Possible solutions can go from a combined system with a low-frequency transformer aided by power electronic converters (hybrid transformer) to the full replacement of the low-frequency transformer by solid state transformers (SST).  
The application of matrix converters is here divided into hybrid transformers, SSTs and flexible AC transmission systems (FACTS).
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author** & **Method** & **Type** & **Effect on Heat Transfer Rate** & **Reference** \\ \hline [PERSON] et al. (2022) & Active & Rotating cylinder & Increase by 21.2\% in heat transfer rate & [16] \\ [PERSON] et al. (2021) & Active & Decanoic acid phase change & Increase by 142\% in heat transfer rate & [17] \\ [PERSON] and [PERSON] (2018) & Active & Surface vibration & Increase by 97\% in heat transfer coefficient & [18] \\ [PERSON] and [PERSON] (2007) & Compound & Combined conical-ring and twisted-tape insert & Increase by 367\% in heat transfer rate & [19] \\ [PERSON] et al. (2022) & Compound & Nano-additives and connecting plates & Increase by 29.97\% in heat transfer rate & [20] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Types of active AHT used and the effect on heat transfer rate. examined previously. This study also simulated the effect of using passive, active, and compound AHT methods in one CTHE system, which has not been performed in the past. The present study (1) investigated the heat transfer effects of varying the quantity (\(N\)) of perforated inserts along the inner tube length of the countercurrent flow concentric tube heat exchanger (CCTHE), (2) examined the impact of rotating the perforated insert at various rotational speeds on the CCTHE performance, and (3) demonstrated the effectiveness of incorporated passive, active, and compound AHT methods in terms of thermal performance factor (\(\alpha\)). Hence, this research will greatly support and benefit the establishment of a comprehensive framework for designing next-generation heat exchangers with higher efficiency across industrial applications.
## 3 Rait  
We consider a densely deployed wireless sensor network that includes camera-equipped nodes. A camera-equipped node frequently transfers an image to a sink node or gateway. A tree-based routingprotocol is used to construct the path from the sensor nodes to the sink node. The path to the sink node can be changed whenever the link states to the upper nodes are unreliable due to node failure or obstacles. The lower nodes in a routing tree have to compete with other nodes for an upper node in a routing tree. The errors and loss that take place in the air are corrected by an error-correcting code such as FEC or ACK, which are based on a retransmission technique. Each sensor node manages two queues: one for receiving and the other for sending [28, 29].  
Figure 1 presents the queuing model in a sensor node. A sensor node has two queues, a receiving queue for transit traffic and a sending queue for transferring data. The packets generated by the sensor node itself are inserted directly into the sending queue. A sensor node could generate a data packet for sensed data in an application layer or a routing packet for routing information in a network layer. Therefore, the packets received from the network also can be one of two packet types, a data packet or a routing packet. A packet received from the network is inserted into the receiving queue and then classified by the packet type and the destination address in the packet header. If the destination address of a data packet is the receiving node, the packet is transferred to an upper application layer. If the destination address is not the receiving node, the packet is inserted into the sending queue. A routing packet is used by the routing protocol in a network layer.  
In the conventional network, a node works as a packet generator (consumer) or a router, however, in the sensor network, a sensor node takes charge of both the packet generator and the router, because the sensor node has to relay the packet from other sensor nodes to the sink node. In the sensor network, the sending queue in the sensor node is shared by the node itself and the child nodes of it, whereas the  
Figure 1: Packet flow in a sensor node. A sensor node has two queues, receiving queue for received packets and sending queue for generated packets and transit packets.  
sending queue in the conventional network is only used by the node itself. Therefore, the network layer in a sensor node should take into account both the state of the receiving queue and the sending queue, whereas the network layer in the conventional network should take into account only one of the sending queue or the receiving queue. As a consequence, one sliding window algorithm is enough for the conventional network, however one more sliding window for the sending queue is required in the sensor network. In this paper, we propose the double sliding window algorithm for the reliable transfer in the sensor network.
# Adaptability of Buildings: A Critical Review on the Concept Evolution
## Content Analysis
### Dimensions and Overlapping Concepts  
This definition of adaptability sounds simple and explicit enough to be understood. Nevertheless, literature on adaptability within the built environment context contains diverse interpretations, and that it reflects a high level of relevance with other concepts and terminologies [30; 41] often utilized to contribute to the physical capacity of a building to adapt, such as Flexibility, Durability, Transformability, Upgradability, Convertibility, Accessibility (making spaces accessible for all life stages), Open Plan, and Performance-based building (which describes the performance dimensions of a building concerning functionality and preserving fit purpose over time) [42]. These strategies are manifestations of adaptability as far as they imply change, e.g., change in configuration, change in space dimensions, change of use or function, change of size, change of a building performance, and changeable building components. However, the permutations among these strategies make it rather difficult to categorize them into defined dimensions or types of adaptability. Nevertheless, some studies made distinctions between adaptability types as in [PERSON] et al. [30] that is considered by some scholars as the most comprehensive [25]. [PERSON] et al. [30] articulate the physical capacity of adaptable buildings in six types: availability, extendibility, flexibility, refitability, movability, and recyclability. Table 5 summarizes the various terminologies and notions together with their relation to the different types of adaptability by considering the type of change they imply.
