esa-sceva/satcom-chunk-collection huggingface dataset:
# Breath Analysis: A Promising Tool for Disease Diagnosis--The Role of Sensors
## 2 The Role of Nanomaterials
### Metal Oxide Semiconductors (MOS)  
MOS are commonly used in different sensor types as sensing materials, for oxidizing or reducing gas detection. Transition MOS (e.g., NiO, Cr\({}_{2}\)O\({}_{3}\)) are more efficient for gas sensing applications than the non-transition MOS (ZnO, SnO\({}_{2}\)), due to more than one preferred oxidation states [7]. Increased affinity of MOS for negatively charged oxygen species (e.g., O\({}_{2}\)\({}^{-}\), O\({}^{-}\)), in contrast to compound semiconductors (e.g., GaAs) [53], leads to the creation of surface-trapped charge density; in this way electron depletion layers are formed in n-type semiconductors while hole accumulation layers are formed in p-type semiconductors [7; 53]. Gas interaction with the oxygen species alternates the surface charge density, resulting in a resistance change [7]. Specifically, oxidation of reducing gases by the adsorbed oxygen species leads to electron transfer towards the semiconductor surface. The adsorption of oxidizing gases, on the other hand, leads to the removal of electrons from the semiconductor surface [53].  
N-type MOS, e.g., SnO\({}_{2}\), ZnO, WO\({}_{3}\), TiO\({}_{2}\), MoO\({}_{3}\), In\({}_{2}\)O\({}_{3}\), and Fe\({}_{2}\)O\({}_{3}\), as well as p-type MOS, such as Co\({}_{3}\)O\({}_{4}\), CuO, NiO, Cr\({}_{2}\)O\({}_{3}\), and Mn\({}_{3}\)O\({}_{4}\), have been used in gas sensors for disease diagnosis [53]. MOS sensitivity is affected by numerous parameters, such as morphology, porosity, particle size, film thickness and doping of MOS, decoration with noble metals, as well as operation temperature [54]. Consequently, various MOS based gas-sensors of different structures (villi-like [55], nanotubes (NTs) [56], Hemi tubes [57], hierarchical fibers [58], nano-sheets [59], or NWs [60]), usually combined with noble MNPs [56; 57; 58; 59], 2D-materials [57], or MOS nanoparticles [59; 60] have been investigated as diagnostic tools for a variety of diseases. Notably, in the case of semiconducting nanomaterials, CNTs, graphene-based nanomaterials, and MOS have exhibited the highest response towards acetone, thus holding great promise for diabetes diagnosis [53]. However, despite their enticing applications MOS possess important limitations including high temperature operation [13] (150-500 \({}^{\circ}\)C) and confinement to single gas detection, due to lack of selectivity towards polar-nonpolar compounds [7].
# ICN-Oriented Mobility Support Method for Dynamic Allocation of Mobile Data Flows
## 2 Related Work
### Mobility Support in IP Networks
The LMA is divided into a Control Plane Local Mobility Anchor (CLMA) and a Data Plane Local Mobility Anchor (DLMA). The CLMA realizes the selection of the DLMA for the MN and avoids the single anchor problem. However, this method still has problems related to triangular routing and tunnel overhead. In general, most protocols that use anchors to solve mobility in traditional IP networks have problems, such as triangular routing, single points of failure, and high tunnel overhead. For mobility protocols that completely separate the dual functions of IP addresses, they rely on the host to process mobile signaling. In addition, end-to-end mobile signaling notification propagation will cause a large handover delay.
## 3 Results
### Model Selection  
#### 3.2.1 Regression  
See Table 6 for a summary of the LOOCV, associated feature sets and performance following training across all 20 training patients. PCA features were not found to minimise MSE for any PG, therefore all chosen models use raw features. Of the 27 features considered during modelling, 16 were among the final models. Of these 16, 8 were volumetric, 3 spatial and 5 derived. The spatial feature 'distance from the centre of PTV48 to the centre of the rectum' was used in four of the six PG models. Six features were each present in two PG models including 'volume of the rectum', 'volume of PTV48', 'distance from the centre of the bladder to the centre of the rectum,'rectum \(\text{VIF}_{\text{PTV48}}\)', 'ratio of the bladder to the rectum' and'slope between \(\text{OV}_{\text{rectum,PTV48}_{\text{B,2 cm}}}\) and \(\text{OV}_{\text{rectum,PTV48}_{\text{B,4 cm}}}\)'.  
