esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusion  
This paper proposes a new energy aware multipath routing protocol based on AODV and AOMDV in WSNs, which we have named as WEAMR. WEAMR makes use of a hotline-assisted routing approach which we verify can play an important role to improve the reliability of source to sink communications. WEAMR makes use of multipath transmission to utilize the network energy efficiently and keeps energy-aware related packet dissemination to a minimum, which results in longer lifetime of the network and also works as a load sharing feature. We showed through our simulation comparisons that WEAMR performs better than the AODV and AOMDV protocols in terms of inter-cluster end to end delay, has less packet loss in inter-cluster communication and has less route discovery overhead in case of intra-cluster path failure scenarios. The proposed topology provides reliability which is independent of the number of nodes in a given cluster or area. In the proposed protocol we have improved intra-cluster reliability and the lifetime of the network. Such features are very important for high density WSN deployments and these features make our routing protocol an ideal fit for all mission-critical applications.  
Figure 19: Average packet loss in end to end communication with increasing number of nodes.
## 3 Results and Discussion
### Effects of EAFS
#### 3.2.2 Compressive Strength  
The following figures show the variation of compressive strength of SCC samples containing EAFS with time. Multiple samples were tested at different intervals ranging between 3 and 56 days, with the average compressive strength being reported.  
Figure 14 shows how the compressive strength of SCC samples containing various doses of EAFS varies with curing age. As shown in the figure, the introduction of EAFS at low doses (5% and 10%) into the benchmark SCC mix improves the compressive strength of the SCC at any age, and increased its 56-day compressive strength considerably from 92.1 MPa to 104.7 MPa and 98.2 MPa, respectively. The introduction of EAFS at higher doses (15% and 20%) showed almost identical compressive strength results to each other with minor variations; both doses initially had a slower strength gain rate compared to the benchmark SCC mix. The EAFS mixes had a maximum standard deviation of 2.70 MPa and a maximum variance coefficient of 2.75%.
## 4 Experimental Analysis
### Experimental Setup
#### 4.2.2 Experimental Results
The proposed RNN-based model converged faster as it was trained for 50 epochs, while the ANN-based model was trained for 200 epochs on the same data. Table 7 presents a comparison of the \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Author** & **Method** & **Accuracy** & **Sensitivity** & **Specificity** & **False Positive \%** \\ \hline [PERSON] et al. [22] & ANN+DWT using 5 statistical features & 98.67\% & 98.00\% & 99.00\% & 1.00\% \\ [PERSON] et al. [58] & SSTFT+GNMF-based LogE+FKNN & 98.99\% & 98.53\% & 99.27\% & 0.73\% \\ [PERSON] et al. [59] & LSTM & 99.46\% & 99.62\% & 99.31\% & 0.69\% \\ [PERSON] et al. [60] & 2D CNN STFT+LSTM & 98.20\% & 98.20\% & 98.99\% & 1.02\% \\ [PERSON] et al. [14] & Gaussian mixture models & 98.43\% & 98.46\% & 97.48\% & 2.56\% \\ [PERSON] et al. [61] & fractional Fourier transform & 98.76\% & 98.34\% & 98.40\% & 1.6\% \\ [PERSON] et al. [62] & CNN+DWT & 97.36\% & 96.17\% & 96.36\% & 3.65\% \\ [PERSON] et al. [63] & MVAR Sample entropy STFT & 98.67\% & 98.80\% & 98.45\% & 1.5\% \\ [PERSON] et al. [25] & SVM+DWT & 99.30\% & 99.50\% & 99.0\% & 1.0\% \\ \hline This work & RNN+DWT & 99.84\% & 100\% & 99.80\% & 0.33\% \\ RNN+FFT & 99.77\% & 100\% & 99.71\% & 0.34\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of proposed model on the BONN dataset with other methods in the literature. Figure 10: RNN classification accuracy based on training/testing ratios and learning rate (CHB-MIT dataset). (**a**) RNN+FFT; (**b**) RNN+DWT. Figure 9: RNN classification accuracy based on training/testing ratios and learning rate (BONN dataset). (**a**) RNN+FFT; (**b**) RNN+DWT. proposed RNN-based model with recent studies in the literature, demonstrating superior performance in terms of accuracy, sensitivity, and specificity. For instance, [PERSON] et al. [22] achieved an accuracy of 89.01% with five statistical features as compared with the proposed RNN-based model's 93.27%.
