esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### LCA and C2C for Product Design  
A comparison of the LCA and C2C methods was carried out, analyzing the scope, procedure, impact categories and presentation of results, in order to find the most appropriate way to integrate the categories used in LCA (all of them in the environmental and social dimensions) and C2C (material health, material reutilization, renewable energy and carbon management, water stewardship and social fairness). Firstly, LCA is a quantitative and systematic assessment tool that determines the environmental, social and economic impact that a product or system generates throughout its life cycle, from the extraction of raw materials to the end of life [50; 51]. Although this method does not provide a design guide, or strategies for improving environmental or social performance, the results of impact and damage generated can be used to progress eco-efficiency: to identify opportunities for improvement, compare different alternatives and provide truthful and representative information for the decision-making process, or dissemination to the community [52]. On the other hand, cradle to cradle (C2C) is a paradigm oriented towards eco-effectiveness [18] and a circular economy [53]. C2C defines a set of design principles as a guide for innovation and continuous improvement, considering environmental and social aspects, from the point of view of zero and positive impact (damage regeneration). Currently, results are qualitative and obtained through weightings of simplified categories. Furthermore, the implementation of solutions requires major social and infrastructure changes, some of which are not currently feasible, such as, for example, achieving a closed cycle of matter exchange, total waste recovery or the use of 100% renewable energy. In the available assessment process, one of the main drawbacks is the lack of relationship between the design principles (water equals food; 100% renewable solar energy; celebrating diversity) and categories of analysis (material health, material reutilization, renewable energy and carbon management, water stewardship and social fairness). Therefore, it is not possible to verify whether a design meets these principles or not. An estimation is achieved with the certification system offered by the company [54], with which the level of sustainable scope of the product is calculated (Basic, Bronze, Silver, Gold or Platinum), but without providing the direct measurement of these principles. Even so, the design strategies are representative and provide eco-innovative solutions to advance sustainable and eco-effective products. Table 1 shows the simplified comparative study between LCA and C2C; the main characteristics of each method are collected. This evaluation was carried out to enable the integration of the two approaches [4; 48; 49; 55; 56; 57; 58; 59].
## 2 Results and Discussion
### Antioxidant Activity
#### 2.4.4 ABTS\({}^{+}\bullet\) Scavenging Assay  
ABTS\({}^{+}\bullet\) scavenging assay is one of the most widely assay used to screen antiradical peptides. In this assay, the blue/green ABTS\({}^{+}\bullet\) produced by oxidation of ABTS with potassium persulfate has an absorption maximum of 734 nm and can be converted back to its colorless neutral form by antioxidants following the decrease of the absorption [43]. As shown in Figure 5D, MCPE-A, MCPE-B, and MCPE-C showed strong ABTS\({}^{+}\bullet\) scavenging activities in a dose-effect manner with \(\mathrm{EC_{50}}\) values of 0.10, 0.05, and 0.07 mg/mL, respectively. MCPE-B showed the strongest ABTS\({}^{+}\bullet\) scavenging activity among three isolated peptides, but still weaker than ascorbic acid at the same concentration. The \(\mathrm{EC_{50}}\) of MCPE-B was significantly lower than those of peptides from protein hydrolysates of salmon (FLNEFLIHV: 1.548 mg/mL) [16], Chinese cherry seeds (FPELLI: 0.40 mg/mL; VFAAL: 0.38 mg/mL) [4], corn gluten meal (FLPF: 1.497 mg/mL; LPF: 1.013 mg/mL; LLPF: 1.031 mg/mL) [38], skate cartilages (FIMGPY: 1.04 mg/mL; GPAGDY: 0.77 mg/mL; IVAGPQ: 1.29 mg/mL) [24], grass camp skin (GFGPL: 0.328 mg/mL; VGGRP: 0.465 mg/mL) [34], and blueinf leatherjacket heads (WEGPK: 5.407 mg/mL; Gly-Pro-Pro (GPP): 2.472 mg/mL; GVPLT: 3.124 mg/mL) [8]. These results indicated that MCPE-A, MCPE-B, and MCPE-C especially MCPE-B have the strong ability to convert ABTS\({}^{+}\bullet\) to its colorless neutral form and block the free radical reaction.
## 4 System Model and Assumptions
### Scalability Parameters  
To evaluate the scalability of the blockchain, two key parameters are introduced:  
* Desired Throughput (\(T_{desired}\)): This parameter represents the high transaction rate that is necessary for the system to be considered scalable. It sets a baseline for the minimum transactions per second (TPS) the system must handle to meet the requirements of practical applications.
