esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Hybrid Topology Architecture
### Implementation
#### 3.2.1 End Devices  
The end devices used to implement the network are Pycom Lopy4 [30] development modules (see Figure 5). These modules support WiFi, Bluetooth, LoRa, and Sigfox, but this implementation of the end device only uses the LoRa physical layer. Each device is equipped with a Pycom Makr expansion board and a 915 MHz antenna attached with a U.FL connector. Visual studio code [35] was the integrated development environment (IDE) selected to upload the code into the Pycom devices through the serial port. This IDE was equipped with the PyMakr plugin [36] that enabled compatibility with the development modules. The programming was performed using the MycroPython ([[https://micropython.org/](https://micropython.org/)]([https://micropython.org/](https://micropython.org/)) accessed on 24 February 2025.) programming language. This language has been optimized to operate with microcontrollers and limited or restricted environments. The programming of the end devices works with the topology manager to change the topology. This programming allows the data generated by the sensors integrated into the end device to be transmitted and listens to the requests forwarded from the LoRaWAN server, which include the transmission mode change petitions generated by the topology manager. The \"LoRa\" class from the \"network\" software (version 1) module provided for Pycom devices was used to construct and send the LoRaWAN messages. The process of identifying topology change petitions, the implementation of each operation state (star, hybrid, or mesh), and the functionality allowing for the topology changes were developed as part of the proposed solution. All the topology modes are implemented into the end devices, and therefore, the topology change petitions only indicate the mode that needs to be executed. This implementation is completely LoRaWAN server agnostic, as all the messages forwarded from the end devices follow LoRaWAN specifications.  
When the end device is in star topology mode, its operation is performed as the LoRaWAN specifications dictate for class A devices. When a message is received, it is analyzed to determine if it is a topology change petition. If it is, the end device executes the part of the code corresponding to the operation of the new topology mode. All transmission modes can receive and process topology change petitions, and appropriately execute the required changes. Therefore, if the end device changes into the hybrid topology mode, it receives the messages from other end devices and forwards them to the gateway, and if  
Figure 4: Elements comprising the implementation of hybrid network topology for LoRaWAN.  
it changes into mesh mode, it forwards its messages to a neighboring end device. All the end devices in hybrid mode within the coverage area of an end device in mesh mode would receive the messages. However, duplicated messages at the MQTT broker are only forwarded once to the LoRaWAN server.
# A Survey of Advanced Border Gateway Protocol Attack Detection Techniques
## 5 Why the Need for a Group-Level AS Anomaly Detection Technique?
What are needed are anomaly detection techniques that can capture the collective dynamics of the mall during the incident (a BGP attack)--how different groups of individuals (ASs) are affected and respond in various ways--reflecting the dynamics of a large, interconnected system. For instance, the way some individuals quickly organize alternative plans might influence others to join them, or the calm demeanor of a seasoned traveler might reassure those around them. These collective dynamics provide a richer, more nuanced understanding of the situation, akin to completing a puzzle and understanding how each piece contributes to the whole picture. In the context of BGP, techniques capable of analyzing the group dynamics of ASs during an incident may offer insights that are more detailed and valuable than those obtained from analyzing single ASs or correlating multiple single ASs. This requires us to have the ability to observe and analyze multiple ASs simultaneously, capturing the intricate interplay among them. A multi-observational, grouped AS approach mirrors the reality of BGP operations within the complex, distributed system of the Internet, where ASs are interconnected in a dynamic and distributed manner. The importance of addressing the visibility limitations of current approaches is only heightened with recent descriptions of'smart' BGP attacks that can avoid public routing infrastructure, specifically targeting visibility limitations and vulnerabilities [15]. A promising direction for enhancing the exploration of group-level AS BGP anomaly detection also involves leveraging recent advancements in strategic VP methodologies for optimized data collection (see [48]). A range of approaches exist for the detection of BGP anomalies. An earlier review of BGP anomaly detection techniques categorized detection approaches into machine learning, reachability-based approaches, statistical pattern recognition, time series analysis, and validation studies based on historical BGP data [11]. While [11] further categorized each study by technique, type of anomaly detected, and whether the source of the anomaly could be identified, several attacks demonstrate the requirement for additional next-generation BGP anomaly detection criteria. We add group-level AS MVP as a requirement for next-generation anomaly detection, given that sophisticated BGP attacks have continued to evolve [15; 36].
