esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### RQ6: Issues in WSN and IoT Frameworks
#### 4.6.2 Data Confidentiality and Privacy  
Data confidentiality is a significant issue in IoT and network security. In IoT frameworks, the client gains access to the information and system management in an unintended environment due to issues such as the use of sensor nodes. Attackers can physically capture them and extract data using an energy analysis attack [84]. Refurbished devices made from these captured devices can launch new attacks and violate security. Therefore, the IoT device should verify whether or not the user or device has been granted permission to access the system. The practice of controlling access to data by granting or denying permission based on a set of laws. Many devices/clients must be authenticated by management to access the system. Data confidentiality and access are the main issues in the Internet of Things (IoT). Researchers are trying to figure out how to handle the personalities of customers, items/articles, and devices in a secure manner. Due to the ubiquitous nature of IoT and WSN systems, privacy and confidentiality are major concerns in IoT devices and frameworks. Some issues, such as sniffing and spoofing, unauthorized access, data changing, forging, and unapproved alteration of IoT and WSN nodes, pose significant uncertainties in IoT. An attacker can use various IoT devices and applications to capture sensitive and personal data that is visible to outsiders.
# 3D Global Path Planning Optimization for Cellular-Connected UAVs under Link Reliability Constraint
## 1 Introduction  
### _UAVs_, _Bvlos_, _and_ Cellular-_Connected UAVs_  
Unmanned aerial vehicles (UAVs), also known as drones, are one of the fastest-emerging technologies. Recently, low-altitude UAVs have received tremendous attention for civil applications, such as surveillance, transportation, environmental monitoring, industrial monitoring, agriculture services, disaster rescue, and goods and medical delivery [1, 2, 3, 4, 5, 6, 7]. UAVs are seen as significant enablers of numerous applications in 5G and 6G networks due to their distinctive features, such as 3D mobility, versatility, and the potential for line-of-sight (LoS) communications [8].  
Currently, in most parts of the world, UAV applications are limited to operating within the visual line of sight of pilots [9]. However, for applications such as inspections and package delivery, drones are expected to operate autonomously over long distances, wherethere is no visual line of sight and the pilot cannot observe the drones during their missions. Hence, the next step in drone technology is to advance drones to fly beyond the visual line of sight (BVLOS).  
To ensure a safe flight, drones need stable and reliable wireless connectivity for payload and control and command (CC) communications [10]. One of the limitations of conventional UAV communication systems is their limited communication range. Mainly drone communication can be held through four technologies: direct RF, cellular, satellite, and dedicated links [11]. Among the existing wireless technologies, the cellular network has recently been considered one of the main enablers for ubiquitous UAV communication [11] due to its distinctive features, such as reliability, data rate, latency, and energy efficiency [12].  
However, existing cellular networks are designed and optimized for terrestrial users, providing ubiquitous, low-latency, high-speed, and reliable connectivity [13; 14]. Providing connectivity to nonterrestrial users, such as flying drones, is challenging. On the one hand, with increasing height above the ground, the radio environment changes, and some problems arise, such as mobility management [15; 16] and severe interference between nonterrestrial and terrestrial UEs [17; 18]. On the other hand, the transmitting antennas are tilted down, resulting in a severe reduction in the antenna gain at higher heights, which leads to lower link reliability and data rate [15]. Hence, addressing the aforementioned issues and providing robust, reliable, and limitless wireless connectivity for UAVs have become the ongoing topics in beyond 5G and 6G communication [8; 19]. Therefore, this paper focuses on identifying the aerial coverage reliability of existing terrestrial 4G networks and optimizing the UAV path planning problem accordingly.
# A Next-Generation Core Network Architecture for Mobile Networks
## 4 IP Addressing  
An SN uses a private IP address space with its UEs and VCNs. In particular, in Figures 8 and 9, the IP addresses labeled with an _IN_ suffix belong to the private address space, while the ones labeled with an _OUT_ suffix are public IP addresses as they have to be routable.  
