esa-sceva/satcom-chunk-collection huggingface dataset:
# Cellulose and Vanadium Plasmonic Sensor to Measure Ni\({}^{2+}\) Ions
## 4 Conclusions  
A novel Au/MPA/V-CNCs-CTA thin-film-based SPR sensor was successfully utilized for Ni(II) ion detection ranging from 2 to 50 ppb. The proposed sensor displayed excellent detection at a low ppb-level compared to other reported work so far. The sensor also exhibits a maximum sensitivity of 0.068\({}^{\circ}\) ppb\({}^{-1}\) as well as a strong binding affinity, determined from Sips isotherm model calculation. Importantly, this proposed sensor successfully achieved a better detection limit, sensitivity, binding affinity as well as high DA and SNR measurements. The Au/MPA/V-CNCs-CTA thin-film-based SPR sensor is hence proposed to have high potential in the detection of Ni(II) ions in real samples and other related applications.  
Conceptualization, methodology, writing--original draft preparation, N.A. S.O.; validation, supervision, writing--review and editing, Y.W.F.; supervision, funding acquisition, project administration, I.R.; supervision, resources, J.A. and M.A.M.; writing--review and editing, visualization, U.Z.M.A. and H.S.H. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Fundamental Research Grant Scheme of the Ministry of Education Malaysia (No. FRGS/1/2019/STG02/UPM/02/1) and Putra Grant (GPPI 9476700).  
Data Availability Statement: Data sharing not applicable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ni(II) Ion Concentration (ppb)** & **FWHM (Degree)** & **DA (Degree\({}^{-1}\))** & **SNR** \\ \hline
0 & 3.3299 & 0.3003 & 0 \\
2 & 3.2952 & 0.3034 & 0.0276 \\
4 & 3.4513 & 0.2897 & 0.0810 \\
6 & 3.5033 & 0.2854 & 0.1317 \\
8 & 3.6767 & 0.2719 & 0.1503 \\
10 & 3.5727 & 0.2799 & 0.1802 \\
20 & 3.9195 & 0.2551 & 0.1874 \\
50 & 3.6420 & 0.2745 & 0.2303 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Full width at half maximum (FWHM), detection accuracy (DA), and SNR of Au/MPA/V-CNCs–CTA thin film with respect to Ni(II) ion concentrations.  
Figure 7: Signal-to-noise-ratio of Au/MPA/V–CNCs–CTA thin film when detecting Ni(II) ion solution ranged from 2 ppb to 50 ppb.  
**Acknowledgments:** The laboratory facilities provided by the Department of Physics, Department of Chemistry, Wireless and Photonic Network Research Center, Institute of Advanced Technology, Universiti Putra Malaysia, are acknowledged.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# An Evaluation of Image Slicing and YOLO Architectures for Object Detection in UAV Images
## 3 Materials and Methods
### Object Detection Model
#### 3.3.2 YOLOv8  
YOLOv8 uses CSPDarknet53 as its backbone; this is a deep neural network that extracts features at various resolutions by progressively subsampling the input image [7]. Feature maps produced at different resolutions contain information about objects at various scales in the image, covering different levels of detail and abstraction in object detection tasks. YOLOv8 is capable of combining different feature maps to gain information about object shapes and textures, which generally provides high accuracy in object detection tasks.  
The backbone of YOLOv8 consists of four parts, each containing a single convolution followed by a c2f module. The C2f module provides a new introduction to CSPDarknet53 [7]. This module consists of sections, one end of which passes through the bottleneck module (having two 3 \(\times\) 3 convolutional residual connections). The output of the bottleneck module is divided by the number N corresponding to the YOLOv8 model size. All of these sections are finally combined and passed through a final convolution layer. These final layer activations represent the output of the model. The general structure of the YOLOv8 algorithm can be seen in Figure 6.  
Figure 5: The network structure of the YOLOv7 algorithm.
# Mobility Management of Unmanned Aerial Vehicles in Ultra-Dense Heterogeneous Networks
## 2 UAV Technology in Wireless Communication
### UAV Communication Scenarios
#### 2.4.1 Flying Base Stations  
A flying BS that connects backhaul and access networks can be a UxNB. The so-called fly ad-hoc network (FANET) is formed when more than one UAV is included in a transmitting apparatus. FANETs are air-borne frames for remote wireless ad hoc networks (MANETs) or mobile ad hoc networks (MANETs). An innovative aspect of the 5G network is \"network from the sky\". UAV have the ability to provide on-demand systems to specific regions due to their built-in mobility features, flexibility in three-dimensional space, adaptive elevation, and symmetric revolution. Ground users can benefit from premium services such as high-quality wireless connections, seamless connection, large data capacity, and low degradations thanks to these unique characteristics. UAV integration with distant cellular systems serving as aerial communication platforms will open up previously unconsidered foundations, new perspectives, and numerous possibilities [44].  