# Future Internet Architectures on an Emerging Scale--A Systematic Review
## 3 Evolutionary and Clean Slate Approaches
### Key Differences between Evolutionary and Clean-Slate Architectures  
The internet has grown tremendously over the years, with different layers of abstraction and networking support provided by various protocols. However, it still faces challenges in supporting all the networking use cases, such as congestion management, mobility, security, QoS, and inter-domain routing. Despite the progress that has been made, these challenges limit the capabilities of the current internet [50]. As a result, there is a need for innovative approaches to network architectures that can address these limitations and provide a more flexible and secure platform for future applications.  
The evolutionary approach holds the potential to offer solutions that enable new services and functionalities while preserving the existing internet architecture. This approach places emphasis on the significance of the context or environment in which a proposed architecture solution will be deployed, rather than designing entirely new protocols from scratch.  
Research on clean-slate architecture has aimed to deliver services that meet the needs of users. For instance, [PERSON] et al. proposed that a clean-slate approach is the most effective solution for advancing the current internet architecture. In contrast, the evolutionary approach advocates for small, incremental modifications to the existing internet architecture that do not disrupt the existing protocols.  
To pursue clean slate networking research that accommodates various resources, it is crucial to compare its performance with that of evolutionary research, which represents a completely different paradigm [23]. The clean-slate architecture addresses several key factors, including network security, scalability, mobility, and network management. For instance, NDN utilizes cryptographic signatures to sign each data packet, preserving the integrity and authenticity of the data for network security [51]. NEBULA uses reliable, trustworthy core networking with caching to achieve scalability [43]. Clean-slate approaches, such as NDN, use soft-state mechanisms that employ consumer and producer mobilities to achieve an overall implementation for the mobility [52]. Additionally, NEBULA incorporates software controllers into its design to support network functionality and makes use of the SDN principles that are a part of the evolutionary design for network management [42]. These comparisons reveal that clean-slate approaches provide reasonable solutions when compared to evolutionary models, which solely focus on protocol design for the current internet environment [53].
# Bending Performance of a Prestressed Concrete Composite Girder Bridge with Steel Truss Webs
## 5 Influence Analysis of Key Parameters
### Height-to-Span Ratio of the Composite Girder  
In this paper, it is calculated that the height-to-span ratios of 19 completed PC composite girder bridges vary between 1/10 and 1/20, as shown in Figure 15. In order to analyze the influence of the height-to-span ratio on the mechanical properties of the composite girder with steel truss webs, the bending properties of the PC composite girders with height-to-span ratios of 1/16.25, 1/13.5 (the test specimen), 1/11.5, 1/10, and 1/9 were obtained by only changing the height of the test specimen. In addition, in order to analyze the influence of the eccentrical effect of the vehicle load on the above five kinds of composite girders with different height-span ratios, the arrangement of the concentrated load \(P\) in the longitudinal bridge direction is consistent with the test. As for the transverse  
Figure 14: Crack distribution obtained by FE analysis.  
Figure 12: Crack distribution (unit: cm).  
Figure 13: Photo of failure mode: (**a**) Overall deformation; (**b**) Crack of specimen.  
bridge direction loading, two conditions are considered: centric and eccentric loadings. Specifically, the concentrated force was applied to the center of the transverse bridge in the centric load, and the concentrated force was applied to one side of the steel truss web in the eccentric load. In the analysis of the eccentric load effect, two cases of considering the girder dead load \(P_{\text{d}}\) or not were discussed. A total of 20 FE models with different parameters were analyzed in this paper.  
In the elastic stage (where \(P=60\) kN), Figure 16 illustrates the variation in deformation and bottom plate stress with respect to the height-to-span ratio. A noticeable trend is observed where deformation and stress decrease with an increasing height-to-span ratio. The main reason for this is that when the span of the composite beam is unchanged, the height of the girder increases with the increase in the height-to-span ratio, resulting in an increase in the bending stiffness of the girder. Therefore, under the same applied load, the stress and deflection of the girder decrease.