The IP address of the UE (i.e., a private IP address) does not change when switching between SN and VCN as it does not change its SIP Proxy server's IP address. This greatly simplifies the re-direction between VCN and SN, in terms of SIP and SDP payload, since the SDP information contained in the INVITE does not need to be modified when moving from SN to VCN. As we discuss in Section 6, the IP address of the default gateway (i.e., P-GW) can also be left unchanged when moving between SN and VCN. On the other hand, the IP addresses of the SN's Mobility Management Entity (MME) and of the VCN's MME need to be different. This is because, in order for the EPS bearer update procedure to work (see Section 6), the two MMEs need to be independently addressable by the eNodeB.  
When a UE \"moves\" between SN and VCN, the external IP address of the UE changes as SN and VCN have different public IP addresses (i.e., addresses labeled with an _OUT_ suffix in Figure 8 and 9). This breaks all existing TCP connections: while this may seem a negligible issue, we have to remember that, today, smartphones keep several TCP connections open in order to enable, for example, push services. In order to address this issue, we create a tunnel between SN and UE so that data on existing TCP connections are forwarded by the SN to the VCN and from this to the UE (see Figure 10). Once all the \"old\" TCP connections have timed-out or have been closed, the tunnel is terminated by the SN.  
Figure 8: SN high-level architecture (SIP Proxy and EPC are multi-homed).  
Figure 7: Enhanced eNodeB protocol stack. (**a**) User Plane; (**b**) Control Plane.
# Microcontroller Unit-Based Wireless Sensor Network Nodes: A Review
## 3 MCU-Based Hardware Platforms for Wireless Sensor Nodes
### General IoT Applications
#### 3.4.7 Iris  
The IRIS mote (MEMSIC) is a popular commercial mote developed for applications that include indoor building monitoring and security, acoustic, video, vibration and other high-speed sensor data and large-scale sensor networks. The mote utilizes the XM2110 CA processor, which is based on the Atmel ATmega 128-1, with 8 kB RAM, 128 kB program flash memory and 512 kB measurement flash memory, and 4 kB of configuration EEPROM. The IEEE 802.15.4-compliant RF transceiver operating in the 2.405 GHz to 2.480 GHz ISM band was chosen for the IRIS mote. The radio can transmit at data rates of up to 250 kbps at a power of 3 dBm. The indoor coverage range is more than 30 m and can reach above 300 m outdoors in line-of-sight situations.  
Several sensor and data acquisition boards (including light, temperature, RH, barometric pressure, acceleration/seismic, acoustic and magnetic) can be connected to the platform thanks to the presence of the standard 51-pin expansion connector that facilitates plug and play installation. The mote requires 2.7 to 3.3 V power supply. A pack of two AA batteries is attached to the mote. Regarding the power consumption, the mote's processor draws approximately 8 mA in the active mode and a total of about 3 mA in the sleep mode. Around 16 mA of current is drawn during the receive mode of the transceiver while 17 mA is drawn when transmitting at 3 dBm.
## 3 Results and Discussions  
### Color Properties of the Mastic Gum Samples  
The color parameters (\(L^{*}\), \(a^{*}\), \(b^{*}\), \(C^{*}\), and \(H^{\circ}\)) of the mastic gum samples from different origins (Turkiye: MGT, Greece: MGG) are presented in Table 1. The \(L^{*}\), \(a^{*}\), and \(b^{*}\) values of the MGT sample were determined as 61.92, \(-\)0.93, and 6.74 while those of the MGG sample were 47.36, \(-\)1.79, and 27.23, respectively. The MGT sample exhibited a higher \(L^{*}\) value, indicating greater brightness, whereas the MGG sample showed higher \(a^{*}\) and \(b^{*}\) values, suggesting a more matte and yellowish appearance. Statistical analysis revealed significant differences (\(p<0.01\)) between the \(L^{*}\), \(a^{*}\), and \(b^{*}\) values of the gum samples from two distinct geographical origins.  
The \(C^{*}\) and \(H^{\circ}\) color parameters were 6.80 and \(-\)82.14 for the MGT sample and 27.29 and \(-\)86.23 for the MGG sample, respectively (Table 1). A comparison of the two samples revealed that the MGT sample exhibited brighter and greener tones, while the MGG sample displayed a more pronounced yellow color. Statistical analysis indicated significant differences (\(p<0.01\)) in the \(C^{*}\) and \(H^{\circ}\) values of the two samples. The higher \(C^{*}\) value in the MGG sample suggested greater color saturation and intensity, resulting in an opaquer appearance. These differences in color characteristics may be related to the variations in harvest time, cultivation, and storage conditions, which can also influence the overall composition of the samples. This study marks the first reporting of color difference in mastic gum samples from two different geographical origins.
