esa-sceva/satcom-chunk-collection huggingface dataset:
# Mechanical Performance Characterization of Lignin-Modified Asphal Mixture
## 4 Results and Discussion
### Thermal Cracking Resistance  
The semicircular bending (SCB) test was proposed as a fracture test to evaluate the low-temperature cracking resistance of asphalt mixtures. A typical load-displacement curve and the related parameters involved in the test are presented in Figure 7. Recently, the University of Illinois proposed the flexibility index (FI) to determine the cracking resistance of asphalt mixture [14]. It is a parameter that combines the fracture energy and post-peak failure behavior of asphalt mixture. The parameters used to derive the FI are illustrated in Figure 7. The FI is defined as the ratio of the fracture energy and the absolute value of the post-peak slope with the help of certain calibration factors. The detailed definition and calculation of each parameter can be found in reference [14]. It was reported that fracture energy alone was insufficient to discriminate among mixture design factors while FI is a valuable parameter in ranking the cracking performance of asphalt mixture [15]. Therefore, in this study, FI was used to compare the thermal cracking resistance of asphalt mixture with/without lignin.  
Figure 5: Putting depth evolution with time in wheel loading tracking test.  
Figure 6: Dynamic stability of different asphalt mixtures.  
Figure 8 shows the load-displacement curves of different asphalt mixtures from SCB tests. It can be seen that the load peak of SMA-LP occurred at a larger displacement, indicating a better flexibility. Brittle failure of the tested samples was not observed. To independently determine the fracture potential of a mixture, Figure 9 shows the FI values of different mixtures. It can be seen that asphalt mixtures with lignin show improved FI than the control mix. SMA-LP had the highest FI of 4.6 while SMA-C had the lowest FI of 2.3. Therefore, both lignin products are beneficial to improve the cracking resistance of asphalt mixture at low temperatures. The effect of lignin powder-modified asphalt on the improvement was more prominent. Lignin-modified asphalt binder improved the adhesive properties between aggregates and mortar, which is especially helpful to resist thermal cracking. Lignin fiber also had a reinforcing effect on the microstructure of asphalt mixture, which delays the cracking.  
Figure 8: SCB test results of different asphalt mixtures.  
Figure 7: A typical load-displacement curve of the Semicircular bending test (SCB) test and illustrated parameters.
# Drone-Based Vibration Monitoring and Assessment of Structures
## 4 Experimental Validation and Results
### Drone-Based Deployment Validation  
In this experiment, we aimed to validate the use of the drone for the sensor package deployment. The experiment was designed as follows: The drone approaches the structure and conducts the docking sequence described in Section 3.2, then mounts the sensor package to the structure. After sufficient time for data collection has passed, the UAS approaches the structure again and reverses the docking sequence to retrieve the package. When the package has been retrieved, the entire system lands, and the test is complete. During this experiment, the electromagnetic shaker was set to excite the structure at 11.3 Hz while we collected acceleration data from the sensor package and displacement data from the OptiTrack Motion Capture system. Due to the underside of the structure being outside of the OptiTrack field of view, only the UAS position and orientation were tracked using the OptiTrack system.  
The results are presented in Figure 8. The first 25 s of the test were used to coordinate the data collection and ensure that the electropermanent magnets were properly set before takeoff. Adjustments were performed to properly align the UAS with the structure before making contact at 38 s, at which point, the UAS maintained contact until the 48 s mark (area II) to ensure the engagement of the electropermanent magnet on the sensor package and the disengaging of the electropermanent magnet on the UAS. Once the magnets were set, the UAS left the sensor package on the structure from the 38 s mark until the 75 s mark (area III). During this time, the UAS hovered near the structure to allow the sensor package to perform data collection without interference from the hanging mass of the UAS.  
After allowing sufficient time for data collection, the UAS began the retrieval process by making contact with the package at 75 s. Contact was again maintained for 10 s to confirm the change in the electropermanent magnet state (area IV). For retrieval, the magnet on the UAS was engaged to maintain hold of the sensor package during landing prior to the magnet on the sensor package disengaging, allowing the system to begin its descent. This descent was performed immediately after securing the sensor package, and the drone landed at 94 s, completing the flight test.  
