esa-sceva/satcom-chunk-collection huggingface dataset:
# Flexible Phased Antenna Arrays: A Review
## 1 Introduction
### Flexibility in Phased Arrays  
After the utilization of fully integrated silicon-based RFIC technology in PA, the next step was to introduce flexibility in PA systems. Flexible Phased Arrays (FPAs) are significantly important in terms of applications like avionics, automotive, warfare, aerospace or even wearable devices. Practical utilization of FPAs started in early 2000s with the development of active PA antenna using membrane technology [33] and Micro-Electro-Mechanical System (MEMS) micro-strip phase shifters on a flexible Liquid Crystal Polymer (LCP) substrate [34]. They led to the development of fully integrated PA system on a single chip with RF-MEMS switches on a flexible LCP substrate for the first time in 2008 [35]. When flexibility is introduced, the resulting deformation changes the geometry of the antenna array. For the preservation of the radiation pattern, a phase compensation mechanism was developed for a self-adapting FPA [36]. In a similar work, more recently, another self-adapting array was developed which was able to dynamically compensate for performance degradation due to real-time deformations [37]. Manufacturing of FPAs has evolved over time with the advancement in Additive Manufacturing (AM) technology. For example, Inkjet Printing (IP) was used for the fabrication of an FPA system using a flexible Kapton substrate in 2013 [38]. AM precisely deposits materials on flexible substrates, paving way for lightweight, and adaptable systems. Despite the revolutionary potential, the evolution of technology still faces some significant challenges such as material limitations, mechanical deformation effects, thermal management, and design complexity.
# Pico-Sat to Ground Control: Optimizing Download Link via Laser Communication
## 4 Results  
In this section, we present both simulated-in-field results. First, we present FSO link-budget analyses between a pico-satellite and an optical ground station. Then, we elaborate on the required improvements for obtaining a 1 Gbps FSO Link. Next, we focus on both lab and field experiments that validate the applicability of our proposed model. Finally, we present experimental results related to the launched pico-satellite SATLLA-2B.
# A Robust and Energy-Efficient Weighted Clustering Algorithm on Mobile Ad Hoc Sensor Networks +
## 3 Description of RE2 WCA Algorithm  
### Energy Consumption Model  
The battery power of each nodes in network are assigned identically. Equations (1) is adopted to calculate the energy consumption of sending data and Equation (2) for receiving data [15]:  
\[\left\{\begin{array}{ll}E_{t}(d,l)=lE_{elce}+l\epsilon_{fg}d^{2}&if\ d<d_{0} \\ E_{t}(d,l)=lE_{elce}+l\epsilon_{amp}d^{4}&if\ d>d_{0}\end{array}\right. \tag{1}\]  
\[E_{r}(l)=lE_{elce} \tag{2}\]  
In Equations (1) and (2), \(E_{dec}\) is required energy for activating the electronic circuits; if the transmission distance is less than the threshold \(d_{0}\), the consumption of energy amplification adopts the free space model. If the transmission range is greater than the threshold \(d_{0}\), the energy amplification adopts the multipath attenuation model. \(\varepsilon_{f}\)s and \(\varepsilon_{amp}\) are required energy for amplification of transmitted signal to transmit one bit in open space and multipath models respectively; \(l\) denotes the number of bits of data; \(d\) is the distance between two nodes; \(E_{t}(d,l)\) is the energy consumption of the sending end; \(E_{r}(l)\) is the energy consumption of the receiving end. The residual energy [6] of each node is calculated by Equation (3):  
\[E_{i}^{residual}=E_{i}^{initial}-E_{t}(d,l)-E_{r}(l) \tag{3}\]  
In Equation (3), \(E_{i}^{initial}\) is the initial or max energy of each node \(i\); \(E_{i}^{residual}\) demonstrates the residual energy of each node \(i\) after transmitting data.  
When each node begins to send data packets, the summation of energy in each cluster can be calculated. In this paper, one of the significant threshold is topology maintenance energy threshold value. When the average of CHs' energy lower than this value, the re-clustering process will be implemented.  