## 3 Results  
### Study Area  
The study area, selected for this experimental research and the implementation of the urban digital twin is the city center of Reggio Calabria (Figure 4). This city has a unique geographical conformation, where urban development is primarily concentrated along the coastal area, gradually expanding toward the mountains, and eventually meeting hilly and peripheral zones. This layout makes Reggio Calabria an ideal location for studying both light pollution and hydrogeological risk phenomena.  
The contrast between the city center and its outskirts allows for a clear distinction in light pollution levels, providing valuable insights into the impact of urbanization on artificial light distribution. Additionally, the city's location makes it particularly suitable for analyzing hydrogeological risks. Reggio Calabria is home to natural watercourses, such as the \"fiumare\" typical of Mediterranean regions, which are prone to flooding, especially during periods of heavy rainfall.  
According to the ISPRA (Higher Institute for Environmental Protection and Research), the hydrological risk, including flooding and landslides, is increasing, particularly in the Calabria region [51]. The city's susceptibility to these phenomena makes it an important case study for understanding the potential impacts of climate change and urbanization on flood risk and environmental resilience. The image in Figure 5 illustrates the study area chosen for this research.  
Figure 4: Study area; Municipality of Reggio Calabria (Italy).
# CFD Analysis of Subcooled Flow Boiling in 4 x 4 Rod Bundle
## 4 Operating Conditions
### Initial and Boundary Conditions  
The total length of the rod bundles was 590 mm. The diameter, pitch, and hydraulic diameter are 16, 21, and 14.02 mm, respectively. The cross-sectional area through which the coolant flows is approximately 0.00401 m\({}^{2}\). The experimentally measured data on Lines 1-5 comparing the numerical analysis results at the cross-section of 580 mm in height are shown in Figure 2.  
The initial conditions for the six experimental cases used in the calculations are listed in Table 2. At the outlet of all cases, the fluid has a pressure of 194.45 kPa. This shows a decrease of about 5 kPa from the inlet. Calculation was performed with constant density.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Case No.**} & \multicolumn{2}{c}{**Mass Flux**} & \multirow{2}{*}{**Velocity (m/s)**} & **Heat Flux** & **Sub-Cooled** & **Inlet Temp.** \\  & **(kg/m\({}^{2}\)s)** & & **(kW/m\({}^{2}\))** & **Temp. (\({}^{\circ}\)C)** & **(\({}^{\circ}\)C)** \\ \hline
1 & 310 & 0.322917 & 168 & 22.7 & 97.5 \\
2 & 310 & 0.322917 & 168 & 20.2 & 100. \\
3 & 310 & 0.322917 & 168 & 25.2 & 95.0 \\
4 & 310 & 0.322917 & 148 & 22.7 & 97.5 \\
5 & 310 & 0.322917 & 200 & 22.7 & 97.5 \\
6 & 250 & 0.260417 & 168 & 25.2 & 95.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Initial conditions of six cases.  
Figure 2: Measured points on Lines 1–5 for numerical analysis.  
In Table 2, the different conditions are highlighted in gray. Cases 2 and 3 have different inlet temperatures compared with Case 1. Cases 4 and 5 have different heat flux compared with Case 1. Case 6 has a different inlet velocity from that of Case 3.