The acceleration of the sensor package presented in Figure 8 substantiated these movements showing intense vibration when the package was onboard the UAS and significantly smaller acceleration while in contact with a structure, indicating successful deployment. Experimental results showed the feasibility of the method, which can now be further developed [26].  
Figure 7: Schematic of the steel plate structure used in the experiments featuring the sensor package placement and the relative location of the supporting bolts used to simulate damage.
## 4 Results and Analysis  
Figure 11 in the Appendix A is an indicative illustration of the results when applying the LCC tool to the energy lighting products assessed. Figure 12,ii in the Appendix A show the results of LCC implementation in the procurement of office equipment conducted by the Greek public sector using environmental criteria. In this category a comparison with the products that were replaced was not possible, therefore LCC tool is used at the after tendering stage, as referred to in Directive 2014/24/EU. Table 1 depicts the aggregated results of LCC application in GPP in Greece and a comparison between the investment costs and the total life cycle costs. It includes data from 15 Indoor/Outdoor Lighting projects, for which the tool has been applied, since the necessary data for the utilization of LCC tool could be collected only for these product categories. The results of Table 1 are graphically illustrated in Figure 2.  
As some of the products being compared have different lifespans, EU LCC tool allows their comparison over the same time horizon. In order to compare future costs and benefits to the current ones, they are often discounted to present value. The life cycle cost of a product is the total discounted costs during its lifetime. At a first glance, the above results illustrate that the cost of investing in green products is significantly higher than using conventional products. Correspondingly, one could argue that the total life cycle cost is in most cases disproportionately increased and only in a few cases is reduced, depending on various factors such as quantities of products offered and product specifications that contribute to the final price. Price comparisons are made between products with the same qualitative/technical characteristics to draw reliable conclusions. Starting deepening in the analysis, there is a significant differentiation in direct and indirect costs. As illustrated in Table 2, indirect costs, including the costs of environmental externalities, differ as percentages of the total LCC costs in green and non-green products. More specifically, in all but one cases, indirect costs represent higher percentages of the total LCC costs in non-green products. This fact is a first argument to deconstruct the dilemma that green products cost more, and the exclusive choice of the purchase price as a criterion in the evaluation process is not safe. Additionally, it is highly likely that a product sold at a low price will not have the required environmental certifications and will have poor operational performance and vice versa, i.e., a product sold at a higher price will carry the required environmental certifications and have better operational performance. Therefore, products' specifications regardless of the purchase cost largely determine whether the use of these products will lower costs.
## 2 Methods
### Transfer Entropy for Directed Phase-Amplitude Interactions  
The conventional approach to estimate directed phase-amplitude interactions through TE consists of two stages. The first stage is a component extraction stage, which involves complex-filtering, or performing a phase/amplitude decomposition, to extract instantaneous phase and amplitude time series (see Figure 1A). Then, there is a TE computation stage that simply consists of estimating the information flow between the previously extracted data [5; 18]. Formally, given two time series \(\mathbf{x}\) and \(\mathbf{y}\), to estimate the TE from the phase of \(\mathbf{x}\) at a frequency \(f_{l}\) (usually a low frequency) to the amplitude envelope of \(\mathbf{y}\) at a frequency \(f_{h}\) (commonly a higher frequency than \(f_{l}\)), we obtain complex time series \(\mathbf{s}_{x}(f_{l})=\xi^{x}e^{i\theta^{x}}\in\mathbb{C}^{T}\) and \(\mathbf{s}_{y}(f_{h})=\xi^{y}e^{i\theta^{y}}\in\mathbb{C}^{T}\), which contain the filtered values of \(\mathbf{x}\) and \(\mathbf{y}\) at \(f_{l}\) and \(f_{h}\), respectively; where \(\theta^{x},\theta^{y}\in[-\tau,\pi]_{t=1}^{T}\) are instantaneous phase time series, and \(\xi^{x},\xi^{y}\in\mathbb{R}^{T}\) are amplitude envelopes [21]. Then, we compute the desired TE as:  
\[TE^{\xi_{\text{S}}}(\mathbf{x}\rightarrow\mathbf{y},f_{l},f_{h})=H_{\text{S}} \Big{(}\xi_{t-1}^{y,dy},\mathbf{\theta}_{t-u}^{x,dx}\Big{)}-H_{\text{S}}\Big{(} \xi_{t}^{y},\xi_{t-1}^{y,dy},\mathbf{\theta}_{t-u}^{x,dx}\Big{)}+H_{\text{S}} \Big{(}\xi_{t}^{y},\xi_{t-1}^{y,dy}\Big{)}-H_{\text{S}}\Big{(}\xi_{t-1}^{y,dy }\Big{)}, \tag{3}\]  
where \(\mathbf{\theta}_{t}^{x,dx}\) and \(\xi_{t}^{y,dy}\) are time-embedded versions of \(\theta^{x}\) and \(\xi^{y}\).