## 3 Results
### Optimizing the Processing Conditions  
In the Taguchi method, it is desirable to minimize the variability in the product's performance in response to noise factors (uncontrollable factors) while maximizing the variability in response to signal factors (control factors). The signal/noise (S/N) ratio is the quantity denoting statistically how the response changes with respect to the target performance (property) under diverse noise conditions [14]. A higher value of the S/N ratio determines the control variable settings that diminish the influences of the noise variables. Maximising the S/N ratio leads to minimised properties that are sensitive to noise. Since maximisation of tensile strength is the object of this work, the expression depicting the greater-the-better feature was employed for the analysis:  
\[\frac{S}{N}=-10\log_{10}[(\frac{1}{n})\sum_{i=1}^{n}{(\frac{1}{y_{i}^{2}})}] \tag{1}\]  
where \(y_{i}\) is the measured tensile strength for each test part, and \(n\) represents the number of specimens for each test trial. The factor levels with the largest S/N ratios will minimise the noises sensitivity and lead to the optimised levels.  
The variation of part strength due to various factors was assessed following the Taguchi technique [15]. The S/N ratios for the melt extrusion FDM printed PLA parts were calculated. Figure 10A shows the results. Based on Figure 10A, the optimised factor levels that lead to parts with the highest tensile strengths were estimated to be A2/B3/C1/D3/E3. These optimal factor levels represent a 0\({}^{\circ}\) print orientation, a fill density of 60%, an oven temperature of 25 \({}^{\circ}\)C, a nozzle temperature of 230 \({}^{\circ}\)C, and a print speed of 60 mm/s.  
Calculated S/N ratios for the solution extrusion 3D-printed PLA parts are displayed in Figure 10B. Based on the figure, the optimised factor levels that result in the maximum part strengths were estimated to be A2/B2/C2/D3/E1. These optimal factor levels indicate a print speed of 35 mm/s, a fill density of 65%, a PLA/DCM ratio of 5 g/5.8 mL, an oven temperature of 22 \({}^{\circ}\)C, and a 45\({}^{\circ}\) print orientation.  
Figure 9: Stress-strain curves of melt- and solution-printed parts.
# A Survey on Penetration Path Planning in Automated Penetration Testing
## 4 Planning Models in Penetration Path Planning
### Summary  
The graphical security model, exemplified by attack graphs, is adept at mapping out attack paths in small-scale networks, offering a transparent visualization of all potential vectors. However, as network complexity and size grow, the number of potential attack paths can escalate exponentially, complicating the construction and analysis of such models. Additionally, these graph-theoretic models often concentrate on vulnerability models, which may neglect the diverse tactics that adversaries might use in real-world scenarios. This limitation underscores the need for more sophisticated approaches in the context of large-scale networks.  
To address this challenge, researchers have used planning domain models with PDDL or similar languages to formalize the problem, simplifying the environment to reduce planning complexity. While this approach provides a solid foundation for penetration path planning, it may not fully capture the dynamic and unpredictable nature of real-world cyber threats. Future research should refine these languages, explore new approaches to better meet the demands of dynamic environments, or adopt more flexible and adaptive models to create more robust and versatile planning systems.  
To bridge this gap, MDPs have been adopted to model the sequential decision-making inherent in network PT. MDPs aim to identify optimal policies that maximize long-term rewards, guiding the selection of actions in each state. In environments where information is incomplete, POMDPs offer a more fitting model by enabling state inference from observations. Yet, while POMDPs provide a closer representation of real-world conditions, they also introduce computational challenges that can limit their scalability. Consequently, MDPs remain a practical alternative for their computational efficiency, particularly for large-scale testing scenarios.
## 2 Model Statement  
### FOWT Description  
In this research work, four OWCs are placed inside the platform's corners as represented in Figure 1. The square-shaped platform is connected to the FOWT's tower and ballasted with seawater to obtain a proper draft, which is not so shallow that it is vulnerable to continuous wave slamming. The platform is anchored through eight catenary cables to avoid it from drifting. Two of the lines from each corner of the barge's bottom have a 45-degree angle from each other.  
