esa-sceva/satcom-chunk-collection huggingface dataset:
# Authenticated Multicast in Tiny Networks via an Extremely Low-Bandwidth Medium
###### Abstract  
We consider authenticating multicast messages in the case of extremely narrow communication channels, such as underwater acoustic communication, with devices such as mobile sensors creating a self-organizing autonomous network. Channel characteristics in this scenario prevent the application of digital signatures (and asymmetric cryptography in general), as it would consume too much of the available bandwidth. As communication is relatively sparse, standard symmetric methods such as TESLA have limited application in this scenario as well. Driven by real-world requirements, we focus on tiny networks of only a few nodes. This paper discusses two issues: (a) strategies of key predistribution enabling flexible creation of multicast groups; (b) authenticating multicast messages in a way that prevents an attacker impersonating the sender by subverting one or more receiver nodes and learning the symmetric keys stored by these nodes. For tiny networks, we show that scalable and asymptotically efficient solutions might be useless, and that specially tailored combinatorial approaches may confer some advantage.  
Keywords:tiny network; bandwidth; multicast; encryption; message authentication; impersonation; key management +
Footnote †: journal: _Applied_  
_aciences_
## 2 Experimental Design and Methodology  
### Material Selection and Characteristics  
The primary raw material selected for the catalytic hydrogenation process was high-purity (98.5%) commercial safflower oil, chosen for its high unsaturated fatty acid content, which enhances its chemical reactivity and thermal stability. Additionally, safflower oil's abundance, renewability, and consistent quality make it a reliable and reproducible choice, aligning with the principles of sustainability and green chemistry. The chemical purity of this oil allows for the precise execution of the hydrogenation process, which serves as the foundation for biocomposite production. The hydrogenation process was conducted using 99% pure hydrogen gas, which was supplied from a gas cylinder and regulated with a precision pressure regulator. The pressure regulator enables the precise regulation of the hydrogen flow rate and pressure, thereby maintaining the desired reaction conditions. This results in an enhanced efficiency of the hydrogenation process while ensuring consistent chemical modifications in the safflower oil. The reaction process was conducted with a nickel catalyst (99% purity, Sigma-Aldrich, St. Louis, MO, USA) in accordance with the established protocol. The nickel catalyst was selected based on its efficacy in converting unsaturated fatty acids in safflower oil into saturated compounds. Commercial polyether polyol (CPP) and MDI were procured from Puryap Company, and the selection of these materials was based on their ability to meet the experimental requirements, including chemical purity, homogeneity, and reactivity. The high purity and chemical consistency of the materials ensured the reliability and reproducibility of the experimental results, allowing for an accurate evaluation of the role of modified safflower oil in biocomposite production. This represents a crucial stage in the enhancement of the physical and chemical attributes of biocomposite matrices. MDI reacts with CPP to form a mechanically robust and flexible crosslinked polymer network, while exhibiting chemical compatibility with safflower oil. This dual compatibility provides a robust basis for evaluating the efficacy and contribution of HSO in biocomposite production. To comprehensively examine the performance of safflower oil in comparison to CPP in the biocomposite matrix, optimization studies were conducted as part of the experimental process. Safflower oil offers a renewable and environmentally friendly alternative that preserves the mechanical strength and processability advantages afforded by CPP. This underscores the potential applicability of safflower oil as a substitute for CPP in biocomposite production. To ensure a fair comparison, the experimental conditions for both components were maintained at a constant level.
# Evaluation of the Use of Compressed Sensing in Data Harvesting for Vehicular Sensor Networks
## 4 Cs-Vdh
### Query Distribution  
In this first phase, a query message (CSQuery) must be distributed inside a precise RoI so that the mobile nodes inside this area could retrieve the sensed information requested by the FC.  
Figure 8: Matching process to obtain a projection vector.  
For this purpose, we must employ a low-level protocol to flood such a query within the RoI. This task is a research topic that has already been investigated in the VANET domain and whose proposed solutions receive the name of geocast routing protocols. Protocols such as PIVCA [31] or AckPBSM [32] have presented outstanding results in this area.  