* Maximum Latency (\(L_{max}\)): This defines the maximum acceptable confirmation time for users. Latency is critical for user experience, and \(L_{max}\) ensures that the system can finalize transactions within a reasonable time frame, maintaining responsiveness.  
These values are often set based on industry standards or specific application requirements, ensuring that the blockchain meets performance benchmarks. For example, in a practical scenario, \(T_{desired}\) might be set at 1000 TPS to align with the throughput of traditional payment systems, while \(L_{max}\) could be defined as 5 s to provide quick transaction confirmations and a positive user experience. Additionally, it is assumed that the network maintains an honest majority, ensuring that adversarial behavior does not undermine performance.
## 4 ON Model
The devices move in closed loops, meeting at the indicated times; (**b**) full opportunistic network model built offline using recorded traces (from [8]); (**c**) Online model construction. Learned nodes and edges up to \(t=35\) for devices \(A\) (blue) and \(B\) (red) are shown. In an ON, the order of encounters is critical for delivering a message. In Figure 1, the path A-D-C depends on nodes A and D encountering before D and C. This dependency is naturally represented in the ONM. In this model, routing messages in an ON is equivalent to routing them in a static graph. Edges can have different costs associated, allowing great flexibility in the definition of optimality, as discussed in Section 5. The model allows representing multiple simultaneous transfers if the underlying network supports them, in the form of multiple _copy edges_ leaving or arriving at a single node. For example, a broadcast transmission would be represented by multiple _copy edges_ leaving a single node, each reaching a different device in range. Furthermore, additional information can be stored in the graph to support learning algorithms and performance modeling. For example, nodes may have associated the location and remaining battery storage, and _copy edges_ can be assigned radio propagation characteristics. As mentioned before, many ON networks are not entirely random but exhibit patterns. These patterns manifest themselves in the ONM. For example, the closeness between devices reflects in more frequent copy edges, while periodic trajectories manifest as a recurrence of copy edges at specific times. This allows using the ONM as a base to apply ML techniques.
# Effects of Intrinsic Factors on the Stability of Silver Nanowire-Based Flexible Electrodes
## 1 Introduction
To achieve control over the surface density of AgNPs, vacuum filtration was used in this work to prepare AgNW films with different surface densities on filter membranes, and a substrate transfer method was used to transfer AgNW films from filter membranes to PET or PVA substrates. To further investigate the effect of AgNW surface network connections on electrode stability, graphene oxide (GO) was used to modify the surface of the PET-AgNW and PVA-AgNW electrodes. The effects of the intrinsic parameters, including flexible substrates, different AgNW surface densities, and AgNW network connections after GO surface modification, on the stability of AgNW electrodes were investigated based on the electrical and structural properties. This work aims to provide an understanding of the relationships between intrinsic factors and the stability of AgNW electrodes and to provide a guide for the development of highly stable AgNW electrodes.
# Thermal Performance of Cryogenic Micro-Pin Fin Coolers with Two-Phase Liquid Nitrogen Flows
## 2 Correlations
### Pressure Drop  
The local two-phase pressure drop \(\Delta P_{tp}(x)\) is determined using the equation below [10]:  
\[\Delta P_{tp}(x)=\frac{1}{x-x_{i}}\int_{xi}^{x}\phi_{l}^{2}\Big{(}\Delta P_{f} \Big{)}_{f}dx+\Delta P_{accl} \tag{14}\]  
The acceleration pressure drop, \(\Delta P_{accl}\) is evaluated as [10]:  
\[\Delta P_{accl}=\left\{\frac{G^{2}x^{2}}{\rho_{g}\alpha}+\frac{G^{2}(1-x)^{2} }{\rho_{l}(1-\alpha)}\right\}_{outlet}-\left\{\frac{G^{2}x^{2}}{\rho_{g} \alpha}+\frac{G^{2}(1-x)^{2}}{\rho_{l}(1-\alpha)}\right\}_{inlet} \tag{15}\]  
\[\alpha_{h}=\frac{x\rho_{g}}{(1-x)v_{l}+xv_{g}} \tag{16}\]  
\[\frac{\alpha}{\alpha_{h}}=1+0.04503(f)^{0.34}\ln(x) \tag{17}\]  
where \(\alpha\) is the void fraction, \(\alpha_{h}\) is the homogenous void fraction, \(\rho_{g}\) is the gas density, \(\rho_{l}\) is the liquid density, \(v_{g}\) is the gas specific volume, \(v_{l}\) is the liquid specific volume and \(f\) is the liquid friction factor.