## 4 Design of Dynamic Cache Allocation Mechanism  
Aiming at the problem that cache resources in ICN are shared by multiple reliable multicast groups and the existing cache allocation mechanism is not reasonable enough, this section proposes a dynamic cache allocation mechanism (DCAM). DCAM selects three factors including the number of NACKs of multicast groups, the distance from the MTN to the multicast source, and the arrival rate of the multicast group in the network, which respectively reflect the packet loss probability, node depth, and transmission rate of the multicast group. By comprehensively considering these three factors, DCAM evaluates the cache requirements of the multicast group and adjusts the cache quota of the multicast group in time to optimize the overall loss recovery delay and cache resource utilization. This section first defines the above three factors as NACK weight, distance weight, and rate weight, and then gives the calculation method of the normalized cache quota weight and the cache quota. The overall pseudo code of DCAM is shown in Algorithm 1.  
```
Input:\(C\), \(\alpha\), \(\beta\), \(\gamma\), \(\theta\) Output:\(C_{i,n}\)
1:Initialization:\(\alpha=0.3,\ \beta=0.3,\ \gamma=0.4,\theta=0.9\)
2: In the construction of the multicast tree, each MTN records the number of hops \(H_{i,n}\) from itself to each multicast source and calculates the total number of hops by \(\sum_{j}^{M}\ H_{j,n}\)
3: Each MTN records the number of NACK \(NACK_{i,n}\) of the multicast group \(i\) in the \(n\)-th statistical period, and calculates weighted moving average of NACK \(EWMA\_NACK_{i,n}\) using Equation (1), then calculates the total number of NACK by \(\sum_{j=1}^{M}EWMA\_NACK_{j,n}\)
4: Each MTN records the number of multicast data packets \(S_{i,n}\) of multicast group \(i\) in the \(n\)-th statistical period, then calculates the total multicast data packets by \(\sum_{j}^{M}\ S_{j,n}\)
5:for multicast group \(i\) in \(\{GUMSID_{1}\ldots GUMSID_{M}\}\)do
6: Calculate NACK weight \(N_{i,n}\), distance weight \(D_{i,n}\), and rate weight \(R_{i,n}\) using Equations (2)-(4), respectively
7: Calculate cache weight \(W_{i,n}\) using Equation (5)
8: Calculate the final cache quota weight \(QW_{i,n}\) using Equation (6)
9: Update the cache quota \(C_{i,n}\) of multicast group \(i\) in the \(n\)-th statistical period using Equation (7)
10:return\(C_{i,n}\)
```  
**Algorithm 1**The Dynamic Cache Allocation Mechanism
## 3 Methodology
### Does BM0 Meet the Criteria of New Regulations?  
The eigenperiod of the structure is \(T_{i}=T_{X}=T_{Y}=0.264\) s. We obtained a soil coefficient for type-B soil equal to \(S=1.2\). The period of the lower limit of the constant spectral acceleration branch is \(TB=0.15\), the period of the upper limit of the constant spectral acceleration branch is \(TC=0.5\), and the value defining the beginning of the constant displacement response range of the spectrum is \(TD=2.5\).  
According to the provisions of EC8 regarding the value of the behavior factor q, this value can be calculated for each direction from the equation \(q=q_{o}\cdot k_{w}\) (SS5.2.2.2(1) of EC8 [24]) where \(q_{o}\) is the basic value of the behaviour factor, dependent on the type of the structural system and on its regularity in elevation, and \(k_{w}\) is the factor reflecting the prevailing failure mode in structural systems. We assumed that \(a_{u}/a_{1}=1.2\), (EC8 SS5.2.2.2(5) [24]), \(k_{w}=1\), \(q_{o}=3\) and finally coefficient q took the value of: \(q=q_{o}\cdot k_{w}=3.6\). The elastic response spectrum for \(T_{B}<T_{X}=T_{Y}<T_{C}\) is defined by Equation (1), as follows:  
\[S_{d}(T)=0.24\cdot g\cdot 1.2\cdot\frac{2.5}{3.6}=1.962\ \mathrm{m/s}^{2} \tag{1}\]  
From the analysis of the vertical loads of BM0, it is noticed that the seismic base shear load is \(\Sigma\)F = 20236 kN. In order to meet the requirements of current anti-seismic regulations, the appropriate/needed seismic base shear is calculated by the equation \(\Sigma F=S_{d}(T)\cdot\Sigma N_{C+0.3Q}/g\), equal to \(\Sigma\)Fneeded = 4007.20 kN in each direction. Therefore, the building must be able to receive a base shear load greater than 4007.20 kN to achieve the criteria of Eurocode 8 [24].