## 2 Materials and Methods
### Methods
#### 2.2.6 Antioxidant Activity (AA)  
Antioxidant activity was assessed using analytical methods, namely ABTS ([PERSON], Staufen, Germany), was dissolved in the water to a 7 mM concentration. ABTS radical cation (ABTS+\(\bullet\)) was produced by reacting ABTS stock solution with 2.45 mM potassium persulfate (final concentration) and allowing the mixture to stand in the dark at room temperature (12-16 h) before use. The bleaching rate of ABTS+\(\bullet\) in the presence of the sample was monitored at 734 nm using a Helios Gamma 100-240 (Runcorn, UK) spectrophotometer. The ABTS+\(\bullet\) solution was diluted in PBS buffer (pH 7.4) to give an absorption value of 0.700 \(\pm\) 0.05 for the analysis of ethanol extracts. Volumes of 2.00 mL of ABTS+\(\bullet\) and respective ethanol extracts in PBS buffer solution were used. The ABTS+\(\bullet\) bleaching was monitored at 37 \({}^{\circ}\)C, and the discoloration after 6 min was used as the measure of antioxidant activity. Radical scavenging activity was measured as Trolox equivalent antioxidant capacity (mg Trolox per g of sample dm). Trolox solutions used for the calibration curve were in the concentration range 0-2.5 mM (R\({}^{2}\) = 0.9957). Additionally, AA was expressed as EC\({}_{50}\) (mg/mL).
# Digitalization Processes in Distribution Grids: A Comprehensive Review of Strategies and Challenges
## 4 Energy Market
### DR in the Energy Market
#### 4.2.1 Mechanisms and Technologies in DR  
This section examines DR mechanisms, emphasizing the essential role of digital technologies in SGs. Digital tools enable strategic energy consumption decisions and leverage optimization methods such as convex optimization, game theory, and dynamic programming to enhance load management. Study [60] highlights the significance of bidirectional digital communication and distributed energy management systems for sustainability and cost reduction. Ref. [66] discusses integrating DR within capacity remuneration mechanisms, advocating for differentiated payments tailored to DR technologies' roles and emphasizing digital tools in DR activation and operation. It critiques traditional mechanisms while praising digital solutions for improving market responsiveness and DR's economic viability. Furthermore, Ref. [21] explores the integration of AI and ML in DR, discussing their role in user engagement, price optimization, and device control, and reviews applications including blockchain to underscore its substantial influence on DR efficiency and SG management.
###### Abstract  
Wave field synthesis (WFS) can be used to construct virtual sound sources (VSSs) with a loudspeaker array. Conventional methods using a single type of loudspeaker showed limited performance in distance perception. For example, WFS with electro-dynamic loudspeakers (EDLs) has the advantage of constructing VSSs near the loudspeaker, while WFS with parametric array loudspeakers (PALs) has the advantage of constructing VSSs far from the loudspeaker. In this paper, we propose a VSS construction method utilizing crossfade processing with both EDLs and PALs. The contribution of EDLs and PALs was balanced to better synthesize the target sound field. We carried out experiments to evaluate the sound pressure, frequency characteristic, and sound image perception. The experimental results demonstrated that the proposed method can enhance these aspects of the VSS.  
virtual sound source; wave field synthesis; parametric array loudspeaker; distance perception; crossfade processing Article
# Cluster-Based Control Plane Messages Management in Software-Defined Flying Ad-Hoc Network
## 4 Evaluation  
In this section, we present the simulations to demonstrate the efficiency of the CAPONE in transmitting control messages over FANET. Specifically, we present the methodology and metrics applied to evaluate the CAPONE for the management of control messages to support the operations of surveillance applications in a disaster recovery scenario. We evaluated the impact of different existing protocols on the PDR, position prediction error, overhead, and remaining energy.
# Fuzzy Logic in Selection of Maritime Search and Rescue Units
## 3 Materials and Methods
### Vessel Selecton Criteria
The examples above show that the coordinator may find it difficult to unequivocally assign a compliance level. Effectiveness of the evaluation may be compromised due to missing information. What is worse, insufficient information may lead to a wrong decision, undue delay, or even failure of the operation. In consideration of the above, the author proposes that the criteria evaluation be supported with the use of fuzzy logic. In conclusion, in order to select ships, it is proposed to build ship rankings based on evaluation criteria. For this, it is necessary to collect data on available ships, to conduct modelling of the decision maker's preferences and to carry out calculations. The computation can be done using MCDA, fuzzy logic or a combination of both methods.
# The Potential of AI-Driven Assistants in Scaled Agile Software Development
## 1 Introduction
### Introduction to SAFe  
Scaled agile development methods, such as SAFe [2] and LeSS [3], entail the application of agile principles on a larger scale, extending beyond small, co-located teams to multi-team efforts that involve numerous actors and interfaces with existing systems. These methods promote collaboration, change tolerance, adaptive software development, and active customer involvement while emphasizing rapid iterations and frequent feedback loops [4].  