When compared to their earthly counterparts, several differences are unquestionably present. The average height of earthbound BSs in an urban setting is about 10-20 m. UAVs can hover up to 100-120 m. This allows the UAV to have a longer range than traditional terrestrial BS s, further reducing interference from nearby terminals. Ground terminals are easily visible from various measured altitudes and points with the UAV. UAVs can track users in 3D with high mobility. Traditional ground-to-ground communications suffer from higher route loss attenuation and blurring. UAV s can provide a better LoS channel probability. In such situations, a few key areas must be considered. Millimeter waves (mm-wave), for instance, are used in 5G systems. LoS is essential for delivering high recurrent transmission capacity to the network. Since the LoS condition allows for effective beamforming in 3D space, UAVs are good candidates for 3D Multiple Input Multiple Output (MIMO). The idea of using UAVs as BSs is represented in Figure 4.  
Figure 3: UAV connection via side lobes in a future ultra–dens heterogenous network.
## 6 Conclusions  
The objective of this survey was to obtain insight into various MAC protocols designed for VANETs. This paper presents a brief introduction to VANETs along with its challenges and applications. The protocols are classified into distributed, centralized, cluster-based, random-access, and virtual token-based and sub-classified into single-channel and multi-channel MAC protocols. The identified primary challenges in the design of MAC protocol are latency, dynamic topology, mobility of nodes, reliability, and bandwidth utilization. This paper also briefly elaborates on various DSRC-based and cellular-based networks, giving a gist of the ITS-G5 standard, LTE, 5G, and C-V2X communication. Furthermore, Table 3 presents a structured, detailed review of recent MAC protocols along with their mechanisms, objectives, comparisons, and classifications.  
An efficient MAC design is of utmost importance to providing delay-intolerant and reliable messaging. Data packets are prioritized based on safety and non-safety applications and disseminated based on priority. With multi-channel protocols, multiple channels can handle interference caused by channel access, resulting in better channel utilization. A multi-channel clustering mechanism with dynamic interval-based channel access is an open challenge for MAC protocols to provide QoS and to improve network performance. Technologies such as SDN, which separates the data plane from the control plane, have also emerged to eliminate traffic congestion. Edge-computing vehicles, VANETs based on the Internet of Things, and cloud-based VANETs are all emerging technologies for reliable and efficient transportation. Multi-transceivers, multi-channel operations, and multiple inputs and outputs (MIMO) are some methods for enhancing the network throughput. Futurework includes experimental analyses of the MAC protocols in VANET in terms of QoS metrics such as throughput, packet reception ratio, energy consumption, and performance improvements using an efficient MAC design.  
**Author Contributions:** This work was completed with contributions from all the authors. conceptualization, L.H. and A.K.; methodology, L.H. and A.K.; software, L.H., B.P.N. and A.K.; validation, formal analysis, data curation and writing--original draft preparation, L.H., A.K., and G.G.M.N.A.; writing--review and editing, L.H., A.K., G.G.M.N.A. and P.H.J.C.; visualization, L.H., and B.P.N.; funding acquisition, P.H.J.C. All authors did edit, review and improve the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** Not applicable, the study does not report any data.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Experimental Design and Experimental Conditions  
For a comparative test of the two carrier types, laminates and modules, an incubation experiment in mesocosoms was performed (Figure 4). Lake water was sampled from the eutrophic Lake Skarlinskiie (NE Poland) in April and transported to the laboratory. The lake water was further poured into plastic chambers (diameter of 57 cm, height of 81 cm) (Figure 4). Each chamber was filled with 180 L of the lake water and left without any intervention for about 6 days to stabilize. After 6 days, modules and laminates were immersed or suspended in chambers and further incubated for 42 days. Two laminate strips were used, with a total surface area of 1500 cm\({}^{2}\) (15 \(\times\) 50 cm each) and a total mass of calcite of about 3.6 g (Figure 4). Modules consisted of three trays with the total surface area of 740 cm\({}^{2}\) (13 \(\times\) 19 cm each), filled with calcite with the total mass of 300 g and covered with mesh to prevent material loss. Chambers without any additions served as control. The whole volume of water in each chamber was mixed twice a day. Incubation took place without light access. During the experiment, the temperature in the laboratory hall was 8.0-13.5 \({}^{\circ}\)C. The experiment was conducted in duplicates.  