Our solution for data gathering in VSNs is agnostic to the relying technology responsible for this task which can be easily adopted. Nevertheless, it does consider the design of the information representation that must be carried inside the CSQuery message to be transmitted. The different elements that must be included in these messages were already enumerated in the previous section.  
The sequence number is an identifier associated to the packet and its issuer which is usually used for detecting duplicated packets, ignoring them this way.  
The address and location of the FC are required since it is the destination of all the sensed information and all the mobile nodes providing their information or forwarding it must be aware of. The number of cells is also essential for the vector-to-matrix conversion. Finally, the most significant component needed to apply CS is the projection vector, which allows mobile nodes to combine and compress their information by projecting their sensed measurements. In Section 2 we explained that CS relies on two principles to its correct operation, sparsity and incoherence. The projection vector must transform the sensed measurements, changing its representation in a sparse basis where only a few components are relevant.  
The main shortcoming is that there is not a solely one representation basis that makes all kind of measurements to change their representation to a sparse domain. By contrast, this representation basis, and therefore the projection vector, must be chosen according to the type of measurements that mobile nodes have to provide: temperature, \(CO_{2}\) concentration, the average speed of an urban sector or humidity to name a few. As we will detail in the evaluation section, we have experimented with the temperature information of the city. This type of measurement has specific properties such as that it does not drastically vary (i.e., high variances in a couple of seconds), and that its new values are related to the ones of a previous moment. For this reason, we have opted to specify a representation matrix based on the variance of these values in a previous moment.
## 2 Numerical Details
### Details of Neural Networks  
Figure 2 shows schematic diagrams of the present neural networks adopted for \(Re=60\) and \(Re=300\). As shown in Figure 2a, the fully connected neural network for \(Re=60\) has one hidden layer with four hidden nodes, while the numbers of input (\(p_{wall}\)) and output (\(v\)) variables are \(44\) and \(8192\), respectively. In Figure 2b,c, for the case of \(Re=300\), we employ two different neural networks of a CNN with an encoder-FCNN structure (CNN-F) and a CNN with an encoder-decoder structure (CNN-D) [32]. In the latter structure, the encoder compresses the input data and encodes them into a latent space, capturing essential features of the input data, while the decoder subsequently generates the output data based on this information [32]. For both encoder-FCNN and encoder-decoder structures employed in CNN as shown in Figure 2b,c, the kernel size of all convolution layers is \(5\times 5\), the stride is \(1\), the number of channels is \(64\), and the number of channels in the last convolution layer is \(1\). For the activation function for hidden layers, a rectified linear unit (ReLU) is applied [33]. The kernel size and stride of the max pooling and up-sampling layers are \(2\times 2\) and \(2\), respectively. The up-sampling layers in the neural network as shown in Figure 2c are widely adopted in tasks such as noise reduction for images and image  
compression and generation [34]. In recent studies adopting CNN with the up-sampling layers [21; 22; 35], it was found that high-resolution velocity fields in fluid flow could be successfully reconstructed using neural networks with the input of the low-resolution velocity field images. In the present study, we adopt a similar neural network structure with up-sampling layers (Figure 2c) to reconstruct the transverse velocity field in the wake of the cylinder but with the input variables consisting of instantaneous wall pressures on the cylinder surface. For the optimization algorithm during neural network trainings, we use the adaptive moment estimation (Adam) method [36]. Hyperparameters used for trainings are a learning rate of 0.0005, \(\beta_{1}=0.99\), \(\beta_{2}=0.999\) for the Adam method, and a mini-batch size of 800, which are comparable to those in the study by [PERSON] et al. [24]. In addition, we note that hyperparameters such as the number of channels in Figure 2c were tuned in our preliminary study. The loss function, denoted as \(\mathcal{L}\), used in the neural network training is defined as follows:  
\[\mathcal{L}=\frac{1}{2N}\sum_{n=1}^{N}{(v_{n}-\vartheta_{n})^{2}}. \tag{3}\]  
Here, \(N\) is the number of training or testing data, and \(v\) and \(\vartheta\) represent the transverse velocity obtained from the numerical simulation of flow over a circular cylinder and that predicted by the neural networks as in Figure 2, respectively. Training of neural networks is performed using the open-source software TensorFlow [37].