Mooring systems, made of eight catenary lines anchored to the floating support platform at fairlead connections, are applied to hold the floater against wind, waves and current. The opposite ends of the lines are moored to the seafloor. Such cables may be made of chain, steel, and/or synthetic fibers and are often a segmented combination of these materials [23]. Depending on environmental conditions, the tension on each cable changes. As the fairleads move with the platform in response to unsteady environmental loading, the restraining forces at the fairleads change with the changing cable tension. This means that the mooring system has an effective compliance for the stabilization of the system. In this study, wind and servo forces for yaw, pitch and generator control of the wind turbine have not been considered. Therefore, eight Degrees Of Freedom (DOF) are assessed namely surge, sway, heave, roll, pitch, yaw, fore-aft and side-to-side motions. The dynamic responses of the FOWT have been included in FAST in order to implement the nonlinear equations of motion. In Table 1, The detailed features of the wind turbine are presented.  
Moreover, the features of the large platform and the platform with OWCs are represented in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Hub height & 90 m \\ Center of mass location & 38.23 m \\ Rotor diameter & 126 m \\ Number of blades & 3 \\ Initial rotational speed & 12.1 rpm \\ Blades mass & 53.22 kg \\ Nacelle mass & 240,000 kg \\ Hub mass & 56,780 kg \\ Tower mass & 347,460 kg \\ Power output & 5 MW \\ Cut-in, Rated, Cut-out wind speed & 3 m/s, 11.4 m/s, 25 m/s \\ \hline \hline \end{tabular}
\end{table}
Table 1: 5-MW floating wind turbine Features [24].  
Figure 1: Barge-based floating offshore wind turbine with four OWCs.
# Research Advances in Large Deformation Analysis and Applications of the Material Point Method
## 3 Application in High-Speed Impact
[40] conducted comparative predictions of the impact response in nano-porous solids using both MPM and MD approaches, demonstrating the feasibility of employing the MPM to study high-speed impact behaviors at the nanoscale. The further development of the MPM in conjunction with other numerical methods into a multi-scale approach bridges the gap between the atomic scale and the continuum scale, achieving a reasonable balance between computational efficiency and accuracy [41]. The MPM can simulate macroscopic phenomena but requires an appropriate equation of state (EOS). Therefore, [PERSON] et al. [42] proposed a multi-scale framework combining MD and the MPM, using the EOS data obtained from MD solutions to provide parameters for the MPM. However, this method requires the calculation of thermodynamic properties under different temperature and volume conditions, significantly increasing the computational cost. [PERSON] et al. [43] satisfy the Rankine-Hugoniot conditions by inspecting the state of the system before and after the impact, enabling the direct simulation of compression shock phenomena and directly obtaining thermodynamic properties after applying specific impact velocities. In simulating the fragmentation phenomenon in high-speed impacts, grid-based methods may experience mesh distortion, which compromises solution accuracy. Although the MPM can evolve structural damage under high-speed collisions, it requires a large number of material points to ensure simulation accuracy, which, to some extent, limits the scale and efficiency of the computations [44]. Due to the extremely high propagation speed of shock waves, the time step must be reduced to ensure numerical stability, which further decreases computational efficiency. Moreover, the MPM may face challenges when multiple physical effects need to be considered, such as thermal effects and phase transitions.
## 9 Performance Evaluation
### Storage Evaluation  
There are two sets of information stored in a SN\({}_{\text{i}}\): (i) information related to the sensor itself such as random numbers: (ai, bi, ci), pseudonyms: (PIDi, BPIDi, FPIDi, APIDi), keys: (ki\(\leftrightarrow\)bs, bki, fbki); (ii) information related to neighbors which include, random numbers: (a\({}_{i\leftrightarrow j}\), b\({}_{j}\), c\({}_{j}\)), pseudonyms: (OHPIDi\({}_{i\leftrightarrow j}\), BPIDi, FPIDj), keys: (k\({}_{i\leftrightarrow j}\), Misc: (link\({}_{i\leftrightarrow j}\), \(\Delta_{\text{j}}\)).  
Figure 28: Simulation for busy network with different R\({}_{min}\) threshold values th = 10, 11 and 12. F stands for the number of fake messages has been reduced and D stand for the number of delayed messages to maintain R\({}_{\text{min}}\). The simulation shows that the number of delayed messages is minimal while it decreases as the threshold increases.  
