esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection and Localization of Multiple Damages through Entropy in Information Theory
## 5 Results
### Single and Multiple Damage Intensity Tracking  
The potentialities of the proposed approach have been tested as well for single and multi-damage tracking. Indeed, buried structures might not always be easily accessible for maintenance and reparation. Furthermore, a certain degree of damage tolerance is always considered at the design stage (see, e.g., [55]). Thus, it is possible that, due to practical or economic constraints, one or more examples of damage detected are not immediately repaired but rather put under surveillance. These examples of damage are then expected to extend over time, with an increase in their severity. Furthermore, other unrelated damage may occur nearby, compromising the already endangered situation.  
Figure 11 reports a damage pattern with 40%/60%/80% stiffness reduction. This can be considered as a scenario where, following the already damaged conditions portrayed in Figure 6b, a joint failure suddenly happens in the pipe section in-between the two pre-existent damages. By comparing these two cases (with and without artificial noise), one can see that the effects of damage #2 combine with the effects of the previous damages in \(\theta\) = 0\({}^{\circ}\) and 90\({}^{\circ}\), while also being detectable elsewhere as expected. Adding artificial noise to the measurement does not significantly affect the results.  
Similarly, Figure 12a,b depict a damage increase from 50%/0%/50% to 60%/0%/60% and 80%/0%/80% (for the noise-free and noise = 10% cases, respectively), emulating, e.g., uniform crack growth or corrosion progress at both locations. In this case, it is possible to note that the increase in the absolute value of the health state indicator can be perceived already by moving from 50%/0%/50% to 60%/0%/60% of stiffness reduction. Then, the increase is even more pronounced moving from the damage case 60%/0%/60% to 80%/0%/80%. The results are also quite consistent in the presence of a relatively high level of noise (i.e., 10%).  
In conclusion, comparing the different damage steps allows detecting the increasing reduction of WE after each subsequent additional damage. This does not allow precise estimates, yet permits some qualitative assessment of the worsening of the pipeline's structural integrity at those points already damaged.  
Figure 11: \(40\%/60\%/80\%\) damage scenario. (**a**) noise-free (to be compared with Figure 6b); (**b**) noise = \(10\%\) (to be compared with Figure 7b).
# Enhancing OLSR Protocol by an Advanced Greedy Forwarding Mechanism for VANET in Smart Cities
## 4 Proposed Work
### Simulation and Results
#### 4.4.3 End-to-End Delay  
We use the end-to-end delay to compare the two protocols OLSR and G-OLSR.  
For different traffic densities, we calculate the end-to-end time using the following \"awk\" code:  
Figure 13: Comparison between PDR and traffic density for OLSR and G-OLSR.  
The simulation results show that our approach has the best performance in terms of end-to-end delay. The EED of G-OLSR decreases more and more as the traffic increases, and this is due to the use of the GFA approach, which gives better results when the mobility and the traffic density increase. In addition, the use of the Dijkstra algorithm presents a high convergence rate and therefore a lower EED for a network for high traffic.  
When the traffic density situates between 200 and 400 vehicles, the EED increases for OLSR but remains stable for G-OLSR. This sudden change is made because the nodes are abundant within the network. The traffic density increases progressively, and the probability to find nodes also increases, so the EED decreases beyond 400 vehicles.  
Figure 14: EED vs. traffic density for OLSR and G-OLSR.
## 3 Experiments and Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **View** & **RDE** & **RMSE** & **SNR** \\ \hline \multirow{3}{*}{MGSA} & 42\({}^{\circ}\) & 39.71\% & 0.0315 & 2.157 dB \\ \cline{2-5}  & 45\({}^{\circ}\) & 41.72\% & 0.0689 & 1.4513 dB \\ \cline{2-5}  & 48\({}^{\circ}\) & 40.23\% & 0.0284 & 1.1362 dB \\ \hline \multirow{3}{*}{Phase encryption} & 42\({}^{\circ}\) & 94.0\% & 0.013 & 11.957 dB \\ \cline{2-5}  & 45\({}^{\circ}\) & 91.4\% & 0.0258 & 10.266 dB \\ \cline{2-5}  & 48\({}^{\circ}\) & 92.46\% & 0.023 & 10.884 dB \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Assessment results of reconstruction images simulated with multi-angle phase encryption algorithm. Figure 10: Schematic of optical setup. Figure 9: Reconstruction with same depth but different view (**a**) with phase-locked algorithm and (**b**) without phase-locked algorithm.