Taking the difference of energy of each cluster into account, the average energy within cluster is implied as the inner cluster average energy. Only when the residual energy of cluster head is lower than the value, the process of new cluster head selection will start. Therefore, Equation (4) is adopted to calculate the inner cluster average energy. In Equation (4), \(C_{k}\) represents each cluster and if a node belongs to one of the clusters, then \(j\in C_{k}\).  
\[E_{k}^{age}=\frac{\sum\limits_{j\in C_{k}}E_{j}^{residual}}{\sum\limits_{j \in C_{k}}i_{j}} \tag{4}\]
# Signature-Based Security Analysis and Detection of IoT Threats in Advanced Message Queuing Protocol
## 6 Analysis and Validation of Statistical Model for AMQP IoT Attack Detection  
This section presents the analysis and validation of the statistical model used to detect attacks within the AMQP IoT protocol. The methodology involves leveraging a dataset generated from network traffic captured during the experiments described earlier. This dataset provides the foundation for identifying the unique features and signatures of various attack types.  
The dataset [25] was generated by capturing network traffic from IoT devices communicating via the AMQP protocol. Using our testbed setup, the data collection process included a wide range of scenarios, encompassing normal operations, malicious activities, and attack traffic. Packets were categorized into three classes:  
* **Normal Traffic**: Data packets representing typical, unaltered communication between IoT devices and the AMQP server.
* **Malicious Traffic**: Packets exhibiting abnormal behavior or unauthorized activities indicative of potential security threats.
* **Attack Traffic**: Packets directly associated with known attack patterns, such as DoS, MiTM, or brute force attacks.  
This classification enabled a comprehensive analysis of network behavior, facilitating the identification of the distinct features that define each attack signature.  
To analyze the dataset and validate attack detection mechanisms, a Python-based statistical model script was developed. The script identifies key features from the captured traffic and classifies packets based on the signatures defined in earlier sections. The statistical model was tested against three types of datasets:  
* **Benign Dataset**: Contains only normal traffic to evaluate false positive rates.
* **Malicious Dataset**: Comprises known malicious and attack traffic for accuracy testing.
* **Mixed Dataset**: Includes both normal and malicious traffic to simulate real-world scenarios and measure overall detection performance.  
The statistical model also incorporates a visualization component to facilitate analysis and enhance understanding of network traffic patterns. A color-coded scheme is used to represent different traffic categories:  
* **Blue**: Represents normal traffic.
* **Gray**: Indicates malicious traffic.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Features** & **Abnormal Traffic** \\ \hline TCP Three-Way Handshake & RST/ACK \\ RST/ACK Packet & tcp.len=0 \(\mid\) seq=509 \(\mid\) nxtseq=509 \(\mid\) \\ tcp.srcport=5672 \(\mid\) window.size=508 \\ Syslog & Invalid credentials on port 5672 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Abnormal traffic features of AMQP brute force attack.  
* **Red**: Denotes attack traffic. This visual approach simplifies the evaluation process by enabling clear identification of anomalies and patterns within the traffic data. By combining automated classification with visual representation, the statistical model provides a robust framework for AMQP IoT attack detection.
## 3 Results  
Table 1 presents the VH values of the eroded enamel after applying remineralizing and regenerative agents. The outcomes have shown that, in Group 3, the (NR-5 TM)-treated teeth exhibited the highest VH values (333.0 \(\pm\) 82.3). However, in Group 1, the (No remineralizing agent)-treated specimens displayed the lowest VH (235.4 \(\pm\) 32.2) score. Through an intergroup comparison analysis, we discovered that the Group 3, Group 4 (TF), (293.0 \(\pm\) 67.2), and Group 5 (CO2 laser + TF) (310.8 \(\pm\) 77.3) samples displayed comparable findings of VH. Furthermore, Group 2 (CurodontTM Repair) (250.5 \(\pm\) 50.2) exhibited no significant difference in their hardness number when compared with the Group 1 samples.  