## 2 Literature Review
### Fjsp with Unlimited Transport Resources  
Integrating job transportation into the Fjsp and solving it by assuming unlimited transport resources has been a common approach in earlier research. [PERSON] et al. [33] identified the unrealistic omission of transportation time in traditional Fjsp models. To address this, they introduced multiple transport devices into the system, where transportation time depended solely on the distance between machines, excluding other disturbances. This study was among the first to incorporate transportation time into Fjsp and applied a novel imperialist competitive algorithm to solve the model. [PERSON] et al. [4] and [PERSON] et al. [34] formulated a multi-objective Fjsp aiming to minimize energy consumption and makespan. They demonstrated that transportation not only affects makespan but also generates additional energy consumption, impacting total shop floor energy usage. [PERSON] proposed an enhanced genetic algorithm, while [PERSON] applied an improved gray wolf optimization algorithm to tackle the problem. [PERSON] et al. [35] extended this line of research by introducing the concept of machine deterioration, where prolonged machine operation leads to decreased efficiency and increased processing time. This notion laid a foundation for the deterioration effect of AGVs proposed in this study. Further complexity was introduced by [PERSON] et al. [36], [PERSON] et al. [24], and [PERSON] et al. [37], who considered setup times and job transportation times. [PERSON] and [PERSON] also accounted for transportation energy consumption and employed multi-objective optimization algorithms, while [PERSON] focused on minimizing makespan through a multi-agent system. In all of these studies, the assumption of unlimited transport resources remained prevalent, with transportation time modeled merely as a function of machine distance. However, such assumptions diverge significantly from real manufacturing environments, where transportation time is closely tied to the number and operational state of transport devices, like battery levels. As a result, the practical feasibility of these scheduling strategies is limited.
# An Initial Case Study of the Computational Modeling of Cord-Rubber Segments
## 2 Materials and Methods
### Computational Modeling
#### 2.3.5 Finite Element Model of the Segment with a Cord Bundle as Individual Cords  
In the second variant of the computational model development for the segment with a cord bundle, attention was redirected towards achieving a more realistic representation of the cord bundle, with individual cords being modeled separately (Figure 9a). This advanced approach aimed to enhance the model's accuracy by accounting for the distinct geometry of cords.  
The cord bundle was modeled using two cross-sectional shapes for the cords: rectangular with rounded corners as well as circular. The rounded rectangular cords (Figure 9b) were employed to increase the contact surface between individual cords, thereby improving the convergence of the model. Additionally, circular cords (Figure 9c,d) were considered, featuring two distribution patterns: a regular pattern where cords were spatially arranged beside and over each other systematically (Figure 9c) and an irregular pattern characterized by a more realistic distribution (Figure 9d), allowing cords to be pushed into adjacent ones, becoming closer to real-life operational conditions.  
The cords were modeled in the case of circular cross-sections so that each cord overlapped with adjacent cords (Figure 9e). This approach was used to ensure initial penetration of the contact surfaces between the cords, thus improving the model's computational efficiency.  
A linear isotropic material model described the mechanical behavior of the cords. Like the previous model, frictional contacts were utilized to model the interactions between individual cords.
## 2 Hybrid-Energy Micro Gird Modeling
### Renewable Generation Modeling  
PV generation is affected by the intensity of solar radiation, so the output of PV is intermittent in nature. PV solar generation modeling: solar energy is the energy radiated by the Sun, which is harnessed by tapping light photons to generate electrons. The PV system consists of a photovoltaic array, which converts the light photons falling on it to electrons. This generates a DC current, which can be boosted with DC-DC converters and then inverted to deliver AC power to the loads. Usually, a silicon PV module output depends on many variables, including the type of material, temperature and solar radiance incident on the surface of the module. Its output can be expressed as follows [19]:  
\[P_{pv}=P_{STC}[1+k(T_{c}-T_{ref})]G_{T}\,/\,G_{STC} \tag{3}\]  
where \(P_{pv}\) is the output power of the PV cells; \(P_{STC}\) is the maximum test power of PV cells in standard test conditions (the light intensity is 1000 W/m\({}^{2}\), and the environment temperature is 25 \({}^{\circ}\)C). \(k\) is the power-temperature coefficient, which is \(-0.47\%\)/K. \(T_{c}\) is the working temperature of the solar panels. \(T_{ref}\) is the reference temperature, which is set as 25 \({}^{\circ}\)C.\(G_{T}\) is the light intensity. \(G_{STC}\) is the light intensity in standard text conditions, which is 1000 W/m\({}^{2}\).  