## 4 Cyber Security Attacks in Vehicular Ad Hoc Networks (VANETs)
### Data Integrity Attacks in Autonomous Vehicles  
Data integrity is pivotal in autonomous vehicle systems, forming the bedrock of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. It is crucial for everything from navigation to collision avoidance [63]. However, the risk of data manipulation is omnipresent, either through unauthorized intrusion or malicious intent. To preserve data integrity, autonomous vehicles employ secure communication protocols and advanced encryption methods.  
One commonly encountered breach is the _masquerading attack_, where the perpetrator impersonates a legitimate entity within the system. This can cause traffic disruption, trigger accidents, or potentially allow the attacker to control vehicle systems or extract sensitive data [39]. Therefore, autonomous vehicles must incorporate systems to authenticate the received information or signals.  
Addressing these challenges, [PERSON] et al. introduced the secure and privacy-enhancing communications schemes (SPECS) system [64]. SPECS leverages pseudo identification and a shared secret key between an autonomous vehicle and roadside units (RSUs) to maintain the vehicle's identity confidentiality. Even when pseudonyms are used for communications, RSUs can verify the signatures, thus preventing masquerading attacks [63, 64].  
Another potential breach of data integrity is represented by the replay attack or playback attack. This attack involves the malicious repetition or delay of valid data transmissions [39]. Modern security designs using robust cryptographic systems, including digital signatures and nonce inclusion in messages, are typically effective at countering these attacks.  
However, the challenge is amplified when the attacker is a trusted insider, like a compromised vehicle with a valid certificate. Anomaly or misbehavior detection systems, such as those discussed in [65], are often employed to manage these scenarios. However, these systems have inherent limitations concerning false negatives and positives and rely on a variety of data sources that might not always be available. Consequently, further research is required in anomaly detection to ensure the robust operation of these algorithms and the consequent protection of autonomous vehicles.
## 3 Results and Discussion
### Stakeholder's Influence in Implementing CE Principles
#### 3.4.3 Consultant  
The interviewees identified the consultant as the third most influential stakeholder in implementing CE principles with a total of 22 points. There was a consensus of the consultant's significant role in providing technical expertise and guidance throughout the project lifecycle. Consultants can also support designers by highlighting the environmental and economic benefits of design choices. CT1 pointed out that \"_Consultants are generally trusted by owners because they do not have a direct economic interest in misleading the client. When a client hires a consultant, the consultant is paid regardless of what they recommend, so they are more likely to provide honest advice. This trust in consultants is why they play a valuable role in influencing design decisions.\"_ C3 discussed the role of consultant by highlighting that \"_A client sometimes does not have required skills to determine their needs so consultants or project management team can use their expertise to introduce, implement all best practices related to CE. Consultants or PMT could easily identify opportunities and threat which can help to move to sustainable practices. As Knowledge transfer is a unique process to ensure effective knowledge sharing about CE strategies. Therefore, consultants play a vital role in this regard\"._
# Floating Photovoltaic Plant Monitoring: A Review of Requirements and Feasible Technologies
## 5 Technologies for Autonomous Water Basin Mapping and Monitoring  
FPV systems' autonomous monitoring presents unique challenges, requiring advanced robotic systems for both surface and underwater monitoring to optimize performance and minimize environmental impact. Unmanned aquatic vehicles are increasingly accessible to reservoir operators [78] due to rapid component development and cost reductions, fueled by advancements in IoT and multiparametric probes (see Section 4).
## 2 Sensing Mechanism  
The designed sensor consists of three parts, the MSC, the polarization controller (PC), and the sensing area of this sensor, as shown in Figure 1.  
