esa-sceva/satcom-chunk-collection huggingface dataset:
# Self-Learning Power Control in Wireless Sensor Networks
## 5 Simulations
### Results
#### 5.2.3 Energy Consumption Analysis  
The energy consumption is directly related to the power and time used by a device while performing specific tasks. Our model excludes sleeping techniques, thus, when the queue for packets is empty, the receiver is kept on. Indeed, the energy is mostly consumed in reception mode, and overall varies based on the power intensity and duration of the transmissions.  
The transmission duration is influenced by the rate of packet generation and retransmissions as a consequence of attenuation, fading and interference. In this section, we provide the details of  
Figure 12: Performance in WSN with eight nodes during the testing period (4 m node distance): average PRR (**a**); average latency (**b**); and energy consumption per bit (**c**).  
Figure 13: Power standard deviation in a WSN with eight nodes.  
the average energy consumption in the homogeneous network with eight nodes. Figure 14 shows the energy consumption only in the transmission mode, while Figure 15 illustrates the total energy consumption in all the modes (i.e., transmission, switch and reception). Looking at Figure 14a, where \(d=2\), the energy consumed by the nodes in the transmission mode has a monotone increasing trend. Instead, in Figure 14b, where \(d=4\), the energy consumption has a convex shape. In the latter case, the energy in transmission mode, using the power between \(-35\) dBm and \(-27\) dBm, is higher than in the case where \(d=2\), because more retransmissions are needed to successfully deliver the packets, given the increased attenuation in the link (i.e., higher \(BER\)). However, in the same range of transmission power levels, the total energy is lower, as per Figure 15a,b. It is explained by the fact that spending more time in transmission mode, in a constant period of observation, is beneficial for the total energy consumption, when the transmit power is lower than \(0\) dBm. In the latter case, the current draw is higher at reception than transmission. Moreover, the node is at reception mode by default, if the buffer of packets to transmit is empty. For the same reason, in Figure 15, the total energy consumption, up to \(0\) dBm, has the lowest trend when the inter-arrival rate \(\mu=25\) ms, and increases as \(\mu\) raises. The trend is reversed after \(0\) dBm.  
Figure 14: Transmit energy consumption, zooming on the lower values, of the homogeneous network with eight nodes and \(d\) equal to: 2 m (a); and 4 m (b).  
Figure 15: Total energy consumption of the homogeneous network with eight nodes and \(d\) equal to: 2 m (a); and 4 m (b).
## 2 State of the Art  
### Maintenance Policies  
Maintenance planning consists of all the scheduled set of procedures, activities, or tasks to follow aimed at both maintaining proper reliability equipment and preventing undesirable downtime and breakdowns. These activities involve the definition of a proper maintenance policy and job planning and scheduling [26]. In accordance with the time of maintenance executed, maintenance-types classification is usually divided into two main classes: (i) corrective maintenance (CM) consisting of all the rules and actions carried out after the system breaks down and (ii) preventive maintenance (PM) consisting of preventing the failure by means of all the procedures and activities carried out during system operation. According to the definition reported by [27], the activities that characterized corrective maintenance strategy are grouped into minimal repair (1C) and corrective replacement (2C). The former consists of the procedures involved in restoring the system to the failure rate it had when it failed without system time modification, while the latter involves the replacement of the system. Thus, the system time restarts to zero while the reliability curve belongs to the new implemented system. Concerning preventive maintenance, it is characterized by two different activities: simple preventive maintenance (1P) and preventive replacement (2P). The former involves the modification of the system reliability to some newer time aimed at maintaining the system in the normal operating conditions,while the latter consists of the implementation of corrective replacement aimed at restoring a new reliability curve in the system state.  
PM activities can be classified into periodic maintenance or time-based maintenance (TBM), and predictive maintenance or condition-based maintenance (CBM). Time-based maintenance is a conventional maintenance practice based on a calendar schedule, in which maintenance tasks (e.g., preventive repair times/intervals) are performed on a system at fixed or predetermined time intervals. Thus, to estimate the system aging, failure time data or used-based data [28] are adopted. According to the hazards or failure rate trends, known as bathtub curves [29], the TBM strategy assumes that the failure behavior of the considered system is predictable.  