At the beginning of the experiment, the concentration of total phosphorus in the lake water was 0.63 mg L\({}^{-1}\), of which 0.16 mg L\({}^{-1}\) was OP (as P-PO\({}_{4}\)). The concentration of total nitrogen was 0.4 mg/L, of which only a small portion (0.024 mg L\({}^{-1}\)) was inorganic nitrogen. The water was characterized with alkaline pH (7.9), high oxygen concentration (10.5 mg O\({}_{2}\) L\({}^{-1}\), saturation of 95%) and relatively high content of chlorophyll \"a\" (17 ug/L), showing primary production already taking place during sampling for the experiment.  
During the experiment, the lake water properties were monitored in each mesocosom every 3 days in the first week of the incubation and once a week during the remaining experiment duration. Temperature, conductivity, and pH were measured in situ at the depth of 30 cm. For the other parameters, including concentration of the OP, calcium, and alkalinity, the water samples were taken from the depth of 30 cm. Determination methods for all the parameters monitored are described in detail in Section 2.4. The change in total water volume at the end of the experiment due to sampling was about 3%.
# Man-in-the-Middle Attacks in Mobile Ad Hoc Networks (MANETs): Analysis and Evaluation
## 2 Related Work  
In this section, we review some studies on MITM assaults. [PERSON] et al. [11] investigated a mathematical model for MITM assaults on SSL protocols in wired technology. [PERSON] et al. [12] proposed the classification of HTTPs by categorizing the assailants into four layers, i.e., vulnerability, behavior, target, and state. [PERSON] et al. [13] provided a review about MITM assaults on OSI layers based on two mobile communication technologies, i.e., UMTS and GSM. [PERSON] et al. [14] investigated the influence of MITM assaults on the MAC layer for wireless communications. [PERSON] et al. [15] investigated MITM assaults through WiFi technology.  
A large amount of research [16; 17; 18; 19; 20; 21; 22; 23; 24] has been performed to address MITM assaults in a vehicular ad hoc network (VANET). [PERSON] et al. [16] proposed a security protocol based on elliptic curve cryptography for signing messages and verifying signatures in order to avoid the occurrence of MITM assaults. [PERSON] et al. [24] proposed a hybrid approach that secures communication between nodes in order to avoid MITM assaults.  
Recently, [PERSON] et al. [25] proposed a key exchange-based Diffie-Hellman protocol to secure MITM attacks in MANET. [PERSON] et al. [26] used artificial neural network (ANN)-based predictive techniques for detecting and preventing MITM assaults in MANET.  
Evidently, MITM assaults are severe in MANET, and there is no research that directly analyzes and evaluates a comparison of the two modes of MITM assaults. To close this gap, we have constructed and assessed two kinds of MITM assaults in the MANET as part of our simulation-based research.  
Figure 1: Illustration of the mobile ad hoc network (MANET) structure.
# Developing a Fire Monitoring System Based on MQTT, ESP-NOW, and a REM in Industrial Environments
## 2 System Architecture
### Message Queue Telemetry Transport  
MQTT is the primary communications protocol for transmitting data from the sensor nodes to the central monitoring server. The publish-and-subscribe architecture of MQTT ensures efficient communication by transmitting data only when anomalies are detected,reducing network congestion and ensuring timely transmission [15]. In the proposed system, each sensor acts as a publisher, sending fire detection data only when an anomaly is detected. The central server, which acts as a subscriber, receives these alerts and processes them in real-time. The unique application of MQTT in the proposed system lies in its integration with ESP-NOW, enabling seamless data flow from sensor nodes to the server, even in environments with significant electromagnetic interference (EMI) [16].
## 3 Results and Discussion
### Load-Strain Response  
In the current research, the load-strain responses were directly measured by strain gauges attached to the concrete and steel bars, namely, C-1, C-2, C-3, and S strain gauges, as shown in Figure 6. As an example of these responses, the load-strain curves for beam B2 are presented in Figure 11. In this figure, all strain measurements were compressive (negative) up to 20 mm below the beam's top surface, while the mean strains were tensile (positive) on the steel rebars (at 125 mm below the top surface). The concrete strain at C-1 progressively increased even after the peak load; however, strains on C-2 and C-3 were likely constant before reaching the beam's end-of-elasticity moment and tended to decrease after the peak load. This reduction in concrete strains indicated the propagation of tensile cracks to the positions of the associated strain gauges. Notably, the ultimate concrete strains at failure were about 0.0038. Additionally, after the ultimate load, the strains of C-3 were close to zero, which indicated that the sectional neutral axis was touching the location of C-3.  
The load-strain responses of steel bars were similar to the beams' moment-curvatures (discussed in Section 3.6). This response was typically linear at various stages, with reduced slopes as the beam's flexural stiffness decreased after achieving the beam's elasticity limit and bar yielding. However, the tensile strains of steel were almost constant (about 0.0085) after reaching the beam's ultimate load.