# Beyond the Horizon, Backhaul Connectivity for Offshore IoT Devices
###### Abstract
By keeping in view above stated requirements of targeted studies on over the sea IoT devices connectivity using ED, we gather real-time experimental measurements for large-scale characterization in the Malaysian Sea region and perform extensive simulations to explore backhaul connectivity of IoT devices using ED. Therefore, the aim of this paper is to analyze the average measured path loss in ED at varying distances and then predict additional path loss variations, also termed as slow fading based on the probability of occurrence of ED height changes. Based on complete path loss information, availability, and capacity of a beyond the horizon backhaul link  
Figure 1: Beyond the horizon Backhaul link to provide connectivity to offshore IoT devices. is calculated to establish connectivity to offshore IoT devices. Main contributions of the paper are:  
* The efficiency of the backhaul connectivity link for offshore IoT devices is measured using average values of path loss in ED at varying distances. * Due to ED height variations, the additional path loss or slow fading with respect to time is estimated using ED climatology [15]. For varying ED climatology, we extended our work in [15] to compute fade margin at 5.8 GHz frequency. * Using measured average path loss and additional slow fading, we have estimated a data rate of 50 Mbps for a 60 km beyond the horizon link to offshore IoT devices with 99% availability. Rest of the paper is organized as follows: Section 2 presents average path loss prediction in ED at 5.8 GHz using PE tool-based simulations, verified with field measurements, and then fitted with Simplified Path Loss model. In Section 3, additional slow fading in ED is predicted at 5.8 GHz using simulations and we thoroughly analyzed the data. Section 4 has the link budget calculations to estimate availability vs. capacity in ED for 30 km, 40 km, 55 km, 64 km, and 80 km ranges. We conclude the paper in Section 4 along with future research directions.
## 4 Cost of Energy Results and Discussion
### LCoE and SLCoE Comparison with Other Studies
Although they have similar system electricity costs, there are several differences: [102] and [142] use different storage technologies simultaneously, include more sectors (industry, agriculture, fishing, and forestry) and renewable energy sources, and either simulate for entire countries (Germany and Spain) [102] or cities in a different continent (North America) [142].
## 2 Theoretical Analysis of Energy Spectrum Study
### Ground Motion Propagation of the Soil
#### 2.2.2 Equation of Ground Motion Propagation  
The equation of motion for vertically propagating ground motion through an unbounded soil medium can be written as follows:  
\[\rho\frac{\partial^{2}u}{\partial z^{2}} = \frac{\partial\tau}{\partial z},\ \tau\ =\ G\gamma\ +\ \eta\frac{\partial\gamma}{\partial t}, \tag{5}\]  
where \(\rho\), \(\tau\), \(G\), and \(\gamma\) are the density, shear stress, shear modulus, and shear strain of the soil, respectively. Additionally, \(u\) is the soil displacement, \(z\) is the depth from the ground surface, and \(\eta\) is the viscosity constant.  
From Equation (5), we can obtain the following:  
\[\rho\frac{\partial^{2}u}{\partial t^{2}}=G\frac{\partial^{2}u}{\partial z^{2} }+\eta\frac{\partial^{3}u}{\partial z^{2}\partial t} \tag{6}\]  
Figure 2: Dynamic shear stress–shear strain curves of the Davidenkov model.  
In time domain analysis, nonlinear analysis can be used to capture the cyclic behavior of soil. In this approach, the equations of motion and equilibrium are solved in discrete time increments. The ground motion propagation equation, Equation (6), is written as follows:  
\[[M]\left(\overset{\sim}{u}\right)+[C][u]+[K][u]=-[M]\overset{\sim}{u}_{g}, \tag{7}\]  
where \(\{u\}\), \(\{u\}\), and \(\left(\overset{\sim}{u}\right)\) are the soil-nodal vectors of relative displacement, relative velocity, and relative acceleration, respectively. Additionally, \(\overset{\sim}{u}_{g}\) is the acceleration at the base of the soil; \(\{l\}\) is the unit vector; \([M]\), \([C]\), and \([K]\) are the matrices of soil mass, soil damping, and soil stiffness, respectively, and these matrices are assembled using the incremental properties of the soil layers given in Figure 1. The properties were obtained from a constitutive model (the Davidenkov model), which describes the cyclic behavior of soil. As shown in Figure 1, each individual layer \(i\) is represented by a corresponding mass \(m_{i}\), a nonlinear spring \(k_{i}\), and a dashpot with a viscous damping coefficient \(c_{i}\). Lumping together half the mass of each of two consecutive layers at their common boundary forms the mass matrix. The stiffness matrix is updated at each time increment to incorporate the nonlinearity of the soil. At each time increment, stiffness \(k_{i}^{l+\Delta t}\) for layer \(i\) is defined as follows:  
\[k_{i}^{l+\Delta t}=\frac{G_{i}^{l+\Delta t}}{h_{i}}=\frac{\Delta\tau_{i}(\gamma _{i})}{h_{i}\Delta\gamma_{i}}, \tag{8}\]  
where \(G_{i}^{l+\Delta t}\) is the current tangent shear modulus and \(h_{i}\) is the thickness of layer \(i\). In a nonlinear soil model, soil damping is captured through hysteretic loading-unloading cycles in the soil model. The use of damping matrix \([C]\) may become unnecessary in this instance. The damping matrix may be used as a mathematical convenience or to include damping at very small strains, where the response of many constitutive models is nearly linearly elastic [39].