Finally, condition-based maintenance has attracted much attention over the last decade, as reported in [30; 31], since the widespread deployment of sensors, actuators and controllers. According to [32], CBM involves maintenance procedures and activities carried out according to the information collected through the condition monitoring process. This process allows detecting indicative prognostic parameters aimed at providing proper information on potential or incipient faults. A detailed overview of CBM is provided by [PERSON] et al. [33] using bibliometric methods, and by [PERSON] et al. [34] focusing on mathematical modelling and optimization approaches.
## 3 Results and Discussion
### Fuel Properties
Nominal fuel feed \(\dot{m}_{nominal}\), in the case of FADR (i.e., 9.83 kg/h) was higher compared with the value for HFO (i.e., 8.52 kg/h). This was due to the lower calorific value of FADR (i.e., LHV 36619 kJ/kg) compared with HFO (i.e., LHV 42251 kJ/kg). It should be underscored that the assumption of nominal power at 100 kW\({}_{\mathrm{th}}\) is very useful, since the power loss is easily transformed into %. The results of energy and power loss due to the demands on fuel transportation and heating are presented in Figures 4 and 5. It can be noticed that in almost all cases the total energy demand varies in the range of 320-806 kJ/kg of combusted fuel, and the more significant component is from the pressurization demand  
Figure 3: The comparison of kinematic viscosity of heavy fuel oil, HFO, and animal fats, data from [5; 16; 28; 29; 30; 31; 32; 33; 34; 35; 36]. (i.e., in the range of 273-635 kJ/kg). This range is consistent with the values presented by [PERSON] et al. [12], who reported the required energy in the range of 180-540 kJ/kg of fuel feed (0.05-0.15 kWh/kg) depending on burner technology. In one case, the energy demand was significantly higher due to the higher value of the pressurization component. Namely, [PERSON] et al. [17] reported the necessary pressure for atomization at the level of 14 bar. The lowest energy demand (i.e., \(E_{total}\) 320 kJ/kg) was observed by [PERSON] et al. during micro wax combustion [22]. Except for the [PERSON] et al. [17] study, the power loss is less than 2% (i.e., in the range of 0.73-1.94%). It is less than 2 kW\({}_{\text{th}}\) for the nominal input power of 100 kW\({}_{\text{th}}\). Except for the [PERSON] et al. [17] study, the required pressure of fuel was in the range of 219-635 kPa. The flow characteristics of FADR by nozzle (diameter of 1 mm and 0.5 mm) are presented in Table 3. It is clear that in the case of this study, the nozzle with a diameter of 1 mm was not efficient due to the too-high value of flow rate, as well as the inefficient atomization quality. The good-enough parameters were obtained using a 0.5 mm nozzle. At a pressure of 528 kPa, the FADR flow rate was in the range of 10.3 kg/h, which is referred to as 105 kW of input power. [PERSON] and [PERSON] [16] reported that the spray formation of animal fat was satisfactory at a pre-heating temperature of 60-95 \({}^{\circ}\)C; nevertheless, during the experimental run they kept the temperature at a level of 94-96 \({}^{\circ}\)C. [PERSON] et al. [9] recommended a higher pressure of fats after the pump (i.e., 10-30 bar); whereas, in the case of this study, it was 5.26 bar. FADR has a significantly lower sulfur content, which is directly related to the lower SO\({}_{2}\) emission. Nitrogen content in FADR is higher (i.e., 0.85 w.%) compared with HFO (i.e., 0.56 w.%). It is known that nitrogen content in fuels influences NOx emission. This issue will be discussed in the Emissions section.