# Translation-Based Embeddings with Octonion for Knowledge Graph Completion
## 2 Related Work
### Semantics-Based Methods  
Semantics-based methods measure rationality by matching the latent semantics of entities and relations, most of which ultimately calculate the likelihood of triples through the product of matrix and vector.  
RESCAL [10] is the earliest semantics-based KGC method, which uses full-rank relational matrices \(\mathbf{M}_{r}\) to model latent pairwise interactions between head and tail entities, and optimizes a scoring function that computes a bilinear product \(\mathbf{h}^{\top}\mathbf{M}_{r}\mathbf{t}\) among head and tail entity embedded vectors and the relational matrix. Due to too many parameters, RESCAL has an overfitting problem. DistMult [27] improves the full-rank relational matrices \(\mathbf{M}_{r}\) of RESCAL to diagonal matrices, which alleviates overfitting to a certain extent. Subsequently, SimpIE [28] and TuckER [29] are proposed based on two types of matrix decomposition methods: CP and Tucker. TuckER achieves satisfactory results in KGC tasks, and proves that the above semantics-based methods are special cases of TuckER. Semantics-based methods thus achieve theoretical uniformity. ComplEx [30] expands DistMult from real-valued space to complex-valued space, and takes the real part of the result of complex multiplication as the final score. Since this operation is asymmetric, ComplEx can solve the problem that DistMult cannot model asymmetric relations. RotatE [31] models each relation of triples as a rotation operation from the head entity to the tail entity in complex-valued space, and has the capability to infer multiple relational patterns. QuatE [32] extends from complex space to quaternion space with higher degrees of freedom.  
Compared with translation-based methods, semantics-based methods better reflect the semantic information of triples in the design of scoring functions, while translation-based methods usually capture shallow features. However, semantics-based methods have more redundancies in computations, so it is easy to overfit. When modeling KGs with a large number of entities and relations, semantics-based methods have to raise the embedded space to a very high dimension in order to completely embed and separate all entities and relations, which leads to high memory and computational resource costs. Moreover, semantics-based methods generally have poor interpretability.
# DDTMS: Dirichlet-Distribution-Based Trust Management Scheme in Internet of Things
## 1 Introduction  
With the development of the Internet of Things (IoT) technology, the formation of relevant standards, as well as the deepening of industrialization, IoT security faces greater challenges. On the one hand, based on the analysis of the IoT security threats and security requirements, many studies put forward some ideas in the research work of the IoT security issues, and study the key technologies, products, and industrialization issues of the IoT security as soon as possible. On the other hand, for the security of system applications, considering the security of IoT based on the industry is recommended, that is, the security measures within the industry: solved internally by the industry. Self-contained protocols achieve confidentiality according to industry requirements.Its specifications refer to industry internal regulations.The overall consideration of IoT is the open security of industrial interfaces, open to designated users, and set different security levels.In addition, through information collaboration of the IoT, enhancing the relevance of information can reduce security risks [1].  
The existing Internet has already carried out a series of considerations on security issues. For the IoT system, special consideration needs to be given to the security of the front-end perception system. This is due to the fact that, from the data flow, if the integrity and authenticity of the data source cannot be guaranteed, then the credibility of the back-end big data and cloud computing is ignorant. Thereare two main aspects of the security of the front-end perceptual system, the security of the node itself and the communication security of the perception layer, which involve the wireless sensor network (WSN) [2]. In this paper, we will focus on the security of the perception layer communication.  
In the perception layer, security threats come from security attacks. Security attacks are classified into external attacks and internal attacks. The technical methods to defend against external attacks mainly include encryption/decryption, authentication, digital signature, and so on. Unfortunately, these methods cannot effectively defend against internal attacks. Conversely, some studies have shown that trust management technology can better defend against internal attacks [3]. Hereby, we will research and propose a trust management scheme based on Dirichlet distribution, and use the on-off attack as an example to verify its effectiveness simultaneously.  
The rest of this paper is organized as follows. In Section 2, the trust management schemes/systems based on the Beta distribution, Gaussian distribution, and Dirichlet distribution are reviewed and discussed. TheDirichlet-Distribution-based Trust Management Scheme (DDTMS) in IoT is proposed in Section 3. Furthermore, the proposed scheme is simulated and analyzed in Section 4. Finally, the conclusions are given in Section 5.