# Distributed Reliable and Efficient Transmission Task Assignment for WSNs
## 3 Description of Reliable and Efficient Task Allocation in WSNs
### Constraints of Task Reliability and Deadline
#### 3.2.2 Task Reliability Constraint
At time \(t\), \(R_{s_{i},sink}^{i}(t,i)\) is the probability that there exists at least one path whose packet delivery rate is greater than the threshold of \(i\)-th transmission in the disjoint minimal path sets (from \(s_{i}\) to sink) is shown in Equation (10). \[R_{s_{i},sink}^{i}(t,i)=1-\prod_{j=1}^{l}[1-R_{path(s_{i},sink)}^{j}(t,i)] \tag{10}\]
## 2 Theoretical Derivation of PIC Model
### Contact Pressure Caused by Wire Twisting
#### 2.2.3 Non-Diagonal Contact Points in Same Layer  
If the contact point between the wires is not on the diagonal but between two wires of the same layer, it is as shown in Figure 4(c.1,c.2).  
According to the principle of force equilibrium, the contact pressure on both sides of the contact points within the same layer of wires is equal. Therefore, the contact pressure between the wires within the same layer will eventually be equal to the interlayer contact pressure between the diagonal wires in the other half of the section (as shown in Figure 7a, where \(q_{1(4,1)}^{(3)}\) is equal to \(q_{1(4,1)}^{(6)}\), and \(q_{1(4,1)}^{(6)}\) can be calculated from \(q_{1(5,2)}^{(4)}\); Figure 7b,c has similar rules of computation). In such cases, the contact pressure between wires within the same layer can be calculated by the following formula:  
\[q_{1(i,j)}^{(k)}=q_{1(i,1)}^{(h)} \tag{7}\]  
where \(\langle k\rangle\) represents the position of the contact point on the wire, and \(k\) can take values 3 and 6; \(\langle h\rangle\) represents the contact point with an equal contact pressure between wires within the same layer, and \(h\) can take values 1 or 2.  
Figure 6: Superposition of contact pressure in different layers: (**a**) contact force and twisting force in the (5,2) wire, and \(q_{(5,2)}^{(w)}\) is the twisting force in (5,2) wire, \(\beta_{a1}\) is the distribution coefficient of twisting force at contact point, (**b**) contact force and twisting force in (4,2) wire, (**c**) contact force and twisting force in (3,2) wire. The red arrow indicates the contact force and the black arrow indicates the twisting force of each wire.  
Figure 7: Superposition of contact pressure in the same layer: (**a**) contact pressure in fourth layer of the cable, \(q_{(4,1)}^{(2)}\) and \(q_{(4,1)}^{(3)}\) are the contact points on both sides of the (4,1) wire, which can be calculated through the contact pressure between (4,1) and (5,2) wires, (**b**) contact pressure in third layer of the cable, (**c**) contact pressure in second layer of the cable.  
For example, in the case of Figure 7, where the marked contact points have equal contact pressure, one can refer to the case of interlayer contact points and calculate the contact pressure between wires within the same layer.  
Through Formulas (2), (4), (7), the contact pressure caused by twisting at the six contact points around a single wire can be calculated.