# One-Class LSTM Network for Anomalous Network Traffic Detection
## 5 Experimental Results and Discussion
### One-Class Classification on CIC-IDS2017  
Two sub-datasets from CIC-IDS2017, CIC-DDoS, and CIC-PortScan were selected as experimental data. We randomly selected 90% records from CIC-DDoS as the training set and the rest as the test set. At the same time, CIC-PortScan data were also used as a test set to measure the generalization abilities of the models, as this is quite important in the field of network security.  
Table 4 illustrates that the OC-LSTM model performed significantly better than the existing state-of-the-art methods. It is evident that the ability of the OC-LSTM model to extract progressively rich representations of complex sequential data within the hidden LSTM layer of the feed-forward network induced better an anomaly detection performance. In addition, the LSTM model also showed great generalization performance on the CIC-PortScan dataset. In contrast, the generalization performance of conventional methods such as soft-bound deep SVDD and one-class deep SVDD was poor, which means that they may not be suitable for applications in complex and changeable large-scale network traffic anomaly detection tasks. Note that the IF and DCAE methods performed better on CIC-PortScan, mainly because of the difference caused by the different types of anomalies in the two datasets. In addition, due to the use of SGD optimization (as recommended in [24]), soft-bound and one-class deep SVDD exhibited higher standard deviations than those of the other methods. The PRC and ROC for each model on the CIC-DDoS dataset are shown in Figures 5 and 6, respectively.
# An Energy-Efficient Mobile Sink-Based Unequal Clustering Mechanism for WSNs
###### Abstract  
Network lifetime and energy efficiency are crucial performance metrics used to evaluate wireless sensor networks (WSNs). Decreasing and balancing the energy consumption of nodes can be employed to increase network lifetime. In cluster-based WSNs, one objective of applying clustering is to decrease the energy consumption of the network. In fact, the clustering technique will be considered effective if the energy consumed by sensor nodes decreases after applying clustering, however, this aim will not be achieved if the cluster size is not properly chosen. Therefore, in this paper, the energy consumption of nodes, before clustering, is considered to determine the optimal cluster size. A two-stage Genetic Algorithm (GA) is employed to determine the optimal interval of cluster size and derive the exact value from the interval. Furthermore, the energy hole is an inherent problem which leads to a remarkable decrease in the network's lifespan. This problem stems from the asynchronous energy depletion of nodes located in different layers of the network. For this reason, we propose Circular Motion of Mobile-Sink with Varied Velocity Algorithm (CM2 SV2) to balance the energy consumption ratio of cluster heads (CH). According to the results, these strategies could largely increase the network's lifetime by decreasing the energy consumption of sensors and balancing the energy consumption among CHs.  
Keywords:wireless sensor networks; network lifetime; energy holes; genetic algorithm; mobile sink +
Footnote †: journal: Sensors
# Development of a Robotic Airboat for Online Water Quality Monitoring in Lakes
## 4 Materials and Methods
### Methodology  
First, the software embedded in the boat was loaded into some computerized device, equipped with a Wi-Fi connection, then the mission was named and the waypoints were determined by the operator. Once these configurations were made, the boat was put into the water and remotely activated to start the mission, autonomously, doing the physical-chemical analysis of water and storing it in the database associated with its Latitude and Longitude coordinates. At the end of the mission, the boat returned, automatically turned-off the aerodrive, and the operator could generate reports with the data collected.  
The system used commonly purchased components, such as cardboard and fiberglass, as well as Arduino open source hardware and software, and a Raspberry Pi minicomputer, which provided easy integration and modification of the software and firmware, during the verification and validation tests. In the first step, the hull of the boat was built and checked for buoyancy.  
In the second step, all the hardware material necessary for the electronic and mechanical control of the boat was listed. The acquired measurement sensors of the water parameters, carried out the calibration of the Atlas sensors, using the Arduino platform, which occurred satisfactorily, based on the values of its calibration solutions with small variations of two points. Then it conducted tests with the IMU, the GPS, the servo of the rudder, the aerodrive, and the temperature sensor in the plate Raspberry Pi. In this phase, the protocol for communication with the sensors was developed.  
In the third step, the control software with access to the database was developed, which provided a greater control and monitoring of the boat's functionality, from the configuration of the mission to the generation of reports.  
For initial tests, the system was operated remotely and manually, where the route was random and determined during the mission. During the route, the boat carried out the analyses and stored them in a database. The reports were then validated, in order to generate graphs of the collected points.  
After the initial tests, the GPS navigation algorithm was developed, so the boat started to navigate, autonomously, collecting data, storing them in the database, and associating each waypoint with the GPS coordinates, thus, generating more complete reports, including the programmed trajectory versus the trajectory carried out.  
Figure 4: Airboat hardware components; (a) external View and (b) internal View.