## 2 Affecting Factors
### Seasonal Changes  
#### 2.2.1. Foliar Level  
PRI is intended as a measure of the responses of a constitutive component dependent on the pigment content of leaves and on a facultative component varying on a short timescale because of the xanthophyllic cycle [67]. [PERSON] et al. [86] reported foliar pigment content had a stronger impact on PRI than on the relationship with RUE at seasonal timescales and proposed a procedure for correcting PRI that clarified this pigment effect at the foliar level. PRI values during growth were lower for sunlit than shaded leaves [83; 112] and were higher for dark-green than either light- or yellow-green leaves [83; 87]. PSII efficiency could not be efficiently tracked by PRI in varying light intensities, seasons and leaf colors due to the influence of chlorophyll, low temperature at night or low illumination on reflectance, which inhibit the epoxidation of the xanthophyllic cycle and retain more xanthophyllic pigments [87; 113]. Likewise, the tracking of seasonal levels of Chlorophyll a/b (Chl a/b) using PRI was affected by high non-photochemical dissipation from the senescence of the vegetation and drought stress in desert species during growth [114]. PRI decreased throughout the soybean growing season due to elevated ozone concentrations, which increased the protective dissipation of excess energy and decreased the foliar contents of nitrogen and chlorophyll [115; 116]. PRI, however, correlated well with NPQ in evergreens during most of the year except during spring recovery after winter down-regulation [72] and throughout the stages of crop leaves unless PRI was corrected for pigment effects [88]. Similarly, EPS could not account for the variation in PRI during spring recovery [54; 55]due to the changes in the constitutive pigment pool, but not the facultative xanthophyllic cycle, which led to the primary variation of PRI during spring recovery and over the year in evergreen conifers. [PERSON] et al. [117] found that changes in chlorophyllic and carotenoid concentrations caused the variation of PRI values due to the seasonal fluctuation of foliar optical signals. Similar findings were also reported for _Salix viiminalis_ trees [112], eggplant [88] and oak and beech trees [86], where the sizes of the pigment pools contributed to most of the PRI variability and thus limited its use for RUE estimation on long timescales. Changes in pigment concentrations due to water stress, however, could also potentially affect the seasonal variability of PRI [118; 119; 120], but these changes varied between species such as _Umbilicaria arctica_ and _U. hyperborean_[118]. PRI clearly differentiated between normal and stressed holm oaks but was not very informative under a severe drought [121], and could generally detect the physiological status of water-stressed plants but was not useful for the drought-tolerant species _Elaeganus umbellate_[122].
## 3 Results and Discussion
### Effects of Extracts towards Caco-2 and HT29-MTX Cells  
The MTT assay is a colorimetrical assay traditionally employed to determine the cell viability through reduction of the tetrazolium salt to a formazan salt [49]. Figure 2 presents the cell viability results of Caco-2 and HT29-MTX after exposure to the _S. ramossissima_ extracts.  
The HT29-MTX viability did not decrease after exposure to the different extracts' concentrations. At the highest concentration tested (1000 \(\upmu\)g/mL), the viability was 97.04% and 94.32%, respectively, for the CE for MAE extracts. On the other hand, none of the CE concentrations led to a decrease of Caco-2 viability at concentrations up to 1000 \(\upmu\)g/mL. However, after exposure to the highest concentration of MAE (1000 \(\upmu\)g/mL), the viability of Caco-2 decreased to 86.55%. Nevertheless, no significant differences (\(p>0.05\)) were observed between the different concentrations of each extract in both cell lines. Considering these results, it is possible to affirm that the IC50 for Caco-2 and HT29-MTX is higher than 1000 \(\upmu\)g/mL.  
To the best of our knowledge, this is the first study that evaluates the effects of _S. ramossissima_ extract on the viability of intestinal cell lines. [PERSON] et al. screened the cytotoxic effects of _S. herbacea_ seeds (prepared firstly with 70% methanol on a hot plate at 60 \({}^{\circ}\)C for 2 h and then with series of solvent fractions (hexane, ethyl ether, ethyl acetate, and water)) in HT29 colon cancer cells [7]. According to the authors, the extract prepared with water did not show inhibitory effects on the cells viability; however, for the different solvents fractions, a high cytotoxicity was reported (IC50 = 50.4 \(\upmu\)g/mL for ethyl ether fraction and around 90 \(\upmu\)g/mL for hexane and ethyl acetate) [7]. In the same line, [PERSON] et al.  
evaluated the cytotoxic effects of _S. fruticosa_ on Caco-2 and HT29 cell lines [48]. The authors prepared four extracts (hexane, dichloromethane, methanol and water), being the dichloromethane the one that most affects the cells viability (IC\({}_{50}\) = 140 and 12 ug/mL, respectively, for Caco-2 and HT29 cell lines) [48]. The other extracts of _S. fruticosa_ did not exhibit cytotoxicity [48]. The results reported in the present work ensure that _S. ramossissima_ is a safe ingredient for human diet.