Table 2 shows the mean and SD of the \(\mu\)SBS scores in MPa for each trial group. The results of the existing study have revealed that, in Group 3, the (NR-5 TM)-(9.99 1.45 MPa)-treated study samples presented the maximum bond integrity values of tooth color restoration to erosive enamel. However, it was found that the Group 1 (No remineralizing agent) samples demonstrated the lowest \(\mu\)SBS score (8.161.22 MPa). An intergroup comparison analysis has revealed that Group 3, Group 4 (TF) (9.171.44 MPa), and Group 5 (CO2 laser + TF) (9.551.26 MPa) presented comparable outcomes of bond strength. Similarly, the Group 2 (CurodontTM Repair) (8.66 1.15 MPa) samples exhibited no significant difference in their \(\mu\)SBS when compared with the Group 1 testers.  
Figure 1 displays the failure mode observed within the experimental groups. The predominant form of failure found was cohesive, specifically in the case of the NR-5 TM, CO2 laser + TF-, and TF-treated groups. In contrast, the control group and the CurodontTM Repair-treated group exhibited adhesive and admixed failure the most.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Remineralizing Pretreatment** & **Mean** &
\begin{tabular}{c} **Standard** \\ **Deviation (SD)** \\ \end{tabular} & \(p\)**-Value!** \\ \hline Group 1: No remineralizing agent & 235.4 \({}^{\text{a}}\) & 32.2 \\ \hline Group 2: CurodontTM Repair & 250.5 \({}^{\text{a}}\) & 50.2 \\ \hline Group 3: NR-5 TM & 333.0 \({}^{\text{b}}\) & 82.3 \\ \hline Group 4: TF & 293.0 \({}^{\text{b}}\) & 67.2 \\ \hline Group 5: CO2 laser + TF & 310.8 \({}^{\text{b}}\) & 77.3 \\ \hline \hline \end{tabular} Topical fluoride (TF), CO2 laser irradiation + topical fluoride (CO2 laser + TF). The different small letters denote a statistically significant difference, showing significant differences among the study groups (ANOVA and Tukey multiple comparison test).  
\end{table}
Table 1: VH scores after treatment with different remineralization protocols.
## 3 Results and Discussion
### Carboxymethylcellulose Characterization
#### 3.1.4 Pro-Collagen I \(\alpha\) I Production
When considering only the intracellular data (Figure 5a), one can see that only two samples (CMC1 and CMC2) significantly (\(p<0.05\)) modulated collagen production with CMC2 increasing collagen production by ca. 228% relative to the basal level of production in the control. When considering the intrinsic properties of the CMC molecules assayed, it is interesting to see that the highest pro-collagen \(1\)\(\alpha\) I productions were obtained for CMC2 which showed the highest DS and \(M_{\text{tr}}\). Moreover, sample crystallinity does not appear to be a differentiating factor between these samples, as CMC4 had the highest crystallinity index of all samples assayed and produced an increase of only 11% in pro-collagen I \(\alpha\) I production. While the lack of previous works on this topic makes reaching any definitive explanation for this behavior difficult, some analysis can still be performed with support from the literature. In line with what was discussed here, [PERSON], [PERSON] [59] reported that levan, pluronic, and CMC hydrogel enhanced collagen production in human fibroblasts. Similarly, [PERSON] and [PERSON] [60] showed that a complex CMC scaffold led to increases in collagen production from osteosarcoma cells. Another fact to take into consideration is that bacterial cellulose has been shown to stimulate Type I collagen production both as a scaffold and as a hydrogel [61; 62]. If one disregards the intrinsic differences between molecules--bacterial cellulose is composed of type I cellulose with a parallel arrangement of chains and is more crystalline than CMC, which is composed of type II cellulose with an antiparallel arrangement of chains and lower crystallinity--it is possible that still unknown intrinsic properties of cellulose may be at play [52; 63; 64; 65].