## 2 Damage Detection Using a Stationary Vehicle  
This paper proposes a baseline-free method utilizing a set of advanced signal processing techniques to process the recorded vibration signal of a stationary vehicle. The first step in the proposed method is to eliminate vehicle's own frequency, as the recorded signal contains both the bridge and vehicle vibration signals. By removing the vehicle's own vibration, the obtained signal contains only the bridge's vibration. This is done by computing the contact-point response of the stationary vehicle. [PERSON], [PERSON] [21] have shown good agreement between the contact-point response and the bridge response under different circumstances. In the second step, the contact-point response is decomposed into different mono-component signals, which reflect the state of the bridge. A new technique was recently proposed, called the variational mode decomposition (VMD), which has shown good outcomes in decomposing time series signals. Finally, instantaneous frequency (IA) is computed for high frequency signals to show the damage as irregularities or as peaks at the damage location. Figure 1 shows the steps of the proposed method. The next sections elaborate, in detail, each step of the proposed method.
# Loop Closure Detection Based on Multi-Scale Deep Feature Fusion
## 2 Related work
Deep learning can extract abstract and high-level features of the input image through multi-layer neural networks, which is more robust to changes in environmental factors [23; 24]. Therefore, it has been widely used in image classification [25] and image retrieval [26]. Considering that visual loop closure detection is similar to image classification and image retrieval, researchers have tried to apply deep learning to loop closure detection. [PERSON] et al. [27; 28] took advantage of Autoencoder to extract image features and used the similarity measurement matrix to detect closed loops, which got high accuracy on public datasets. [PERSON] et al. [29] applied FLCNN (fast and lightweight convolutional neural networks) to extract image features and calculate the similarity matrix, which further improved the real-time and accuracy of loop closure detection. [PERSON] et al. [30] extracted image features by PCANet, and proved that these featuresare superior to traditional manual design features. [PERSON] et al. [31] used PlaceCNN to extract image features for loop closure detection, which got high accuracy even when the light changed. However, these methods relied on local deep features and ignored the scale problem.
# Investigation of Different Storage Systems for Solar-Driven Organic Rankine Cycle  
[PERSON]  
Theoretical author of the present work is the study of different thermal storage systems for a solar-fed organic Rankine cycle (ORC) system that operates with parabolic trough collectors. The conventional design with sensible thermal oil storage is compared with a storage configuration with thermal oil and ceramic rocks, as well as the use of latent storage with phase change materials (PCMs) is investigated. The initial system is studied parametrically, and it is properly designed to order for the cycle to have high performance. Different organic fluids are studied in the organic Rankine cycle and different rocks are investigated as storage materials. Toluene is found to be the best candidate in the cycle and ceramic rocks are found to be the best candidate energetically and financially. The final results proved that both the thermal oil-ceramic rocks and the PCM are better technologies than the simple sensible thermal oil storage. For the design with a 180 m\({}^{2}\) collecting area and 8 m\({}^{3}\) storage tank volume, the thermal oil-ceramic rocks design leads to 13.89% system efficiency and net present value (NPV) to 129.73 k\(\epsilon\), the PCM storage to 13.97% and 128.66 k\(\epsilon\), respectively, while the pure thermal oil case leads to 12.48% and 105.32 k\(\epsilon\), respectively. Moreover, it is useful to state that when the collecting area is varied from 160 m\({}^{2}\) to 200 m\({}^{2}\) with the tank volume at 8 m\({}^{3}\), the efficiency enhancement with ceramic rocks compared to pure oil ranges from 8.99% up to 12.39%, while the enhancement with PCM ranges from 7.96% to 13.26%. For the same conditions, the NPV is improved with ceramic rocks from 18.35% to 25.79%, while with PCM from 14.17% to 25.29%.
# Nonlinear Analysis of Built-in Sensor in Smart Device under the Condition of Voice Actuating
## 5 Nonlinear Mapping between Microphone and Accelerometer  
Since the sampling rates of a microphone of VCS (44 kHz) and built-in accelerometer (200 Hz) have a large gap, it is hard to match their record voice directly. The nonlinear mapping phenomenon motivated us to find a matching relation between the microphone readings and the low sampling rate sensor data. By using nonlinear mapping, we could convert the microphone data to low-frequency data and compare that with the same frequency range motion sensor for similarity check.  
We used a voice application to generate a sound of pure frequency tones in the frequency range of 100 Hz-1600 Hz (with 100 Hz interval) and checked whether the signal could be observed in the frequency domain of microphone and built-in accelerometer. Figure 13 shows that some high-frequency voices could be observed in accelerometer (230 Hz). Significant frequency response on accelerometer readings was on 400 Hz and 1600 Hz.  
Figure 13: Nonlinear mapping between microphone and accelerometer.