The model formations were strong for PTV dose falloff, rectum D\({}_{\text{max}}\) and bladder D\({}_{\text{max}}\) with mean adjusted R\({}^{2}\) greater than 0.990. The quality of the models were reduced, but still adequate, for rectum D\({}_{\text{mean}}\), bladder D\({}_{\text{mean}}\) and PTV conformality, with R\({}^{2}\) between 0.835 and 0.907.
# Underwater Wireless Sensor Networks Performance Comparison Utilizing Telnet and Superframe
## 3 Background on Communication Protocols
### Optimized Link State Routing Protocol (OLSR)  
OLSR (optimized link state routing) is a prominent type of dynamic routing technique. Frequent transmissions of routing information from each network node ensure that all network nodes have the same comprehensive perspective. The protocol incurs significant extra expense due to its periodic structure. The problem is resolved since OLSR limits the amount of traffic that can be transferred. To achieve this purpose, multi-point relays (MPRs) are used; these devices are in charge of relaying and routing messages. Each node selects MPRs from among its neighboring nodes. It is advised that for optimal performance, a node choose MPRs that allow it to connect with at least one neighbor that is reachable over a hop-and-a-half path. MPRs are in charge of relaying control traffic generated by other nodes [19].
## 2 Business Concept with the Developed IoT-Based Gas Level Sensor  
### Conceptualization  
This solution is based on the needs of an LPG distribution company. According to the company, the developed IoT-based gas level sensor (from now on, this will be referred to as IoT-GL-Sensor for simplicity) will be delivered to customers free of charge, so they have no problems installing it in their tanks.  
The philosophy of \"design thinking\" was followed during the conceptualization of the device, which consists of taking into account the experiences of the company and understanding the problem to be solved. In joint meetings, considerations were presented and the functions of the electronic device were determined in order to obtain a product that meets the needs of the gas distribution company. The considerations and functions that the IoT-GL-Sensor must have are summarized below.  
1. Based on the experience acquired by the gas company with residential users, it is known that 85% of them have stationary LPG tanks in a horizontal position with a capacity between 100 and 500 L, another 10% of residential customers have stationary tanks with a capacity between 1000 and 2200 L, and the remaining 5% of users have tanks with a larger capacity.
2. Domestic stationary tanks are filled to a maximum of 80% for safety reasons.
3. The period between recharges depends on the level of use and the size of the stationary tank. However, according to the company's experience, the average minimum time is around 7 weeks.
4. The IoT-GL-Sensor will be provided free of charge to customers so that they do not object to its use.
5. It is desirable that the device uses Wi-Fi communication, as 100% of customers use Internet connectivity.
6. The IoT-GL-Sensor displays both the user and the gas company the percentage of the LPG level in the tank, through an app and a web platform, respectively.
7. When installing and configuring the sensor for the first time, the user will be prompted to provide the exact location of the stationary tank using Google Maps on a mobile device. The tank location is stored in the server warehouse. In this way, the gas company can offer the filling service and reduce unfair competition, such as the illegal distribution of stolen gas [4; 5; 6].
8. LPG distribution tanker trucks must be equipped with a GPS system, as shown in Figure 2. The GPS system tracks the trucks in real time, to verify the distribution route and eventually prevent vehicle theft [4; 14].
## 2 Wideband Antenna Designing
### Results of Unit Elements
#### 2.2.2 Gain  
Figure 6 shows a comparison of the proposed antenna's prototype gain and software-predicted gain. At the resonance frequency of 30 GHz, the antenna delivers a peak gain of >8 dB. It may be noted from the figures that the antenna delivers gain >6.5 dBi at an operational bandwidth of 26.5-43.7 GHz. The suggested antenna is the best candidate for future 5G devices using wideband and high gain because of the great agreement between predicted and tested outcomes.
