esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Physical Characterization of CMSC  
#### 3.8.1 Degree of Substitution (DS)  
The DS is a major factor governing properties such as water solubility of Na-CMC, but below DS 0.4 the polymer is water-swellable and insoluble. The polymer is entirely aqueous soluble with its hydro affinity increasing with increasing DS [58]. An average DS of 0.62 was obtained with isopropanol as the solvent medium. The role of the solvent in the carboxymethylation reaction is to provide accessibility of the etherifying reagent to the reaction centers of the cellulose chain [59]. A NaOH concentration (25%) was utilized according to [PERSON] _et al._[30]. This is because, in the carboxymethylation procedure, two competitive reactions take place simultaneously. The first reaction involves sag cellulose hydroxyl groups react with Na-MCA in the presence of NaOH to give CMSC. The second reaction of Na-MCA with NaOH to give sodium glycolate as a byproduct [60]. Hence, increasing the concentration of NaOH could cause the second reaction to prevailing more than the first. According to [PERSON] _et al._[25], increasing NaOH would lead to more glycolate formation. Similar research was done on maize starch where it was concluded DS value would also gradually decline due to the sodium glycolate formation in the synthesis of CMC and polymer degradation would also occur due to the high concentration of NaOH [61].
## 3 Results
### Temporal Matching between the In Situ Data and Satellite Imagery  
To investigate the impact of timing of field data collection on the strength of the derived relationships, regression analysis was performed using different sets of in situ data; including \"all data\", \"data from 2013 only\" and \"data from 2014 only\". The highest R\({}^{2}\) values were obtained when the in situ data and the satellite image came from the same year (Figure 6; Table 3). In situ data and satellite data from the same year are used in all subsequent analysis, except where otherwise stated.  
Figure 5: Time series of Landsat 8 top-of-atmosphere NDVI for different grassland types that correspond to different productivity levels (Table 2) (**top**); and time series of within habitat and between habitat variance in NDVI (**bottom**). The optimum time window (grey shaded areas) was defined as when “between habitat variance” > 0.01 AND “between habitat variance” > “within habitat variance”, where “between habitat variance” was calculated as the variance of the mean NDVI value for each habitat and “within habitat variance” was calculated as the mean of the variance in NDVI for each habitat.
# Advanced Brain Tumor Segmentation Using SAM2-UNet
## 3 Materials and Methods
### Brain Tumor Segmentation Model  
Deep learning has demonstrated remarkable success in image segmentation tasks due to its ability to automatically learn hierarchical feature representations from data. Architectures like U-Net, SegNet, and Mask R-CNN have consistently achieved state-of-the-art performance across various domains, including medical imaging, autonomous driving, and satellite imagery analysis. For example, the U-Net architecture has become a standard choice for biomedical image segmentation due to its effective use of skip connections and its ability to localize fine-grained details [10]. In this study, SAM2-UNet demonstrates robust performance by achieving a mean Dice score (mDice) of 0.771, a mean Intersection over Union (mIoU) of 0.569, an \(S_{a}\) score of 0.692, a weighted F-beta score (\(F_{\beta}^{av}\)) of 0.267, an F-beta score (\(F_{\beta}\)) of 0.261, an \(E_{\phi}\) score of 0.857, and a mean absolute error (MAE) of 0.046 when training for 30 epochs, with 250 iterations for each epoch.
## 2 Materials and Methods
### System's Software Programming and Workflow
#### 2.2.2 Software: System Pipeline  
The user interface of RoboPlantDet is designed to be intuitive and lightweight. It utilizes the cvui2.7.0 UI library [55], which is built on OpenCV drawing primitives. Through this interface, users can easily perform various tasks, as shown in Figure 9a. One of its key features is the ability to set the scheduled time interval for plant recognition and image acquisition, along with specifying the number of plants to be identified. The system is designed to perform several tasks repeatedly.  
Firstly, it identifies the plants and records their precise locations. It then captures images of these plants using both RGB and NIR cameras while also recording the distance of each plant from the cameras based on readings from both distance sensors. Next, the system processes the captured images to determine the total projected leaf contours, diameter, center, total projected leaf area, and centroid of each plant. This information is then combined with the date and time of the recording. All these calculated data values, along with the date and time, are logged into a logfile for future reference. Additionally, the system offers the option to overlay this information onto each image. Furthermore, the acquired RGB and NIR images are saved separately in designated folders.  
To ensure data accessibility and backup, the system synchronizes the folders containing the images and recorded data with other dedicated computers through cloud storage. This allows for easy sharing and collaboration among multiple devices.  
Figure 9: In (**a**), the plant recognition and monitoring user interface is shown. In (**b**), the user’s selection menu for setting the upper and lower thresholds of HSV color values (left side) is shown. Apart from the plant in focus, all other information is filtered out (right side).