# An Energy-Efficient and Robust Multipath Routing Protocol for Cognitive Radio Ad Hoc Networks
## 4 Energy-Efficient and Robust Multipath Routing  
In this section, the proposed ERMR protocol for CRAHNs is presented in detail. In ERMR, energy-efficient primary and backup paths are built to counteract route failures for a source-destination pair. The route discovery process starts only when both primary and backup routes break down. The selection of next-hop nodes based on their residual energy not only conserves their energy for latter sessions but also ensures balanced energy consumption throughout the network. It is guaranteed that the paths considered are the most energy-efficient paths with respect to the energy consumed in transmission, reception, channel switching, and idle listening.  
It is worth noting that the route discovery procedure is carried out in collaboration with the channel selection. At each link, the selection of the next-hop node is performed simultaneously with channel allocation. The assignment of a better channel to each link can boost the overall performance of the network. Hence, the channel assigned is the most stable one in terms of access duration. The proposed ERMR protocol consists of route discovery, route reply, data transmission, and route maintenance phases.
## 4 Discussion  
This article presents a new Hybrid Method which combines two optimization methods (a Genetic Algorithm and a conjugate gradient) of a very different nature. The test case used to evaluate the performance of the new Hybrid Method is a single objective optimization with five design variables associated with a Synthetic Jet actuator. The CFD simulations are solved using a URANS model, which has proven precise enough to capture the active flow actuator effect and to compare between different designs. For the test case of the Synthetic Jet actuator, the Hybrid Method has outperformed both of the traditional optimization algorithms. The new Hybrid Method successfully combines the best characteristics of both optimization algorithms. It shows a great convergence rate, like a gradient based method, but without the lack of robustness that usually comes with gradient based methods and complex applications. The increase in robustness is provided by the Genetic Algorithm player which avoids local minimums. The strategy to exchange information between the two optimization algorithms proposed in this Hybrid Method has proven efficient to overcome the main drawbacks of both classical optimization methods, and the result is an optimization method capable of exploring and exploiting the full search space.  
The future work aims to further test the new Hybrid Method in a multi-objective optimization problem such as the one introduced in [PERSON] et al. [20]. In high CPU demanding applications, such a Synthetic Jet actuator optimization is important when working with optimization algorithms that require a low number of evaluations of the physical model, and the new Hybrid Method has proven a good option.  
Conceptualization, M.C., N.M.T., J.P.-P., G.B. and J.M.B.; methodology, M.C. and N.M.T.; software, M.C. and N.M.T.; validation, M.C., N.M.T., J.P.-P., G.B. and J.M.B.; formal analysis, M.C., N.M.T., J.P.-P., G.B. and J.M.B.; investigation, M.C., N.M.T., J.P.-P., G.B. and J.M.B.; resources, N.M.T. and J.M.B.; data curation, M.C. and N.M.T.; writing--original draft preparation, M.C.; writing--review and editing, M.C., N.M.T., J.P.-P., G.B. and J.M.B.; visualization, M.C. and N.M.T.; supervision, J.P.-P., G.B. and J.M.B. All authors have read and agreed to the published version of the manuscript.  
This research has been partially supported through the Severo Ochoa Centre of Excellence (2019-2023) under the grant CEX2018-000797-S funded by MCIN/AEI/10.13039/501100011033.  
The third author, [PERSON], is a Serra Hunter Fellow.  
The authors declare no conflict of interest.