\[T_{c}=T_{a}+\frac{(NOCT-20)}{0.8}G \tag{4}\]  
where \(T_{a}\) is ambient temperature (in \({}^{\circ}\)C), \(G\) is the global solar radiation incident on a horizontal plane (in kW/m\({}^{2}\)) and \(NOCT\) is the normal operating cell temperature, which is approximately 48 \({}^{\circ}\)C [18,19].  
Wind power is influenced by wind speed, so the output of wind power is also intermittent in nature. A wind energy system is a structural energy arrangement to utilize the kinetic energy of blowing wind by using an aerodynamic machine. A wind turbine's output power is determined by the area of the turbine blades, the wind speed and the wind density of the surrounding air. However, for a fixed location, the expression for the output power of a wind turbine can be related to the wind speed with the following function [20]:\[P_{\text{W}}\left(v\right)=\begin{bmatrix}0&0\leq v\leq v_{ci}\\ P_{rated}\times\dfrac{\left(v^{3}-v_{ci}^{3}\right)}{\left(v_{r}^{3}-v_{ci}^{3} \right)}&v_{ci}\leq v\leq v_{r}\\ P_{rated}&v_{r}\leq v\leq v_{co}\end{bmatrix} \tag{5}\]  
where \(v_{ci}\), \(v_{r}\) and \(v_{co}\) are the cut-in speed, rated speed and cut-off speed of the wind turbine (WT), respectively.
## 4 Results and Discussion  
### Effects of the Blending Ratios on Emissions  
#### 4.1.1 NO\({}_{\rm x}\) Emission Characteristics  
Figure 7 shows the trend of NO\({}_{\rm x}\) emissions of different fuels at 1400 rpm. With the increase in the PODE blending ratio, the NO\({}_{\rm x}\) emission under each load condition slightly increases, and with the increase in load, the NO\({}_{\rm x}\) emission is significantly larger and the difference between different fuels increases. The rising in-cylinder temperature is the reason that the NO\({}_{\rm x}\) emission increases as engine power increases.  
The NO\({}_{\rm x}\) emission is mainly controlled by combustion temperature, the duration of high temperature, and the oxygen concentration of the mixture. The numerical model can accurately predict the variations in the engine in-cylinder temperature. The in-cylinder temperature field when burning various PODE/diesel blends is shown in Figure 8. It can be observed that the volume of the region with high temperature increases with the increase in the blending ratio. In addition, the higher oxygen content of PODE helps to improve the distribution range of oxygen atoms, thus reducing the range of the poor combustion zone with uneven mixing [33], which is beneficial to the generation of NO\({}_{\rm x}\). Therefore, the NO\({}_{\rm x}\) emission increases as the blending ratio increases.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**NO\({}_{\rm x}\) Emission Mode Fraction (\(\times 10^{-6}\))**} & **P0** & **P10** & **P15** \\ \hline Test results & 1918 & 2189 & 2200 \\ Simulation results & 1954 & 2270 & 2308 \\ Error value & 1.84\% & 3.57\% & 4.68\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of NO\({}_{\rm x}\) emission simulation and test results for different PODE/diesel blends.  
Figure 7: NO\({}_{\rm x}\) emission characteristics of different PODE/diesel blends under different loads.