# FOXS-GSC--Fast Offset Xpath Service with HexagonS Communication
## 3 Proposed Solution
### Message Routing Addressed to Cloudlet  
This routing method occurs when only the destination's geographical location is known, that is, when a message is sent to a cloudlet. When a vehicle prepares a message addressed to cloudlet, it checks whether the cloudlet is within its communication coverage. \begin{table}
\begin{tabular}{c c} \hline \hline
**Field** & **Description** \\ \hline origin\_sector & sector that the initial node is within \\ \hline origin\_node & initial node id \\ \hline destination\_sector & sector that the destination node is within \\ \hline destination\_node & destination node id \\ \hline nexthop\_sector & next sector \\ \hline nexthop\_vehicle & next hop (relay) \\ \hline visited\_sectors & list of visited sectors \\ \hline visited\_vehicles & list of vehicle used as relay \\ \hline recovery\_tag & number of backtrack steps \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Protocol message format (MSG). Figure 3: Vehicle sectors and neighbor list. If it is, the message is sent directly; otherwise, it is necessary to use the proposed multi-hop communication protocol. The proposed multi-hop protocol uses the prior knowledge of the cloudlets' geographical locations and the knowledge on neighboring nodes acquired through their received beacons. Figure 1 presents the discretized scenario and the behavior of the proposed protocol. As can be seen, the vehicle in sector \(C\) wants to send a message to the nearest RSU. When the routing process starts, it is necessary to check which neighbor sectors have at least one vehicle and which sectors have been visited in order to avoid looping. Therefore, to find the best path to the destination, the protocol performs high-level routing using the shortest path algorithm (e.g., Dijkstra) in the graph representing the sectors discretized in the scenario. When the next sector is chosen, one vehicle located in this sector is selected and the message is addressed to it. This method is executed at each hop by the respective relay until reaching the destination. Algorithm 1 formalizes this protocol. In Algorithm 1, the initial node is the sector in which the vehicle is located and the final node is the destination cloudlet sector. The other input parameters are the neighbor list, the list of hexagons and their positions, the list of cloudlet IDs and positions, the list of vehicle IDs and position, and the current sector (lines 1 to 3).
# AERQ--A Web-Based Decision Support Tool for Air Quality Assessment
## 3 Results  
### The AERQ Dss  
The AERQ platform integrates multiple data sources, including Copernicus, regional databases, in-house datasets, and ISTAT data, along with high-performance computing (HPC) infrastructure and large-scale data storage. This integration supports the execution of models such as AERMOD and various Business Intelligence (BI) applications. After executing the scenarios, BI algorithms process the data. Hourly analyses are conducted to evaluate daily cycles and are then aggregated to provide annual, monthly, and seasonal insights, which are presented through the application interfaces.  
AERQ DSS enables comprehensive analysis by the following:  
Estimating atmospheric pollutant emissions from diverse sources, such as transportation and industrial activities.  
Predicting pollutant concentrations in the air, analyzing spatiotemporal variations and trends.  
Identifying, detecting, reconstructing, and forecasting pollutant behavior, enabling deeper insights into environmental dynamics.
## 3 Results
### AI for Sustainable Agriculture and Agroecosystem Management
For the assessment of heavy metal contamination (Cd, Pb, Cr, Hg), the study by [36] employed ensemble models including Random Forest and Gradient Boosting Machine, reducing prediction errors by 58% and improving food safety outcomes. The application of artificial intelligence and deep learning in agroforestry presents new opportunities for evaluating and improving land-use systems. In [37], a DL model based on U-Net architecture was developed for tree component classification in agroforestry landscapes. This approach allowed for precise analysis of land-use types and provided robust support for sustainable development initiatives. The integration of artificial intelligence and the Internet of Things (IoT) enables real-time monitoring of soil properties and adaptive management of water and fertilizer use. In the study by [38], these technologies contributed to increased crop yields, reduced operational costs, and minimized environmental impacts. Additionally, smart farms leveraging cloud platforms and AI facilitate the automation of agricultural operations. Study [39] introduces the concept of a \"smart cloud brain\", which coordinates the actions of unmanned agricultural machinery, IoT devices, and precision farming technologies, ensuring sustainable production with minimal reliance on pesticides and fertilizers. To evaluate the agroecological impacts of agricultural practices in Luxembourg, agent-based modeling (ABM) was combined with life cycle assessment (LCA). The study by [40] illustrates how AI can forecast the effects of management changes--such as reduced livestock density or a shift to local feed--on emissions and economic performance, thereby supporting the development of sustainable agricultural strategies. Finally, the study by [41] focuses on forecasting water usage and optimizing the distribution of water and energy resources to enhance the resilience of agroecosystems and reduce their carbon footprint. The authors emphasize the need to integrate digital, robotic, and biological approaches in the future of agriculture. The cumulative analysis of the reviewed studies demonstrates that AI has become an integral component of modern agriculture. Its implementation not only improves the precision of monitoring and forecasting but also transforms approaches to managing land, water, and soil resources. This enables adaptation to the impacts of climate change, enhances productivity, and supports food security while reducing anthropogenic pressure on the environment. AI plays a pivotal role in strengthening the sustainability of agroecosystems at both regional and global scales--from steppe pastures and degraded lands to high-intensity agricultural areas. Ongoing advancements in AI algorithms, their localization to specific conditions, and integration with other digital technologies lay the foundation for transitioning toward more sustainable and efficient models of agricultural production in the future. In total, according to the regional classification, the following references pertain to Asia [29; 31; 34; 36; 37; 39] and Europe [38; 40].