## 3 Implementation
### The Workflow  
The workflow for the proposed method is shown in Figure 8. There are five main steps in the closed-loop execution:  
1. DVS captures the events related to a moving object;
2. The microcontroller receives the events in AER format, converts them to SpiNNaker packets and sends to SpiNNaker;
3. SpiNNaker receives packets and injects spikes into the SNN, and after processing by SNN sends the outputs back to the microcontroller;
4. The microcontroller receives the IDs of output spikes and generates the servo command by using the algorithm (Figure 4);
5. The position of the robotic arm is adjusted by changing the pulse width of control signal received by the digital servomotor.  
Figure 7: **(a)** Model: the neural network identifies in which of the possible \(N\) ‘lanes’ the ball is approaching the goal (\(N=8\) in the demonstration). This model covers only a subset of all possible trajectories of the ball towards the goal, but it is sufficiently good approximation for the platform hardware demonstration purposes. The field of view of the DVS camera is approximately 35 cm \(\times\) 50 cm (not a square because of perspective). **(b)** The spiking neuronal network (SNN) topology.
# Quantitative Assessment of Upper- Limb Volume: Implications for Lymphedema Rehabilitation?
## 7 Future Directions and Challenges
### Addressing Challenges, Costs, and Limitations  
Challenges associated with volumetric assessment methods should be addressed to maximize their clinical utility. For example, the cost and availability of certain techniques,such as MRI and CT, may limit their widespread use [65; 66; 72]. By incorporating estimated acquisition costs, clinicians can make informed decisions tailored to their patient demographics and available resources, enhancing practicality [96]. These cost estimates provide a practical dimension to the assessment. For example, by factoring in costs for techniques such as three-dimensional laser scanning, clinicians can assess whether benefits outweigh higher acquisition costs compared to more budget-friendly options [9]. This empowers practitioners to make choices balancing clinical relevance and budget constraints. Efforts to optimize and refine less expensive methods, such as BIS or 3D scanning techniques, should be pursued to improve their accuracy and reliability while reducing costs [38; 49].  
Furthermore, addressing the limitations of volumetric assessment methods in specific patient populations is important. Lymphedema can occur in various clinical contexts, such as post-surgical, post-radiation, or congenital lymphedema. Understanding the performance of volumetric assessment methods in these specific populations, considering factors such as changes in tissue composition or fibrotic alterations, will enhance their clinical applicability [1]. In summary, the future directions of volumetric assessment for upper-limb lymphedema involve the integration of emerging technologies.  
Despite these considerations, this narrative review is not free of limitations. Firstly, a meta-analysis was not performed due to the narrative review design and the heterogeneity of the volumetric assessment methods. On the other hand, according to the Cochrane Handbook of Systematic Review [97], a meta-analysis might not be possible in cases of heterogeneity of study participants, interventions, and outcomes. Therefore, a quality assessment was not performed in accordance with the SANRA quality criteria [16]. However, it should be noted that the decision to adopt a narrative synthesis approach aimed to best address the complexity of the reviewed topic and provide a meaningful synthesis of the available evidence.
## 6 Hardware Experimental Setup
### Dynamic Scenario  
The details of the static antenna scenario are as follows:  
* Time of simulation: 16 January 2019 at 12:00:00 p.m.
* Location: Downtown Toronto, ON, Canada with a starting point at 43 deg39'6.63'' N latitude and 79 deg22'54.17'' W longitude.