## 2 Related Work
More specifically, the DoS class had comparatively poor detection rates across all tested models. The CIDDS-001 dataset employed in [24] utilizes the AttackType label to train machine learning algorithms, including RF, MLP, and LSTM, to accurately categorize a network flow as either benign or as displaying DoS, Brute Force, Ping Scan, or Port Scan attacks. The models employed were implemented from two separate perspectives: single-flow and multi-flow. The outcomes were compared to ascertain which model was more appropriate for the specific case. From the single-flow perspective, the models exclusively focused on individual flow characteristics, with RF attaining the highest performance, evidenced by the F1-score of 85.04%. Conversely, the multi-flow perspective examined both the distinct characteristics of individual flows and the short- to long-term interrelations among the flows within a certain sequence. The LSTM model achieved the optimal F1-score of 91.66% with a sequence size of 70. Based on the summary presented in Table 1, we can make the following assumptions:  
* One of the biggest shortcomings is the situation in which one or more classes, known as minority classes, are significantly less frequent than other classes, known as majority classes. This will result in a distortion of the decision border, hence increasing the classification error for high-value attack data. * The classification approaches proposed by the other authors are not oriented to correctly classify prioritized classes; instead, all multi-class classes are combined in two classes (normal and abnormal), and binary classification approaches are used to differentiate between normal and abnormal behavior,
* A new method needs to be proposed if we are3 to create an ML model that is oriented towards better recognition of the important classes that have been prioritized. These assumptions guided us in the development of the proposed method to optimize a machine learning model for improving the recognition of prioritized classes using the AHP. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
\begin{tabular}{c} Binary \\ classification \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A list of state-of-the-art research papers on imbalanced multi-class dataset classification methods.
# Design and Testing of an Intelligent Control System for Maize Picking Harvest
## 2 Materials and Methods
### System Description
#### 2.1.2 System Hardware Composition  
Figure 2 shows a structural block diagram of the hardware components for the intelligent control system of the maize harvester. The intelligent control system comprises sensors, the intelligent controller, actuators, and hydraulic control elements. The controller was constructed with the programmable controller IMC T3654, which was specially designed for mobile machinery with a flexible input/output (I/O) port configuration and up to 90 ports. Inputs to the harvester control system include the rotational speed of the pulling rollers, operating speed, header height, maize picking loss, and button control signal (from a keyboard and handle). The maize picking loss sensor can obtain the maize picking  
Figure 1: Schematic diagram of intelligent control system.  
loss rate by collecting image information of broken ears and sending it to the intelligent controller through the control area network (CAN) bus. The rotational speed sensor collects the rotational speed information of the pulling rollers. The speed sensor collects the vehicle speed information. The header height sensor detects the header height. These sensors send their information to the controller in real time. The keys and driving control handle are located on the armrest box assembly, which can send signals by the CAN bus to the controller according to the inputs of the operator in manual control mode. The sensor signals are processed by the controller, which then outputs the appropriate command signal to the actuators to adjust the cylinders or motors.
# Multi-Robot Exploration Employing Harmonic Map Transformations
## 4 Results
### Multi-Robot Results  
The suggested multi-robot method was tested in Gazebo using the same simulated workspace as in the single robot simulation scenario. Three TurtleBot3 Burger robots were used. Multiple simulations were performed to reduce randomness in the results. The mean values and standard divination of ET, PD, and total PD are presented in Table 1. The  
Figure 8: Some of the HMT’s for Exploration calculated online during the exploration experiment. Top left is the initial transform and bottom right is the final when the workspace is considered fully explored. The boundaries are shown with a black line, the frontiers with a green line and their mapping on the HMT is shown as a red circle. The mapping of the inner boundaries on the HMT is shown with a red dot.  
Figure 9: The path taken by the robot, in blue, to explore the workspace, whose boundary is denoted with a black line.  
simulation is designed to replicate the methodology outlined in the simulation scenario 1 from [PERSON] et al. (2015), facilitating a direct comparison of results between our method and theirs. The comparative results are shown in Table 2.  
The paths produced by the robot are visualized along with the workspace in Figure 10. The final maps generated by SLAM and the map merging process is presented in Figure 11. Notice that our method outperforms the other in terms of path length, exhibiting slightly longer execution than the best one.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **ET (s)** & **PD Robot 1** & **PD Robot 2** & **PD Robot 3** & **Total PD (m)** \\ \hline Mean Values & 168.75 & 15.10 & 23.86 & 17.54 & 56.50 \\ Std Dev & 1.2698 & 1.9450 & 2.3356 & 1.1087 & 9.1234 \\ \hline \end{tabular}
\end{table}
Table 1: HMT for Exploration Simulation Results.  