# Broadcast Ephemeris with Centimetric Accuracy: Test Results for GPS, Galileo, Beidou and Glonass
## 8 Conclusions
\begin{table}
\begin{tabular}{l l c c c c c c c c c c c c c c c} \hline
**Orbit** & & & & & & & & & & & & & & & & & & & \\
**Type** & & & & & & & & & & & & & & & & & & & & \\ \hline KSO & **C06** & \(-\)0.010 & 0.591 & \(-\)0.300 & 0.338 & 0.362 & 0.209 & 2.220 & 1.760 & 3.522 & 1.523 & \(-\)5.739 & 5.415 & \(-\)0.027 & 0.000 \\ IGSO & **C07** & \(-\)0.636 & 0.573 & \(-\)1.247 & 0.454 & 0.115 & 0.335 & 2.232 & 3.277 & 2.154 & 3.446 & 0.176 & 8.256 & \(-\)0.002 & 0.002 \\ IGSO & **C08** & \(-\)0.998 & 0.927 & \(-\)1.900 & 1.667 & 0.078 & 0.478 & \(-\)2.057 & 3.992 & 4.229 & 3.180 & \(-\)5.988 & 5.747 & \(-\)0.003 & 0.001 \\ IGSO & **C09** & 0.359 & 0.730 & \(-\)0.066 & 0.273 & 0.135 & 0.289 & 1.962 & 1.276 & 0.476 & 2.170 & \(-\)5.849 & 5.322 & \(-\)0.030 & 0.000 \\ IGSO & **C10** & \(-\)1.417 & 0.821 & \(-\)0.835 & 0.213 & 0.152 & 0.309 & 2.311 & 1.303 & \(-\)2.750 & 3.054 & 6.918 & 5.200 & \(-\)0.019 & 0.001 \\ IGEO & **C11** & \(-\)0.126 & 0.563 & 0.124 & 0.503 & \(-\)0.462 & 0.393 & 1.675 & 4.108 & \(-\)1.418 & 8.841 & \(-\)1.229 & 9.970 & \(-\)0.028 & 0.002 \\ MEO & **C12** & \(-\)0.145 & 0.650 & 0.226 & 0.507 & -0.467 & 0.465 & 3.745 & 4.368 & \(-\)1.500 & 8.715 & \(-\)0.165 & 9.271 & \(-\)0.020 & 0.002 \\ IGSO & **C13** & \(-\)1.244 & 0.525 & \(-\)0.751 & 0.257 & 0.114 & 0.203 & \(-\)0.420 & 1.298 & 2.481 & 1.600 & \(-\)1.932 & 3.658 & \(-\)0.006 & 0.000 \\ MEO & **C14** & \(-\)0.005 & 0.499 & 0.266 & 0.560 & \(-\)0.709 & 0.487 & 2.284 & 4.577 & 0.454 & 6.516 & 3.984 & \(10.634\) & \(-\)0.028 & 0.005 \\ IGSO & **C16** & 0.122 & 0.598 & 0.049 & 0.346 & 0.254 & 0.180 & 2.229 & 1.698 & 3.068 & 1.443 & \(-\)5.469 & 5.389 & \(-\)0.021 & 0.000 \\ Average & \(-\)0.417 & & \(-\)0.442 & \(-\)0.037 & & 1.621 & & 1.073 & & & \(-\)1.528 & & \(-\)0.021 & \\ std & 0.013 & 0.725 & 0.336 & 1.645 & & 2.388 & & 4.453 & & & & & \\ \hline \end{tabular}
\end{table} 
Table 4: Helmert parameters relating the Boideu force frame to the TRFP2014 reference frame of the precise ephemeris of CODE. Average values for January 2020.