# A Comparison of Blockchain Recovery Time in Static and Mobile IoT-Blockchain Networks
## 5 Evaluation
### Emulated IoT-Blockchain
#### 5.2.1 Static Scenario
Figure 8a shows the average, maximum, and minimum recovery time for the ten experiments in the static scenario with the IoT-blockchain system and three routing protocols. The results show that BATMAN  
Figure 6: Emulated IoT-blockchain system. (**a**) Ad-hoc network connection; (**b**) Blockchain connection. achieves the shortest recovery time. It has an average recovery time of 5.2656 s, which is 69% faster than OLSR and 60% quicker than BABEL. When the sender transmits the transactions to the receiver, the failure of one of the two-hop relay nodes, device2, prevents the sender from continuing to receive OGM packets from it. However, the sender keeps receiving OGM packets from the other two-hop relay nodes (e.g., IoT device6 and IoT device7). Once the number of OGM packets received from the two nodes is more significant than that obtained from IoT device2, BATMAN can select a new relay node. A closer examination of the different components of the average recovery time is provided in Figure 8b. In OLSR, \(T_{MPR\_lost}\) and \(T_{NEICHB\_HOLD\_TIME}\) consumed 82.2% of the total recovery time in this evaluation. On the other hand, the value of \(T_{pure\_blockchain}\) is about 17.8%. For BABEL, \(T_{neighbor\_update}\) occupied 90.8% of the total process's time and \(T_{pure\_blockchain}\) occupied 9.2%. For BATMAN, \(T_{switch\_next\_hop}\) was about 57% of the total recovery time and \(T_{pure\_blockchain}\) was about 43%.
## 2 Regional Seismic Risk: Assessment and Mitigation through a Simplified Procedure
### Building Stock Definition  
Regional seismic risk assessment requires finding a reasonable compromise between the completeness of building information and correctness of analysis results. In the procedure assumed in this work, the following data are needed for each building: geographical location, age of construction, geometry in plan and elevation (number of storeys, total height and width, number of frame ways and bay length), structural details (columns and beams cross-section and reinforcement) and material's strengths. Considering that the first three types of information could be easily collected in a reasonable time window by survey or from national census, they are considered as deterministic data, assumed to be known. In a different way, the knowledge about materials and detailing from original design documents is probably not accessible without carrying out destruction tests on structural members. Considering the unfeasibility of this type of investigation in large-scale seismic risk analysis, these data are considered to be unknown. They are treated as random variables (RV) with probability distributions characterized by mean value \(\mu\) and standard deviation \(\sigma\) that can be assumed on the basis of eventual available data in design documents, related references or expert judgement in relation to the known age of construction. The full factorial design method is assumed in the procedure for the design of experiments [20] and three points are chosen for each RV (namely \(\mu\), \(\mu+\sigma\), \(\mu-\sigma\)). Consequently, 3\({}^{\mathrm{k}}\) possible realizations of each building are considered in the process, where k is the number of RV. In the procedure proposed in [19], 11 RVs are assumed, thus leading to 3\({}^{11}\) = 177\({}^{\prime}\)147 realizations for each building. This high number allows decision makers to take into account uncertainty about the effective structural details and materials.
# Collision Risk Assessment and Operation Assistant Strategy for Teleoperation System
###### Abstract  
Teleoperation robots remain superior to fully automated robots in complicated and unstructured environments (e.g., in-orbit assembly). However, the collision risk is also greatly increased in these environments. Therefore, the teleoperation robot should possess the capability of collision risk perception and be configured with security assistance strategy to improve safety and efficiency. With this objective in mind, this paper proposes a collision risk assessment system based on fuzzy theory, which comprehensively considers the effects of shortest distance, operation speed, and delay time. The introduction of fuzzy theory makes the risk assessment results more accurate. Furthermore, this paper also proposes a creative discrete expandable bounding box method to make the calculation of the nearest distance between complicated obstacles and the robot more efficient and faster. Secondly, to improve the safety and efficiency of teleoperation, this paper proposes a set of teleoperation assistance strategies for teleoperation robots based on collision risk. The strategies include partial view highlighting, variation in the motion mapping ratio, and haptic risk warning, all of which are achieved in the graphical interactive interface. Finally, this paper verifies the proposed collision risk model and the operation assistant strategy through experiments. The results show that the proposed fuzzy collision risk model has a correct trend with risk factors, and the proposed operation assistance strategies can effectively reduce the robot collision risk and improve the safety of teleoperation robots. In conclusion, this research contributes to the collision risk assessment and obstacle avoidance assistance strategy of teleoperation robots.  
in-orbit manufacturing and assembly; teleoperation; fuzzy inference; collision risk assessment; obstacle avoidance