## 1 Introduction and Backgrounds
To develop such DSS, we need to take the human cognitive point of view, specifically the human information processing and decision-making, as well as the constraints of the working environment, into consideration. These cognitive points of view and constraints can be represented as a set of information required by the decision-makers. The importance of a correct and complete set of information requirements is being increasingly emphasized throughout the years. Motivated by this, the objective of this paper is information requirements identification as the first step of DSS development for managing severe accidents. To the best of our knowledge, our approach is the first attempt to combine FRAM and decision ladder for systems development, including information requirements development, of DSS. FRAM and decision ladder complement each other, similar to the way system-based analysis complements task-based analysis [20]. FRAM inherits the main drawback of system-based analysis, i.e., it cannot analyze the internal aspects of individuals, such as their information-processing, knowledge, cognitive load, and so on. The decision ladder complements this drawback by allowing the analysis of an individual's information processing and other cognitive details while performing a certain task. The limitation of the decision ladder, as a type of task-based analysis, is that it can only be used to analyze predetermined tasks and cannot consider unpredictable events nor holistic view of the system. The analyst's ability to analyze the tasks/functions by following the FRAM modeling principles (performance variability and resonance) complements this limitation. Thus, the FRAM and decision ladder are mutually reinforcing. By combining both methods, we expect to develop a comprehensive set of information requirements for a DSS that considers the cognitive side of human decision-making and can cope with unpredictable events. The resulting set of information requirements is not only necessary but also a sufficient set of information requirements that, if satisfied, shall allow work adaptations under the broadest range of events [20].
## 2 Materials and Methods
### The BILLI System
Finally, efforts have been spent to improve the system portability and, in particular, to integrate existing data processing routines [42]. Summarizing, the main characteristics of the system are as follows:  
* the lidar is able to explore the troposphere in both vertical and horizontal directions;
* every lidar return is obtained averaging at least 50 shots ON and OFF (interlaced between them with t\({}_{\text{shift}}\) = 0.1 s);
* the temporal resolution of the lidar echoes (\(\Delta\)t) is about 10 ns, corresponding to a \(\Delta\)R of 1.5 m, although the actual time resolution of BILLI is 5 m: this latter value corresponds to the time response of the detection system and is linked to its bandwidth [39]);
* a concentration profile can be obtained in about 10 s by a couple of lidar signals (ON and OFF) using the newly designed mathematical technique explicitly developed for this application;
* starting from lidar signals of the same scan, it is possible to retrieve the dispersion map of in-plume CO\({}_{2}\) concentration (ppm) in the investigated area;
* knowing the (estimated) plume velocity, it is possible to obtain the carbon dioxide flux (Kg\(\cdot\)s\({}^{-1}\)) [21; 41; 42]; and
* starting from lidar profiles acquired successively, it is possible to track the motion of discrete atmospheric particles emitted by the volcanic crater, and this allows one to also estimate the wind velocity [38] (more details are reported in Section 3.2). However, a comprehensive overview of the special features of BILLI has been reported in previous works [21; 37; 38; 39; 40; 41; 42]. \begin{table}
\begin{tabular}{c c c c c} \hline  & **Pozzuoli Solfatara, October 2014** & \multicolumn{3}{c}{**Stromboli Volcano, June 2015 and Mt.**} \\  & & & **Etna, July 2016** & \\ \hline
**Mode** & ON & OFF & ON & OFF \\ \hline
**Wavelength [nm]** & 2012.194 & 2011.1 & 2009.537 & 2008.484 \\ \hline
**Wavenumber** & & & & \\
**[cm\({}^{-1}\)]** & & & & \\ \hline
**CO\({}_{2}\) Abs. Coeff.** & & & & \\
**[m\({}^{-1}\)]** & & & & \\ \hline
**CO\({}_{2}\)\(\Delta\sigma\) [m\({}^{-1}\)]** & & & & \\ \hline \end{tabular}
\end{table} 
Table 2: Wavelengths, wavenumbers, and CO\({}_{2}\) absorption coefficients calculated (at T = 296 K, P = 1 atm) during the field campaigns of Pozzuoli Solfatara, Stromboli volcano, and Mt. Etna.