# Hybrid Dual-Link Data Transmission Based on Internet of Vessels
## 3 Algorithm Design
### HDTA Algorithm (Hybrid Dual-Link Transmission Algorithm Based on Internet of Vessels)
If the timer T expires and there are still no responses, the ship will send the data to the nearest underwater node, which will redistribute the task. The underwater node will issue the task, and if no ship accepts the task within a specified time D, it will send the data to the highest-priority underwater neighbor (i.e., the closest underwater node to the target node). This neighboring node will then initiate another task allocation process, continuing until a ship accepts the task. The data transmission path is established step by step, creating a complete transmission link from the source node to the destination node.
This work presents a hSDN-based reliable control plane that reacts on control communication impairments, and even, controller failure. Our scheme differs from state-of-the-art solutions in the way the control plane failure is handled. We considered a metric named Control Packet Loss Ratio (CPLR). As control packet loss is our major concern, from the point of view of a network node, the failure of the SDN controller is equivalent to the failure of the control communication channel. Thus, we propose to look at control plane reliability from the point of view of the node that experiences the impairments. We introduce a local agent in the nodes to detect unreliability of the control plane communication channel and a control logic switching algorithm to make a decision for whether to operate in a centralized or distributed way. Therefore, our scheme provides a solution not only for the control communication channel failure but also for the SDN controller failure as well. In this sense, this article presents a scheme that explores the benefits of centralized and distributed operations depending on control communication channel conditions. It combines both modes of operation into the same node; hence, the hSDN approach is generalized in the network and not just used in selected nodes. Some preliminary evaluations were presented in ref. [14], which indicated the impact that an unreliable control plane may have over the data plane. In this sense, it analyzed the dimension of the problem. Based on these initial findings, this paper further refines the preliminary thoughts mentioned in that paper toward more elaborate and automated decision making by presenting a complete solution and exhaustively evaluating it for various network sizes and diverse network impairments. We also emulate different network topologies under various traffic conditions. This article compares centralized and distributed operations under unreliable conditions to find the correct network operation switching point between centralized and distributed operations. In this direction, we deploy a hSDN scheme that changes the mode of operation at the node level when needed, based on the conceived control logic switching algorithm. The results show that, for the evaluated scenarios, the proposed hSDN approach maintained the network operational by achieving the best performance of both modes under all conditions. In the case of high loss regimes, the proposed hSDN scheme improved the throughput and maintained acceptable latency compared to the purely distributed approach. The rest of sections of the paper are organized as follows. Section 2 presents the current state of the art. Section 3 describes the proposed DenseNet-hybrid control plane architecture, including proposed changes in data plane node architecture with an integrated local agent. The control logic switching algorithm is also described in this section. The proposed approach is evaluated in Section 4. Section 5 presents a comparison of the proposed approach to the existing approaches, and Section 6 discusses the performance metrics. Finally, our conclusions are presented in Section 7.
# Performance Evaluation of Mobile RPL-Based IoT Networks under Hello Flood Attack
###### Abstract  
The RPL protocol is essential for efficient communication within the Internet of Things (IoT) ecosystem, yet it remains vulnerable to various attacks, particularly in dense and mobile environments where it shows certain limitations and susceptibilities. This paper presents a comprehensive simulation-based analysis of the RPL protocol's vulnerability to the Hello Flood attack in mobile environments. Using four different group mobility models--the Column Mobility Model (CMM), Reference Point Group Mobility Model (RPGM), Nomadic Community Mobility Model (NCM), and Pursue Mobility Model (PMM)--within the Cooja simulator, this study uniquely investigates the Hello Flood attack in mobile settings, an area previously overlooked. Our systematic evaluation focuses on critical performance metrics, including the Packet Delivery Ratio (PDR), End-to-End Delay (E2 ED), throughput, Expected Transmission Count (ETX), and Average Power Consumption (APC). The findings reveal several key insights: PDR decreases significantly, indicating increased packet loss or delivery failures; ETX values rise, necessitating more packet retransmissions and routing hops; E2 ED increases, introducing delays in routing decisions and data transmission times; throughput declines as the attack disrupts data flow; and APC escalates due to higher energy usage on packet transmissions, especially over extended paths. These results underscore the urgent need for robust security measures to protect RPL-based IoT networks in mobile environments. Furthermore, our work emphasizes the exacerbated impact of the attack in mobile scenarios, highlighting the evolving security requirements of IoT networks.  
RPL; Hello Flood attack; mobility; group mobility models Article