# Quad-Band Uniformly Spaced Array Antenna Using Diverse Patch and Fractal Antennas
###### Abstract  
Multi-band antennas have received significant interest because they can support multiple wireless communication services with a single antenna. However, an array antenna consisting of these element antennas can suffer from non-periodic arrangement due to the irregular sizes of the elements. In this paper, various shapes of patch antennas with fractal antennas are used to ensure the periodic arrangement of the array antenna, and antenna array incorporated with a feed network is proposed. Four different antenna arrays operating at 2.45/3.7/4.3/5.0 GHz are aggregated in an antenna with interleaved disposition of the different element antennas. It is observed that mutual couplings between two elements are sufficiently low, at less than \(-23\) dB. Peak antenna gain ranging from 11.1 dBi to 14.4 dBi at the four different bands is obtained.  
multi-band antenna; Sierpinski fractal antenna; patch antenna; array antenna; quad-band array antenna; Wi-Fi; 5G communication systems; sensors +
Footnote †: journal:  
miniaturization techniques can be simultaneously applied to reduce the size of the elements operating at lower frequency bands in order to obtain similarly sized element antennas.  
In this paper, a quad-band uniformly spaced array antenna is proposed based on similarly sized element antennas using square and bow tie patch antennas and corresponding fractal antenna designs. A fractal antenna design based on a fat-bow-tie-shaped patch antenna is proposed. The set of element antennas is designed to cover some of the center frequencies of Wi-Fi and 5G communications, including 2.4 GHz/3.7 GHz/4.3 GHz/5 GHz. Four different array antennas are divided into two upper arrays (2.4 GHz/4.3 GHz) and two lower arrays (3.7 GHz/5 GHz), inter-element spacings between the two elements being set as 0.5A at 2.4 GHz and 0.9A at 4.3 GHz for the upper arrays and 0.64A at 3.7 GHz and 0.87\(\lambda\) at 5 GHz for the lower arrays. A common ground plane is placed in the middle layer between the radiating elements in the top layer and the feeding network in the bottom layer.
## 3 Methodology
### Mathematical Model and Variance Analysis  
As shown in Figure 3, the mechanical properties of low-carbon steel were greatly influenced by the carbon content and vacancy ratio. In order to reduce the influence of local random relationships on the mechanical parameters of the material, 50 simulations were conducted for each case, and [PERSON]'s modulus \(E\) and yield strength \(Q\) were calculated for each simulation. The median of the results of each calculation was taken as the mechanical characteristic of the material. The results are summarized in Table 3.  
Figure 3: Curve of stress–strain. Figure 2: Simulation model. The experimental data in the table were subjected to regression analysis, and the response surface regression equations for \(E\) and \(Q\) were obtained as follows:  
The following conclusions can be drawn from Tables 4-6. The F-values of the modulus of elasticity \(E\)(\(T\),\(R\),\(V\)) and yield strength \(Q\)(\(T\),\(R\),\(V\)) are 19,467.81 and 372.32, respectively, with \(p\)-values all less than 0.00001, so the model is significant and there is only a 0.01% probability that this model is inaccurate due to errors. Based on the \(p\)-value analysis, it can be concluded that the effect of temperature on the yield strength is more significant than the C content, as well as the vacancy rate, from the single factor analysis. As for [PERSON]'s modulus, the temperature, C content, and vacancy rate all have a significant effect on it. The interaction factor of C content and vacancy rate does not have a significant effect on [PERSON]'s modulus change, and the interaction of temperature with the other two factors often shows a more significant effect. The factors \(TV\), \(RV\), and \(TRV\) will likewise have a significant effect on the yield limit. \[ \begin{array}{ll}E(T,R,V)&=132.73991+0.481666T+11.32157R+8.9423V-0.078182 TR\\ &\qquad-0.028533 TV+2.54469 RV-0.001214T^{2}+13.46899R^{2}\\ &\qquad-0.871564V^{2}+0.0005227 RV+0.000048T^{2}R\\ &\qquad+0.000024T^{2}V+0.000491 TR^{2}+0.002053 TV^{2}\\ &\qquad-0.340772R^{2}V-0.217449 RV^{2}+6.22653*10^{-7}T^{3}\\ &\qquad-2.90703R^{3}+0.042566V^{3}\\ \end{array}  \tag{7}\]  
\[ \begin{array}{ll}Q(T,R,V)&=19.4554-0.032261T+1.15173R-0.367567V+0.005267 TR\\ &\qquad+0.001003 TV-0.163202 RV+0.0000297^{2}-1.4126R^{2}\\ &\qquad+0.010969V^{2}+0.000385 TRV-4.46411*10^{-6}T^{2}R\\ &\qquad-1.04853*10^{-6}T^{2}V-0.000382 TR^{2}+9.40534*10^{-6}TV^{2}\\ &\qquad+0.003993R^{2}V-0.003319 VR^{2}+6.87185*10^{-9}T^{3}\\ &\qquad+0.319692R^{3}-0.002348V^{3}\\ \end{array}  \tag{8}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Name** & **E/GPa** & **Q/GPa** & **Name** & **E/GPa** & **Q/GPa** & **Name** & **E/GPa** & **Q/GPa** \\ \hline