# A Comprehensive Review of Indoor Localization Techniques and Applications in Various Sectors
## 3 Methodology and Application of Indoor Localization in Various Fields
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**References** & **Motivation** & **Indoor Localization Technology** & **Measurement Method** & **Application** \\ \hline \multirow{4}{*}{[14]} & To analyze security & & & \\  & holes in IoT-based & & & \\  & Wi-Fi indoor & Wi-Fi-based IoT system & & \\  & localization systems in & with edge nodes and & RSSI and triangulation & Tracking patients and \\  & healthcare settings and & cloud integration & & medical staff within \\  & propose mitigation & & & \\  & techniques. & & & \\ \hline \multirow{4}{*}{[17]} & To provide real-time & & & \\  & tracking of patients, & & & \\  & staff, and medical & BLE and iBeacon & RSSI and least square & \\  & assets using BLE and & technology & estimation approach & \\  & iBeacons in healthcare & & & \\  & environments. & & & \\ \hline \multirow{4}{*}{[26]} & To evaluate UWB and & & & Tracking goods, robots, \\  & BLE technologies for & & TOA and AoA for & \\  & indoor localization in & UWB and BLE & UWB; RSSI and AoA & \\  & industrial settings with & & for BLE & environments like \\  & dynamic environments. & & & warehouses \\  & & & & \\ \hline \multirow{4}{*}{[27]} & To review and provide & & & \\  & a comprehensive & Various technologies & Multiple methods: & \\  & overview of indoor & including BLE, Wi-Fi, & RSSI, triangulation, & \\  & positioning systems & RFID, UWB, & fingerprinting, & \\  & and their applications & and hybrid approaches & and hybrid techniques & \\  & in IoT. & & & \\ \hline \multirow{4}{*}{[36]} & To create a reliable & & & \\  & multi-technology & & & \\  & indoor positioning & system combining GPS, & & \\  & system for firefighters & LoRa (868 MHz and 2.4 & & \\  & and soldiers operating & & & \\  & in unknown & & & \\  & environments. & & & \\ \hline \multirow{4}{*}{[58]} & To improve the & & & \\  & accuracy of indoor & Hybrid model & & \\  & localization in critical & combining WLAN, & & \\  & situations using hybrid & dead reckoning (DR), & & \\  & localization & and smartphone & & \\  & technologies. & sensors & & \\ \hline \multirow{4}{*}{[59]} & To present a low-cost & & & \\  & indoor localization and & & & \\  & material tracking & Passive RFID with UHF & RSSI-based & \\  & method using passive & technology & & \\  & RFID for construction & & & \\  & projects. & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of indoor localization technologies and their applications across different domains.
## 3 Simulation Results
### Result of Pattern 2, Pattern 3 and Pattern 4
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Parameter/Method** & & **TSO** & **ITSO-INC** & **CS** & **INC** \\ \hline \multirow{3}{*}{Pattern 3} & Extracted power (w) & 6468.86 & 6470.66 & 6265.27 & 6044.33 \\  & Tracking efficiency (\%) & 99.97 & 99.99 & 99.94 & 96.82 \\  & Tracking time (s) & 0.272 & 0.095 & 0.188 & 0.022 \\ \hline \multirow{3}{*}{Pattern 4} & Extracted power (w) & 4352.33 & 4701.45 & 4702.98 & 4489.89 \\  & Tracking efficiency (\%) & 92.09 & 99.48 & 99.51 & 95.00 \\  & Tracking time (s) & 0.141 & 0.054 & 0.269 & 0.024 \\ \hline \multirow{3}{*}{Pattern 5} & Extracted power (w) & 5110.89 & 5114.33 & 5106.86 & 4672.36 \\  & Tracking efficiency (\%) & 98.50 & 98.56 & 98.41 & 91.08 \\ \cline{1-1}  & Tracking time (s) & 0.145 & 0.106 & 0.267 & 0.006 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Numerical results of TSO, ITSO-INC, CS, and INC methods in pattern 3 and pattern 4.  
fastest tracking speed and the highest algorithm efficiency among the algorithms that could estimate zero steady state.
# Solving the Fuzzy Transportation Problem by a Novel Particle Swarm Optimization Approach
## 3 Fuzzy Logic Definitions
### Generalized Fuzzy Numbers  
In our comprehensive investigation of the TP involving a broad spectrum of fuzzy numbers, the exploration of generalized fuzzy numbers comprises an indispensable aspect of our research. [PERSON] and [PERSON] [16] had the idea of implying the generalized fuzzy numbers in the TP using exact algorithms. In our study, in some of the presented problems the transportation cost will be illustrated by generalized fuzzy numbers. Our basic aim is to assess their effectiveness following the implementation of TrigAC-PSO algorithm.  