* The number of visible GPS satellites: 11, as shown in Figure 6.
* Duration: 4 min.
* Platform: Land vehicle.
# Analysis of a Collision-Energy-Based Method for the Prediction of Ice Loading on Ships
###### Abstract  
Ships designed for operation in Polar waters must be approved in accordance with the International Code for Ships Operating in Polar Waters (Polar Code), adopted by the International Maritime Organization (IMO). To account for ice loading on ships, the Polar Code includes references to the International Association of Classification Societies' (IACS) Polar Class (PC) standards. For the determination of design ice loads, the PC standards rely upon a method applying the principle of the conservation of momentum and energy in collisions. The method, which is known as the Popov Method, is fundamentally analytical, but because the ship-ice interaction process is complex and not fully understood, its practical applications, including the PC standards, rely upon multiple assumptions. In this study, to help naval architects make better-informed decisions in the design of Arctic ships, and to support progress towards goal-based design, we analyse the effect of the assumptions behind the Popov Method by comparing ice load predictions, calculated by the Method with corresponding full-scale ice load measurements. Our findings indicate that assumptions concerning the modelling of the ship-ice collision scenario, the ship-ice contact geometry and the ice conditions, among others, significantly affect how well the ice load prediction agrees with the measurements.  
Polar Code; Polar Class; goal-based ship design; arctic ships; energy method; ice load; ice strength +
Footnote †: journal: Applied Mechanics and Physics
## 5 Study Results and Discussion
### Shock-Absorber System  
Figure 15 depicts the components of the shock-absorber system, namely the damper device and its endings (highlighted in pink), as well as the neoprene springs and their endings (highlighted in yellow). The rubber bumpers and the UPN100 profiles are also highlighted in red. The system behaved as expected, with the damper device being crucial in the initial moments, when velocity in the chair and in the human body in relation to the shelter is higher (Figure 15b). After those initial moments, when the velocity decreases, the elastomeric springs and the rubber bumpers on the UPN 100 profiles control the chair's response.  
Figure 14: Internal efforts in the human chest (situation 2).  
For the damper (Figure 16), its 600 mm maximum stroke proved to be enough, as in the worst situation (situation 1), it deforms about 400 mm downwards (Figure 15a) and less than 150 mm upwards. With a maximum value of 27 kN, the axial force installed in it was well below its 50 kN limit (Figure 16b). The axial and shear forces inflicted by the chair on the shelter columns were also computed (Figure 16c,d).  
Figure 16: Damper results (situation 1 to situation 3).  
Figure 15: Details of the shock-absorber system (frontal and lateral views).  
The elastomeric springs present high distortions (Figure 17), being higher for the upper spring, with a maximum of 600% in the three (3) fall situations. The intermediate and lower springs suffer lower distortions, with maximum values of about 400%.  
The values of the distortional components transverse to the shelter columns differ among each situation, with negative values corresponding to movements outside the shelter limits. For the lower and intermediate springs, this movement out of the shelter depends on the falling situation, being less relevant for the tilted fall on the raised chair (situation 2) and more significant for the vertical fall (situation 1, Figure 10). In the latter, the chair columns theoretically come out of the shelter's limits for about 110 mm (210-100 mm, where 100 mm corresponds to the gap between those columns and the limits external shelter boundary).  
Finally, for the situation where the springs and the damper impose larger bending stresses to the shelter columns (situation 2, Figure 18), this pair of columns will have to be able to withstand an additional bending moment of 51 kN-m, which represents 42% of the pure flexural strength of the corresponding profile (HEB120 in S355 steel grade).  
Figure 17: Displacements of the top (superior) and bottom (inferior) neoprene springs (fall situation 1 to situation 3).
# Plum Tree Algorithm and Weighted Aggregated Ensembles for Energy Efficiency Estimation
## 2 Background
### Contributions with Respect to Previous Research  
Compared to the method presented in [80], where ensembles of ANN were used for the prediction of smart grid stability, the ensemble proposed in this manuscript consists of four regressors. The same objective function for the evaluation of the performance of the ensembles was considered, but the results were compared to more nature-inspired algorithm-based ensembles, using 30 runs for each fold.  