1 & 157 & 10.8642 & 20 & 154 & 11.3220 & 39 & 152 & 11.2760 \\ \hline
2 &
## 1 Introduction
Although there are many localization techniques available to solve positioning problems in the WSNs, there are practical limits on the combination of these techniques as well as on the minimal number of anchor nodes that can be deployed in such scenarios. For example, in many situations, only one or two anchor nodes are able to communicate with the sensor nodes that need to be localized. Hence, new positioning techniques based on hybrid data fusion and/or heterogeneous access are proposed and analyzed [42]. In this paper, we present a detailed survey on recent localization techniques and concepts with their fundamental limits, challenges and applications. Although literature survey on localization techniques are available in [8; 43; 44; 45; 46; 47; 48], only a few papers exist that focus on range free localization techniques [49] without focusing on recent advanced techniques and applications. Thus, the survey in [45] is outdated, whereas [43] focuses only on ultrasonic positioning systems. The work in [8] describes relatively recent localization techniques but focuses only on the indoor localization techniques and briefly discusses about range free localization. The works of [46; 47] review different technologies, such as Wireless Local Area Network (WLAN), used for indoor positioning. However, they do not discuss positioning neither from the perspective of energy efficiency nor from the requirement in recent applications, such as ambient assisted and health living applications. The survey in [48] provides notable categorization of various fingerprint-based outdoor positioning techniques, discussing how each method works. So, we intend to present a survey focused specially on range free techniques. Moreover, the rapid growth of various localization approaches in this field and the need for a complete and up-to-date survey of the techniques, applications and future trends, provide the motivation for this survey paper. The remainder of this paper is organized as follows. Basic distance measurement techniques for localization in WSNs are described briefly in Section 2 with their common pitfalls and challenges. Different localization algorithms and their comparative analysis are discussed in Section 3. Section 4 describes various localization based applications. Section 5 presents various evaluation criteria for localization. Then we present perspective and challenges in range free localization algorithms in section 6. Finally, Section 7 concludes the paper.
# Machine Learning for Physical Layer in 5G and beyond Wireless Networks: A Survey
## 5 Challenges and Future Directions
Roaming security: While roaming from one network to another, updates on the security parameters of the user are greatly required, particularly in the 5G densification scenario. Ingress/egress firewall security policies, subscriber-level security, and personal firewalls are used to protect from security and privacy attacks, but the challenge is to provide these services to different subscribers in multiple locations. Hence, ML also provides an intelligent information-sharing mechanism for roaming security using network slices to address these challenges. 16. Signaling storms: Low-cost M2M devices have several limitations such as computational capabilities, energy support, and memory capabilities. At the same time, these low-cost devices can be compromised and allow DoS and DDoS attacks against the radio access network. Unexpected non-malicious events may also cause the devices to behave abnormally and produce \"flash crowd\" situations, leading to the exhaustion of radio resources. Hence, ML is required to provide an overload control (currently relying on MME) mechanism to prevent all devices from attempting to access the network, as well as to initiate the \"START\" and \"STOP\" and select UEs to target the overload procedures. * DoS and DDoS attacks: Fifth-generation networks are expected to support a number of network devices, while DOS and DDOS attacks will become a real threat designed to exhaust the physical and logical resources of the target. In this regard, network infrastructure and devices DOS attacks are common categories. Using these attacks, the attackers drain the network, logical, and physical resources of 5G users and devices accordingly. These challenges can be minimized by introducing ML-based approaches. Intelligent ML-based approaches can predict an attack using ongoing traffic and offer optimized network management. Network control elements can be hidden and revamp the unencrypted control channels.