In the sensor, the light beam is coupled into the fiber by Flange and propagates as the fundamental mode into the MSC. The MSC is made from two fused fibers and is used to produce a pure higher order mode in the visible band. In general, as the fundamental mode propagates in the fusion region, the energy transitions periodically from one fiber to another, propagating as the LP\({}_{11}\) (LP\({}_{21}\)) mode when the coupling length increases. It is, therefore, possible to optimize the MSC parameters so that the fiber output is purely the LP\({}_{11}\) (LP\({}_{21}\)) mode. An optical isolator is used to avoid reflections from the fiber output affecting the output LP\({}_{11}\) (LP\({}_{21}\)) mode.  
Figure 1: Schematic diagram of SPR sensor with a pure higher-order mode.  
The PC controls the light field distribution of the modes by adjusting the polarization direction of the higher order modes, which in turn concentrates the power for excitation of the SPR in the sensing area.  
The sensing area of this sensor for RI measuring is made by stripping part of the cladding from the few-mode fiber and coating it with silver film to excite SPR in the solution. Then, the output light intensity through different RI solutions is received by the detector, where the sensitivity and RI resolution of the sensor can be measured.  
Based on the proposed fiber SPR sensor, in order to obtain a higher order mode with a higher power proportion for full excitation of the SPR, in the next section, the power proportion of different higher order modes in the cladding is theoretically analyzed and an MSC with a central wavelength of 500 nm is designed. In addition, all parameters related to the sensor and used in the Section 3 and the Section 4 are listed in Table 1.
## Abbreviations  
\begin{tabular}{l l l l}
**SCADA** & Supervisory Control and Data Acquisition & **DG** & Distributed Generation \\
**SGs** & Smart Grids & **DER** & Distributed Energy Resource \\
**IED** & Smart Electronic Device & **HEMS** & Home Energy Management Systems \\
**IoT** & Internet of Things & **I-Energy** & Integrated-Energy \\
**ToU** & Time of Use & **ACP** & Active Consumer Participation \\
**VPP** & Virtual Power Plant & **Pros (dp)** & Prosumer Energy \\
**VPSs** & Virtual Power Stations & **Erse** & Charging stations \\
**SG 2.0s** & Multi-Agent Systems & **HEMS** & Home Energy Management System \\
**AMI** & Advanced Metering Infrastructure & **EEA** & Energy Efficiency/conservation Agent \\
**DRA** & Demand Response Agent & **TOUA** & Time Of Use Agent \\
**LSA** & Load shifting Agent & **SHA** & Smart Home Agent \\
**SMA** & Meter Agents & **GNSS** & Global Satellite Navigation System \\
**PBNM** & Uniform Communication Standard BNM & **GUI** & Graphical User Interface \\
**UC** & User Comfort & **ADLs** & Daily Activities \\
**AWT** & Average Waiting Time & **HEMS\_OST** & Operation schedule time \\
**PAR** & Peak-to-Average Ratio & **HEMS\_ODA** & Operation duration for activity \\ \(\Phi\) & Scheduling time horizon & **SHES\_OST** & Scheduling Time \\
**SPEED** & Sequence Prediction via Enhanced Episode Disco & **PESM** & Prosumer Energy Sharing and Management \\
**PAg** & Smart meter information shows how Prosumer & **DSM** & Demand-Side Management \\
**FIPA** & Foundation for Intelligent Physical Agents & **JADE** & Java Agent Development Framework \\
**JIAC** & Java-based Intelligent Agent Component ware & **AgSpr** & Intelligent Agents SPRINGS \\
**JACK** & Intelligent Agents & **AgTra** & Intelligent Agents Tracy \\
**JADEX** & Intelligent Agents & **AgServ** & Intelligent Agent Service \\
**SCNM** & Smart Grid Network Management & **PCGs** & Prosumer Community Groups \\
**MAL** & Microgrid Agents Layer & **DAGL** & DER Agents Layer \\
**PAL** & Prosumer Agents Layer & **CAL** & Component agent Layer \\
**LLAL** & Loads Layer agents Layer & **SGFH** & Fault Handling \\ \end{tabular}
## 2 Inertial Aided PLL in Compass/INS Integrated Navigation System
In a highly dynamic situation with satellite-receiver line-of-sight jerk, if there is large offset or drift in inertial aiding information, the aided Kalman-PLL will diverge.