Figure 10: The path taken by the robots to explore the workspace. Robots with priority numbers 1, 2, and 3 are depicted with blue, orange, and green, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **ET (s)** & **Total PD (m)** & **PD (m)** & **Mean-PD** & **Std-PD** \\ \hline M-RRTs ([PERSON] and [PERSON], 2016) & 244.21 & 110.30 & [32.49, 25.38, 52.43] & 36.7667 & 14.0229 \\ RRT-GFB ([PERSON] and [PERSON], 2016) & 234.49 & 107.82 & [27.41, 34.87, 45.54] & 35.9400 & 9.1122 \\ RRT-BFS ([PERSON] and [PERSON], 2016) & 202.10 & 97.34 & [26.28, 30.47, 40.59] & 32.4467 & 7.3569 \\ E-RRT ([PERSON] and [PERSON], 2016) & 219.07 & 102.89 & [29.70, 27.52, 45.67] & 34.2967 & 9.9097 \\ ID-RRT ([PERSON] and [PERSON], 2016) & 194.33 & 86.26 & [17.69, 25.01, 43.56] & 28.7533 & 13.3351 \\ HMS-RRT ([PERSON] and [PERSON], 2016) & **144.18** & 67.28 & [21.33, 16.43, 29.52] & 22.4267 & 6.6135 \\ \hline HMT & 168.75 & **56.50** & **(15.10, 23.86, 17.54)** & **18.8333** & **4.5209** \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of Multi-Robot Exploration Simulated Experiments.
# Pattern Recognition in Dried Milk Droplets Using Lacunarity and Deep Learning
In the context of dried droplets and milk adulteration detection, lacunarity analysis presents a promising approach to explore new patterns that could serve as valuable biomarkers and enhance quality control mechanisms for milk. Recent studies focusing on pattern analysis of dried droplets have made significant contributions to the detection of adulteration in milk. However, differentiating among various types of milk, such as whole or lactose-free, remains elusive. Identifying new patterns in deposits could potentially lead to the discovery of biomarkers, thus enhancing the quality control mechanisms for milk. Despite significant advancements in milk adulteration detection, an important gap remains unaddressed: current methods primarily focus on identifying specific contaminants such as starch, urea, or spoilage-related alterations, but fail to effectively differentiate between different milk types. Moreover, while salts have been recognized as key modulators in droplet pattern formation, their potential role in enhancing classification accuracy in milk analysis has been largely unexplored. Deep learning approaches have demonstrated high accuracy in identifying adulteration [34], yet they often lack interpretability and require extensive training datasets. In contrast, lacunarity analysis provides a robust quantitative framework for characterizing pattern complexity, offering a potentially more interpretable and scalable approach for milk classification. Addressing this gap, our study investigates how the addition of NaCl influences the formation of dried droplet patterns and evaluates whether NaCl-enhanced morphology improves the reliability of lacunarity analysis, particularly in the context of adulteration detection. In this study, we present a novel approach for differentiating whole milk from lactose-free milk and detecting water adulteration in each of these types of milk, through the analysis of dried droplets. The key innovation lies in the controlled addition of NaCl, which modulates the crystallization process and enhances the structural differentiation, especially in adulterated samples where intrinsic morphological differences are subtle. By leveraging lacunarity analysis, we quantitatively characterize the complexity of these structures and evaluate their discriminatory power using ROC curve analysis. Our results demonstrate that lacunarity alone achieves high classification accuracy (\(95.04\%\pm 6.66\%\)), while the integration of deep learning further improves performance (\(95.22\%\pm 4.47\%\)). Notably, the highest accuracy is achieved with \(2\%\) NaCl, where lacunarity reaches \(97.08\%\pm 2.27\%\) and deep learning \(96.88\%\pm 2.8\%\), indicating enhanced precision and stability. Our findings support NaCl-enhanced lacunarity analysis as a robust and interpretable methodology for detecting adulteration and, to a lesser extent, for classifying milk types when intrinsic differences are insufficiently distinctive.
## 2 Materials and Methods
### Application Efficiency  
The original equation proposed for evaluating application efficiency (Ea) in stationary sprinkler irrigation systems is Equation (1) [2]:  
\[\text{Ea }=\text{ Oe }\times\text{ Re }\times\text{ DUpa} \tag{1}\]  
where Oe: losses due to leaks in pipelines, which are here considered equal to 1; Re: losses due to droplet evaporation and drift; DUpa: distribution uniformity for the percentage of the adequately irrigated area; all of these are the dimensionless components.  