If we presume that the keys, the random numbers, the pseudonyms and the hash functions are all \(n\) bits long in average, and the required bits for miscellaneous data altogether is _two_ bytes, and the average number of neighbors \(\mathrm{N_{ave}}\), then the total storage memory required is:  
\[\mathrm{Storage}=10n+(7n+16)\times\mathrm{N_{ave}} \tag{52}\]  
[PERSON] _et al._[16] indicated the storage for _SAS_, CAS, _APR_, _DCARPS and EAC_. We also calculated the storage for _PhID_, _ACS_, HIR and RHIR. All are listed in Table 4. The size of storage increases proportionally when the size of \(n\) increases. The most common hashing functions which are considered very secure are: _MD4_[50] which uses 128-bits digest, _SHA-1_[50] which uses 160-bits digest, and _Whirlpool_[50] which uses 512-bits digest [50].
# Energy-Efficient and QoS-Aware Computation Offloading in GEO/LEO Hybrid Satellite Networks
## 2 Materials and Methods
### Computation Offloading Method Based on Minimum Cost Maximum Flow
#### 2.2.3 Successive Shortest Path-Based Computation Method
```
0: the set of on-board missions, GEO/LEO satellite processing capacity, channel bandwidth, and channel gain;
0: offloading decision for on-board missions;
1: According to the input information, construct the flow network \(G\);
2: Construct the residual network \(G^{R}\) based on \(G\): \(f(a,b),f(b,a),w(a,b),w(b,a)\);
3:for\(e(a,b)\in G^{R}\)do
4:\(w(b,a)\leftarrow-w(a,b)\);
5:\(f(a,b)\gets 0\);
6:\(f(b,a)\gets 0\);
7:endfor
8:while TRUE do
9: Use shortest path fast algorithm in the residual network \(G^{R}\) to find the augmenting path \(p\) from the source node \(S\) to the sink node \(T\) with an increase of \(\delta\);
10: if there is no augmenting path \(p\) in \(G^{R}\)then
11: break;
12:endif
13: Augment the flow in \(G^{R}\) along the augmenting path \(p\);
14:for\(e(a,b)\in p\)do
15: Update \(f(a,b)\) based on \(\delta\);
16: Update \(f(b,a)\) based on \(\delta\);
17:endfor
18:endwhile
19:for\(e(a,b)\in G\)do
20:for\(e(a,b),a\in Mission\_Node,b=U\)do
21:if\(f(a,b)=0\)then
22: Obtain the offloading decision for this mission, which can be expressed as \(\lambda_{a}=\frac{f(a,c)}{B_{a}},c=GEO\_Node\);
23:else
24: Record this mission in the mission failure collection;
25:endif
26:endfor
27:endfor
```  
**Algorithm 1** Successive shortest path-based computation offloading algorithm
# AUTOSAR Runnable Periods Optimization for DAG-Based Complex Automobile Applications
## 2 Related Work
[23] developed a runnable placement and scheduling method considering the inter-runnable communication overhead in an electronic control unit (ECU). [PERSON] et al. [24] proposed an algorithm for sequencing and scheduling runnables for multicore ECUs. [PERSON] et al. [25] studied the runnable-to-core mapping problem using the integer linear programming (ILP) technique. [PERSON] et al. [26] developed a method for migrating a legacy AUTOSAR application to a multicore processor while minimizing energy consumption. _AUTOSAR runnable scheduling._ However, the above studies about AUTOSAR applications commonly assume that runnable periods are given a priori, which is not valid in the industry practice. With this motivation, runnable periods optimization problem was first formulated in our previous research paper by [PERSON] et al. [5], which proposed a combinatorial search method that is useful only for small systems due to its high computing demands. [PERSON] et al. [27] partly solved the scalability problem using an analytical method only for limited application structures. These two papers are precursors to this paper and will be thoroughly explained even in more depth and detail in Sections 4 and 5 for the self-completeness of this paper. This paper then further extends our previous works by presenting a more general solution that applies to arbitrarily-shaped complex DAG-based AUTOSAR applications.