The authors of [81] approached the problem of energy efficiency estimation using the same experimental dataset as the one used in this manuscript. However, they focused on heating predictions only rather than on both cooling and heating predictions. Even though they used nature-inspired algorithms such as the Firefly Algorithm (FA) [82], the Shuffled Complex Evolution (SCE) algorithm [83], the Optics-Inspired Optimization (OIO) algorithm [84], and the Teaching-Learning-Based Optimization (TLBO) algorithm [85], those algorithms were used to tune the hyper-parameters of an ANN rather than to tune ensembles of regressors such as in the approach presented in this article. The conclusions of the authors showed that the prediction results were performant and better than the results obtained by using state-of-the-art algorithms.  
The approach presented in [86] applied the Shuffled Frog Leaping Algorithm (SFLA) [87] in the tuning of the hyper-parameters of a Regression Tree Ensemble (RTE), which was an approach proposed for the accurate prediction of heating loads and cooling loads. Compared to other methods presented in the article, the SFLA-based approach showed the best results in terms of evaluation metrics.  
In [88], the PSO algorithm was applied to improve the performance of the Extreme Gradient Boosting Machine (XGBoost) algorithm [89] used in the prediction of heating loads. The PSO-based approach returned better results compared to classical state-of-the-art approaches. However, the authors of the study focused only on the prediction of the heating load like the approach in [81]. Similar to the approach presented in this article, 80% of the dataset was considered in the training phase, and 20% was considered in the testing phase. On the other hand, that approach also considered a re-sampling method based on 10-fold cross-validation to reduce errors.
# SDN Anomalous Traffic Detection Based on Temporal Convolutional Network
## 2 Related Work
### Traditional Anomalous Traffic Detection Methods  
Traditional abnormal traffic detection methods mainly rely on predefined rules and statistical analysis techniques. The rule-based method identifies abnormal traffic by setting a series of fixed rules, such as determining whether the traffic is abnormal based on specific port numbers, protocol types, packet sizes and other characteristics. The method is simple to implement and fast to detect, but its drawbacks are also very obvious, i.e., the formulation of rules needs to rely on expert knowledge, and it is difficult to cover all possible anomalies, and it is weak in detecting new types of attacks and unknown threats.  
Methods based on statistical analysis have been applied in the field of abnormal traffic detection since the 1990s [12]. Statistical analysis methods, on the other hand, model normal traffic by analyzing the statistical characteristics of network traffic, such as the arrival rate of packets, the average packet size, and the flow duration, etc., and when the detected traffic differs significantly from the normal model, it is considered to be an abnormal flow. However, statistical analysis methods are susceptible to noise and normal traffic fluctuations in the face of complex network environments and variable traffic patterns, leading to a decrease in detection accuracy and an increase in false alarms. [PERSON] et al. [13] proposed a DDoS attack defense scheme, SDCC, which integrates bandwidth detection and data flow detection techniques, and employs a confidence-based filtering (CBF) method  
Figure 1: The base structure of SDN.  
to calculate the CBF score of the packet. If the CBF score of a packet is below a specific threshold, the packet is determined to be an attack packet. The algorithm is simple to compute, but it needs to be constantly updated and optimized in the emerging attacks.
# An Introduction of a Modular Framework for Securing 5G Networks and Beyond
## 3 Proposed Network Service Security (NSS) Framework
### Security Modelling
#### 3.2.1 Physical Layer Security  
This level of security is concerned with the PLS such as the spectrum, resource allocation, interference and signal. Even though PLS is out of scope, this article gives a brief overview of PLS solutions applied by relying on wireless channel characteristics such as interference, signal and fading--whilst the quality of the attacker's signal can be degraded through keyless secure communication by using signal engineering and processing techniques. Different studies explored PLS in 5G, and a detailed review of different PLS techniques are presented [36, 37, 38]. For example, artificial noise injection is used to increase channel quality and an anti-eavesdropping signal approaches are used to align multiple users' signals at the eavesdroppers. Secure beamforming, on the other hand, improves the spatial distribution properties of the transmitted signal, resulting in a greater difference in channel quality between legitimate users and eavesdroppers.