But how are generalized fuzzy numbers defined? Generalized fuzzy numbers extend the concept of classical fuzzy numbers by providing more specific details through additional parameters.  
In [18], [PERSON] represented a generalized trapezoidal fuzzy number \(\overset{\sim}{A}\) as \(\overset{\sim}{A}=(a,b,c,d;w)\), where \(0<w\leq 1\) and \(a,\ b,\ c,\ d\) are real numbers.  
According to [PERSON], the membership function of a fuzzy set \(\overset{\sim}{A}\) has the following properties.  
* \(\mu_{\overset{\sim}{A}}:\mathbb{R}\rightarrow[0,\ w]\) is continuous;
* \(\mu_{\overset{\sim}{A}}(x)=0\) for all \(x\in(-\infty,\ a]u[d,\ \infty)\);
* \(\mu_{\overset{\sim}{A}}(x)\) is strictly increasing on \([a,b]\) and strictly decreasing on \([c,d]\);
* \(\mu_{\overset{\sim}{A}}(x)=w\), for all \(x\in[b,c]\) where \(0<w\leq 1\).  
**Definition 5**.: _A fuzzy number \(\overset{\sim}{A}=(a,\ b,\ c,\ d;w)\) is said to be a generalized trapezoidal fuzzy number if its membership function is provided by_  
\[\mu_{\overset{\sim}{A}}(x)=\left\{\begin{array}{ll}w.\frac{x-d}{b-a},\ a\leq x <b\\ w,\quad\ b\leq x\leq c\\ w.\frac{x-d}{c-d},\ c<x\leq d\\ 0,\ otherwise\end{array}\right.\]
# An Aquatic Mobile Sensing USV Swarm with a Link Quality-Based Delay Tolerant Network
## 3 Proposed Architecture
### Architecture Overview  
The aquatic monitoring platform is a part of a larger city-wide architecture described by [PERSON] et al. [16] and illustrated in Figure 2. This platform contains heterogeneous elements, such as  
Figure 1: UUV Simulator: Software structure inspired by [PERSON] et al. [31].  
Data Collecting Units (DCUs), cars, bicycles, aerial drones and USVs. The aquatic platform is integrated in the more general one, but has specific requirements which require an extended architecture:  
* Heterogeneous mobile nodes (USVs), with only short range communication (e.g., Wi-Fi) or also long range communication (e.g., LoRa);
* Collection and dissemination of environmental data from the short range communication mobile nodes to the long range ones (mobile gateways);
* Fallback support of mobile aerial gateways if USVs are isolated; and
* Monitoring of an entire tank with minimal cost.
# Implementation of a Lossless Moving Target Defense Mechanism
## 4 Moving Target Defense
MTD is a cybersecurity strategy classified as a dynamic strategy, aimed at actively protecting computer systems, networks, and data by continuously changing the parameters that constitute the so-called attack surface. The attack surface is the space that attackers must explore or reconstruct to determine the configuration of the target system before initiating the actual attack. It comprises all the points through which attackers can enter the system, indicating which components can be exploited. MTD is an extremely broad concept, encompassing virtually all levels of networks and computer systems:  
* At the data level, where the data format or representation can be changed;
* At the application level, considering dynamic changes to application code, such as during compilation;
* At the runtime environment level, dynamic changes may include random memory allocation for storing critical data;
* At the operating systems level, utilizing dynamic changes in instruction sets or entire operating systems;
* At the hardware level, involving techniques related to memory, processors, and networks. The first two techniques can utilize methods proposed for operating systems. Techniques used for dynamic changes are much broader and can include, among other things, changes to layer 2 and/or layer 3 addresses, TCP and UDP protocol port numbers, protocol types, and other network parameters. Changes can be made periodically or randomly. It is also possible to use decoys in the form of servers or stub networks, where the detection of events unrelated to the standard network operations triggers MTD mechanisms. MTD confuses attackers, making it difficult for them to establish a foothold and exploit vulnerabilities in the system. Implementing MTD mechanisms provides an advantage over attackers by forcing them to confront a constantly evolving and challenging target, thereby increasing the complexity and cost of attacks. This dynamic state is not present in traditional networks, which are characterized by static security measures stemming from fixed configurations and patterns.