# Low Temperature RF-Plasma Initiated Rapid and Highly Ordered Fracture on Ag Nanowires
## Materials and Methods  
AgNWs with diameters ranging from 60 to 300 nm (average 150 nm) and lengths ranging from 10 to 50 \(\upmu\)m (average 40 \(\upmu\)m) were synthesised by hydrothermal method [15]. Polyvinyl pyrrolone (PVP) (molecular mass 360 k) Aladdin (Aladdin reagent (Shanghai) Co., Ltd. Shanghai, China), sodium sulphide and silver nitrate Sinopharm (Sinopharm Group Co. Ltd. Shanghai, China) were mixed in ethylene glycol and put in Teflon-lined stainless autoclaves. AgNPsWs of various sizes (diameters and lengths) were obtained after 4-7 h under 160 \({}^{\circ}\)C. The sizes of AgNPsNWs can be controlled both by reaction time and by temperature. In our case, larger diameter and longer AgNPs can be obtained by prolonging reaction time within 12 h. To remove the adhered PVP layer on AgNPs surfaces, the suspension of AgNPs was treated by ethanol rinse and filtration with five circles. The prepared AgNPs were suspended in ethanol with 0.1 wt%, 0.5 wt% and 3 wt% and were dropped on Si substrates to form dispersed, slightly lapped, and seriously lapped AgNPs respectively. Following this, these samples were dried in a drying oven under 50 \({}^{\circ}\)C.  
Herein, a tuneable RF system (0-250 W) with frequency of 13.56 MHZ and Ar gas of 0.2 Pa has been used to generate plasma and avoid oxidation on Ag nanowires. The prepared samples have been treated with plasma for various times. In contrast, heat treatments on Ag nanowires under the same Ar gas pressure have also been carried out at a heating rate of 10 \({}^{\circ}\)C/min.  
The characterizations of sample morphologies were carried out with an optical microscope (OM, ZEISS, Axioplan 2 imaging MOT), a field emission scanning electron microscope (SEM, FEI-Nova Nano SEM 450) and a transmission electron microscope (TEM, JEOL-2100F).
# Prediction of Building's Thermal Performance Using LSTM and MLP Neural Networks
###### Abstract  
Accurate prediction of building indoor temperatures and thermal demand is of great help to control and optimize the energy performance of a building. However, building thermal inertia and lag lead to complex nonlinear systems is difficult to model. In this context, the application of artificial neural networks (ANNs) in buildings has grown considerably in recent years. The aim of this work is to study the thermal inertia of a building by developing an innovative methodology using multi-layered perceptron (MLP) and long short-term memory (LSTM) neural networks. This approach was applied to a public library building located in the north of Spain. A comparison between the prediction errors according to the number of time lags introduced in the models has been carried out. Moreover, the accuracy of the models was measured using the CV(RMSE) as advised by AHSRAE. The main novelty of this work lies in the analysis of the building inertia, through machine learning algorithms, observing the information provided by the input of time lags in the models. The results of the study prove that the best models are those that consider the thermal lag. Errors below 15% for thermal demand and below 2% for indoor temperatures were achieved with the proposed methodology.  
neural network; LSTM; MLP; thermal inertia; building performance Article
## 6 Simulation Results and Discussion
### Optimum Configuration Considering DSM Strategy and Resource Forecasting  
The time-ahead resource forecasts obtained using RF model are used to implement the FDR scheduling based on the anticipated mismatch between the generation and load demand. The forecasting results based on MAE, RMSE and \(r^{2}\) performance metrics for wind speed, solar irradiances, wind and solar photovoltaic power are as summarized in Table 4.  
Figure 4: Aggregated renewable power generation, battery energy storage system (BESS) power and the load demand profiles without the consideration of the DSM strategy (case 1).  
Figure 5: Charging and discharging power and the dynamics of the BESS (Case 1).  
Figure 6: BESS state of charge (case 1).  
From the Case 2 simulation results, the minimum total investment cost is US $13,355,165.67 with an optimum size of the PV, WT and BESS equal to 1401 kW, 1866 kW and 5200 kWh (with power converter capacity of 820 kW), respectively. Figure 9 illustrates the aggregated generated power from renewable energy sources, BESS discharging power and the load demand profiles. Figure 10 and Figure 11 show the charging and discharging power and the SOC of the BESS for case 2, respectively.  
profile and the load demand and thus decrease the system component capacities of the WT and BESS. The FDR shifting programs time shifts the flexible energy demands from deficit generation to surplus generation periods resulting in percentage cost proportion of the BESS to the total investment cost of 41.32% without DSM and 34.31% with DSM. The resultant system configuration with DSM resulted in a significant cost saving in the investment costs of about 12.41% with respect to the case 1 (without DSM). This investment cost saving is due to the ability of the DSM strategy to reduce the gap between generation and demand for most of the considered time instance. Its worth noting that the increase in PV capacity and decrease in WT optimal system configuration is due to costs per kW difference between the two technologies, PV system is relatively cheaper per kW compared to WT generation technology.