## 3 System Validation Methods
### Audio Fingerprint Matching
#### 3.2.2 Hamming Distance  
In order to find a simple audio matching for verifying and validating the proposed system, the Hamming distance is implemented because is performed at the bit-level and therefore, requires less computational complexity. The Hamming distance between two \((NFx32)\)-bit binary fingerprint vectors \(fv_{1}\) and \(fv_{2}\) is computed as Equation (1):  
\[H_{d}(fv_{1},fv_{2})=\sum_{i=1}^{NFx32}F(bit_{fv_{1}}(i)\
eq bit_{fv_{2}}(i)) \tag{1}\]  
where \(NF\) denotes the number of subfingerprints of vectors, \(bit_{fv_{1}}(i)\) and \(bit_{fv_{2}}\) are the i\(th\) element of binary fingerprint vectors, and \(F()\) is an function defined by Equation (2):  
\[F(x)=\left\{\begin{array}{ll}1&\text{if $x$ is true}\\ 0&\text{otherwise}\end{array}\right. \tag{2}\]
# Design of a Chaotic Trajectory Generator Algorithm for Mobile Robots
## 7 Experimental Results
### Methodology  
Three experiments with the Khepera III differential robot were performed using different initial conditions with 200 iterations for each of the following cases:  
1. \(x_{0}=1.1\), \(y_{0}=-0.9\), \(\theta_{0}=1.8^{\circ}\);
2. \(x_{0}=-0.5\), \(y_{0}=-1.3\), \(\theta_{0}=359.1^{\circ}\);
3. \(x_{0}=232.1\), \(y_{0}=685.3\), \(\theta_{0}=-10.3^{\circ}\).  
The black square represents the boundaries of the work area (table) described above. The dimensions of the table are 1400 \(\times\) 2400 mm, the same as the work area taken for the simulations shown in previous sections.  
The experimental results are shown in Figure 17. Each color represents a time span of two seconds during which the robot uses the speed values obtained by each iteration before moving to the next ones.  
Figure 16: Khepera III differential drive mobile robot.  
It can be seen from Figure 17a,b that a small change in millimeters in the initial conditions produces completely different and unpredictable trajectories in the robot, demonstrating experimentally that the chaotic property of sensitivity to initial conditions is met. It is notable that with 200 iterations most of the work area is covered. It should be mentioned that these experimental results are preliminary; it is necessary to improve the algorithm of evasion of edges so that the angle of reflection is more precise and use other chaotic systems to compare their performance.
## 1 Introduction
However, currently available event detection methods are unable to detect fixations, saccades, PSOs, and smooth pursuits simultaneously. In this study, we proposed a combination of 2D-CNN with an LSTM deep learning model that simultaneously identifies fixations, saccades, PSOs, and smooth pursuits. We aim to find the combination of features that gives the best results for different events. We compared the proposed model for different sets of features extracted from the raw data and with the baseline method, which is the most similar to our approach, presented in [21]. We made this comparison on two levels, i.e., a sample level and an event level. Finally, we evaluated event measures for the proposed methods and compared them to the results of manual classification. The event-measuring metrics were the number of events and the mean duration of events. We tested the performance of our methods on the publicly available dataset described in the Section 3. This work has significant practical implications for eye-tracking applications. For example, in human-computer interaction (HCI), accurate eye movement classification can improve gaze-based interfaces, enabling smoother and more intuitive user experiences. Inmedical diagnosis, the precise identification of eye movement events can help in the early detection of neurological disorders such as Parkinson's disease or multiple sclerosis, where abnormalities in eye movements serve as critical diagnostic markers. By improving the classification performance for multiple eye movement events simultaneously, this model could provide more reliable and detailed eye-tracking analytics, facilitating advancements in both HCI systems and clinical diagnostics. The main contributions of the paper are as follows:  
* Testing the performance of the models for different feature sets consisting of velocity, acceleration, jerk, and direction. * Creating a model that simultaneously classifies four types of events, i.e., fixations, saccades, smooth pursuits, and post-saccadic oscillations. * Comparison of the usability of different features in classifying specific events. * Proposing the use of event statistics as a simple tool to evaluate the quality of a model. The remainder of this paper is structured as follows: The related work is reviewed in Section 2. Section 3 describes the dataset used in this study, including its characteristics and potential biases. The proposed method is discussed in Section 4. Results and discussion are presented in Section 5. Section 6 evaluates the proposed methods using event measures. Finally, the paper concludes in Section 7, where future directions are outlined.
## 2 Experimental Validation
### Comparison of Fire Resistance  
To obtain fire resistance, it is necessary to apply the thermal insulation criterion, defined by the standard EN 1363-1:2020 [20]. The fire resistance by this criterion is defined by the shortest time step, on the unexposed face, based on the maximum temperature \(T_{mix}\), that is, 180 \({}^{\circ}\)C above the initial temperature T0 of 20 \({}^{\circ}\)C, or by the mean surface temperature \(T_{m\dot{a}d}\), 140 \({}^{\circ}\)C above the initial temperature, Equations (2) and (3), respectively.  
\[T_{mix}=T_{0}+180 \tag{2}\]  
\[T_{m\dot{a}d}=T_{0}+140 \tag{3}\]  
According to the standard EN 1363-1:2020 [20], as soon as the surface not exposed to fire reaches a temperature higher than the maximum point temperature or the average allowable temperature, the insulation criterion is no longer verified.  
To analyze the temperature evolution, different nodal points were defined on the model surface not exposed to fire. For this study, only the model M4 was considered, because it presented the best approximation to the experimental test. Additionally, to reach the limit temperatures imposed by the thermal insulation criterion according to EN 1363-1:2020 [20], another numerical simulation was carried out considering the temperature evolution equivalent to 240 min of fire exposure.  
The average temperature on the surface not exposed to fire and on the node where the maximum temperature occurs was verified. It was concluded that the fire resistance imposed by the thermal insulation criterion is approximately equal to 216 min.  
Analyzing the results obtained by [PERSON] and [PERSON] [7] for Test 4, and based on the computational model, the authors obtained a fire resistance of approximately equal to 226 min. With this, it is concluded that the results obtained for the developed M4 model are close to the results obtained by the researchers, thus, validating the numerical model.