## 3 Results and Discussion
### Total Phenolics (TP) and Total Flavonoids (TF) of Green Tea Extracts  
Table 2 summarizes the TP and TF values obtained for each green tea sample. The TP levels of the tea samples can be considered an indirect measure of their antioxidant activity because the basic redox mechanism of the Folin-Ciocalteu method was chosen to screen phenolic content. The TP results, expressed in milligrams of GAE/g DE, showed the highest value in July (326.92 mg GAE/g DE and 320.58 mg GAE/g DE in 2019 and 2020, respectively) followed by August (325.14 mg GAE/g DE and 315.86 mg GAE/g DE in 2019 and 2020, respectively) and the lowest value in April (291.14 mg GAE/g DE and 300.25 mg GAE/g DE in 2019 and 2020, respectively). The higher values found in samples from summer can be explained by the longer daylight and sun radiation intensity that  
Figure 1: Ferrous iron-chelating (FIC) activity in dry extracts of Azorean _Camellia sinensis_ (L.) [PERSON] green tea samples from Gorreana Tea Plantation that was obtained during the collection seasons of two different years. Values are mean \(\pm\) SD (\(n\) = 3); different letters indicate those values that are significantly different (\(p\) < 0.05) within the same column bar.  
characterize this season compared to the spring months. The other investigated samples presented very similar TP values, which ranged from 302.47 to 312.80 mg GAE/g DE and are comparable with those reported by [PERSON] et al. [32]. In general, the results from 2019 presented slightly higher values in comparison to 2020. [PERSON] et al. [33] also studied the seasonal variation of TP in tea leaves and observed that TPs were lower in cool months and higher during the warmer months. The authors also suggested that these differences may be a result of changing temperature, irradiance, and day length. [PERSON] and [PERSON] [34] also reported that the TP increased with sun exposure, while [PERSON] and [PERSON] [35] reported that the biosynthesis of TP is induced by stronger sunlight and longer day length.  
Concerning the TF levels of the tea samples, determined by an aluminum chloride colorimetric method, and expressed in milligrams of RE/g DE, differences were also observed between the two years. In 2019, the values ranged from 51.85 to 67.93 mg of RE/g DE and presented higher values in the spring months (67.93 \(>\) 67.01 \(>\) 53.60 mg of RE/g DE, in April, May, and June, respectively), while decreasing in the summer months (53.26 \(>\) 51.85 mg of RE/g DE, in July and August, respectively). Compared with 2020, the results show a similar pattern, with values that range from 50.27 to 69.57 mg of RE/g DE, except for August, which presented the second better value (68.88 mg of RE/g DE). However, these values are higher than those reported by [PERSON] et al. [30], lower than those reported by [PERSON] et al. [32], and similar to those reported by [PERSON] et al. [36].
# Mechanical Properties of Bridge Steel from the Late 19 th Century
## 3 Results
### Impact Strength  
The second phase of the task consisted of the testing of the impact strength of bridge steel in ambient temperature. The aim of these tests was the experimental definition of the impact strength of the material, by way of the measurement of energy needed to break the given sample in a defined temperature.  
The analysis was carried out on samples cut out from the structural elements of the bridge. Samples were obtained and prepared for testing in accordance with standard PN-EN ISO 377 [27]. Experiments were carried out on standard samples with V-shaped notches, of a 10 \(\times\) 10 mm cross-section and a length of 55 mm. Notches with an angle of 45\({}^{\circ}\), depth of 2 mm and the bottom radius of 0.25 mm were used. Samples cut out in a direction longitudinal and transverse to rolling were considered.  
Tests were performed in an ambient temperature of +23 \({}^{\circ}\)C. A Charpy pendulum machine with an initial energy of 300 + 10 J, which was compliant with the requirements of standards PN-EN 10045-1 [28] and PN-79/H-04371 [29], was used during experiments. The test criterion was assumed to be the breakage of the sample.  
Impact strength \(U\) [J/cm\({}^{2}\)] was determined on the basis of a population of \(n=3\) samples for both longitudinal and transverse directions of rolling. The main values were defined, such as the mean value, standard deviation and confidence interval for the assumed significance level of 5%. The results are listed in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Longitudinal Direction** & **Transverse Direction** \\ \hline
**Parameter** & **\(U\)** & **\(U\)** \\  & **[J/cm\({}^{2}\)]** & **[J/cm\({}^{2}\)]** \\ \hline Mean value & 62.9 & 107.3 \\ Standard deviation & 7.53 & 8.08 \\ Error bound & 8.53 & 9.15 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Impact strength of tested bridge steel.