The most recent configuration of the SAFe framework, version 6.0 [2], is the foundation of knowledge between lean, agile, and DevOps for achieving business agility. It focuses on seven core business agility competencies that are essential to gaining and maintaining a competitive advantage in an increasingly digital world: lean-agile leadership, team and technical agility, agile product delivery, delivery of enterprise solutions, lean portfolio management, organizational agility, and continuous learning culture [2; 5].  
SAFe accommodates a wide range of development environments with four different configurations/levels, making it a versatile and adaptable approach for organizations of varying sizes and industries:  
* **Essential SAFe--**The basic building block of other SAFe configurations. This level includes all the essential elements needed to benefit from SAFe and the starting point for any implementation. It provides complex solutions for large teams and it consists of a program and team level [2].
* **Large-Scale SAFe--**It provides complex solutions that do not require portfolio management assistance. It is used mainly by organizations with multiple Agile Release Trains (ARTs) working together [2].
* **Portfolio SAFe--**Intended primarily for companies to align agile development with value streams and ARTs. The concept of lean-agile budgeting empowers decision-makers and includes visibility of portfolio and Work In Progress (WIP) limits through the Kanban system, as well as objective metrics that support management and improvement through Bucket-size planning. This level of configuration consists of a portfolio, program, and team level [2].
* **Full SAFe--**A complete SAFe framework that includes all levels of SAFe--team, program, large-solution, and portfolio level. It includes a complete set of roles, events, and artifacts, and is suitable for complex organizations that need to synchronize ARTs across the entire enterprise [2].  
One of the reasons SAFe is a preferred choice is its popularity [1] within the agile development landscape, evident from the amount of literature and resources dedicated and connected to it. This extensive knowledge base makes it easier for organizations to access information and support, as well as educate themselves on the experiences, success, and failure factors in other real-life applications. In the upcoming discussion, we will narrow our focus to SAFe as a representative example of SADMs, to explore its principles and benefits in connection to AI in greater depth.
## 7 Experimental Evaluation
### Results for the Partial Request Satisfaction Mode
#### 7.3.2 R-DPAP Results  
In the case of R-DPAP with partial request satisfaction mode, Figure 19a shows that the maximum utility is reached by all algorithms whatever the constellation size, except for n-rr. Note that the obtained global normalized utility is not equal to 1 because there are still some conflicts between some orbit slots that prevent the agents from obtaining their best paths.  
Figure 19b shows that the global reward increases with the number of satellites in the constellation. In fact, the larger the constellations, the higher the number of orbit slots and the higher the number of paths with a higher utility in the graphs.  
For all of the algorithms, the computation time required is much lower than for V-DPAP with partial satisfaction mode. This is quite natural, since even if there is a large number of paths, the selection of one path for an agent does not require deactivating many nodes in other graphs. This comes from the fact that orbit slots can be split between agents, which results in less conflicts between nodes.  
We do not provide here radars per instance, since the profiles obtained by the algorithms all overlap. Indeed, Figure 20 confirms that all the agents have a utility almost equal to 1 for all the algorithms except n-rr. The latter struggles with highly conflicting settings. With less conflicting settings (with more satellites) n-rr drastically improves its performance, since there is less chance to reach a situation where an agent must skip one RT.  
Figure 18: Average utility profiles (in leximin order) for each constellation size and each algorithm (south: best utility over all agents; west: second best utility; north: third best utility; east: worst utility), for partial request satisfaction mode and encoded as V-DPAP.  
Figure 19: Performance metrics obtained by each algorithm for each constellation size, for flexible requests encoded as R-DPAP. (**a**) Normalized utility; (**b**) global reward; (**c**) computation time.  
Figure 20: Average utility profiles (in leximin order) for each constellation size and each algorithm (south: best utility over all agents; west: second best utility; north: third best utility; east: worst utility), for partial request satisfaction mode and encoded as R-DPAP.  
These results suggest that in the case of R-DPAP with full request satisfaction mode, algorithm greedy offers the best trade-off in quality/time as it allows a fair allocation to be reached with a high global utility (as for other algorithms) but in much less time.