The equations used to calculate each of the Ea components were taken from [2]:  
\[\begin{array}{c}\text{Re}=0.976+0.005\times\text{ETo}-0.00017\times\text{ ETo}^{2}+0.0012\times\text{WS}-\text{CI}\times(0.00043\times\text{ETo}\\ +0.00018\times\text{WS}+0.000016\times\text{ETo }\times\text{WS})\end{array} \tag{2}\]  
where ETo: reference evapotranspiration (mm day\({}^{-1}\)); WS: wind speed (m s\({}^{-1}\)); CI: coarseness index proposed by [2]:  
\[\text{CI}=0.032\times\frac{\text{Pa}^{1.3}}{\text{dn}} \tag{3}\]  
where Pa: sprinkler operating pressure (kPa); dn: nozzle diameter (mm). Equation (3) is valid for \(7\leq\text{CI}\leq 17\); if CI \(<7\), then CI = 7 and if CI \(>17\), then CI = 17. The operating pressure used was determined for the pressure regulators of each sprinkler.  
\[\text{DUpa}=100+\left(606-24.9\times\text{pa}+0.349\times\text{pa}^{2}-0.00186 \times\text{pa}^{3}\right)\left(1-\frac{\text{CUC}}{100}\right) \tag{4}\]  
where pa: percentage of adequately irrigated area; CUC: [PERSON]'s uniformity coefficient. In this work, CUC was replaced by the Heermann and Hein coefficient of uniformity (HHCU) since this uniformity indicator is specific for center pivot systems.  
\[\text{HHCU}=100\left[1-\frac{\sum_{1=1}^{n}\lvert V_{i}-V_{MP}\rvert S_{i}}{ \sum_{i=1}^{n}V_{i}S_{i}}\right] \tag{5}\]  
where \(n\): number of catch cans; \(i\) is a number assigned to identify a particular catch can beginning with \(i=1\) for the catch can located nearest the pivot point and ending with \(i=n\) for the most remote catch can from the pivot point; \(V_{i}\) is the depth collected in the \(i\)th catch can (mm); \(S_{i}\) is the distance of the \(i\)th collector from the pivot point (m); \(V_{MP}\) is the weighted mean of the amount of water caught (mm).  
Weather records were obtained every 5 min from a meteorological station located 1000 m from the irrigation system. To determine Re, the pivot operated during daylight hours, without crop and by applying four irrigation depths (40, 24, 12 and 6 mm). ETo was obtained using the FAO equation [24] and mean or accumulated values corresponding to each irrigation event were calculated.  
Two novel features were incorporated into the original equation (Equation (1)), the components of irrigation losses due to partial residue retention (PRR) and leaf interception (LI).
## 2 Related Works
### Application Layer Related Works  
Application layer in WVSN consists of complex items with regards to WVSN specifications. QoS preservation and computation reduction in video based sensor networks are instances of these complexities. Two methods of application admission controls are presented in [15] and [16]. In [15], the aim was to increase the lifetime of the network subject to the bandwidth and reliability. In [16], admission defining depends on added energy load and application reward. Although the authors consider application level QoS, they do not simultaneously meet multiple QoS requirements (e.g. delay, reliability, and energy consumption), as required in WMSNs. Moreover, their communication optimizations need high in-network computation, which is not compatible with WVSN constraints.  
Source coding is also one of application layer's services. In view of limitations of VSNs and needs of multimedia transmission, coding techniques should have high ratio of compression, low complexity and error resiliency [2]. However, traditional compression and coding like MPEG, H.263 [17] or H.264 [18] have complex encoders which do not provide energy efficiency. A group of coding is distributed source coding [19] and [20] which use a lot of energy to fuse captured images.
# Collecting Sensed Data with Opportunistic Networks: The Case of Contact Information Overhead
## 3 A Model for Contact Information Overhead
### Impact of Contact Information Size on Message Forwarding  
Here, we model the impact of contact information size on message forwarding in terms of delay (i.e., the amount of time remaining for forwarding messages after exchanging contact information) and energy consumption (i.e., the amount of energy required to send and receive this information per encounter).