esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Characterization  
The water contact angle test used the SZ-CAMA1 equipment from the Shanghai Xuanzhun Instrument Company (Shanghai, China). Before the test, the tablet press was used to maintain the pressure at 10 MPa for 5 min and press the sample into the shape of a round cake. The sample was then gently placed on the equipment to test its contact angle with 4 \(\mu\)L water droplets.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Samples**} & **EVA** & **MH** & **PCS** & **DVB** \\  & **(wt.\%)** & **(wt.\%)** & **(wt.\%)** & **(wt.\%)** \\ \hline EVA/MH & 50 & 50 & 0 & 0 \\ EVA/MH/PCS & 50 & 47 & 3 & 0 \\ EVA/MH/PCS/DVB & 50 & 47 & 2 & 1 \\ EVA/MH/PCS+DVB & 50 & 47 & 2 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Compositions of the EVA composites.  
Figure 1: Synthesis process of MH/PCS/DVB.  
X-ray diffraction (XRD) was conducted on a diffractometer (X'PeRT PRO, PANalytical, Almelo, The Netherlands) with Cu K\(\alpha\) (1.5406 A), a test resolution of 4 cm\({}^{-1}\), and a scanning range of 5-80\({}^{\circ}\).  
Fourier transform infrared spectroscopy (FTIR) was performed using the NICOLET iS10 infrared spectrometer manufactured by Thermo Fisher Scientific, Waltham, MA, USA. The spectral range was 4000-400 cm\({}^{-1}\), and the highest resolution was 0.4 cm\({}^{-1}\). The test resolution was 4 cm\({}^{-1}\) with 16 scans. The solution of the test was 4 cm\({}^{-1}\).  
The limiting oxygen index (LOI) test was performed using an oxygen index meter (HC-2, Jiangning Analysis Instrument Company, Nanjing, China) with samples measuring 120 mm \(\times\) 6.5 mm \(\times\) 3 mm according to the ASTMD 2863-97 standard [34].  
A UL-94 vertical burning test was carried out using a CZF-III vertical burning tester (Jiangning Analysis and Instrument Company) with samples measuring 127 mm \(\times\) 127 mm \(\times\) 3 mm according to the ASTMD 3801 standard [35].  
The combustion behaviors were evaluated according to ISO 5660-1 [36] on a CONE calorimeter (6810, Suzhou Yangyi Voucu Testing Technology Co., Ltd., Suzhou, China) with 35 kW/m\({}^{2}\) heat flux. Various parameters such as the heat release rate (HRR), total heat release (THR), total smoke release (TSP), and mass loss (MASS) were obtained via the CONE test.  
A TGA 4000, produced by the PerkinElmer Company of the United States (Waltham, MA, USA), was used for a thermogravimetric analysis. The test atmosphere was nitrogen, the heating rate was 10 \({}^{\circ}\)C/min, and the test temperature range was 50-800 \({}^{\circ}\)C.  
The surface elemental compositions of the char residues were analyzed via X-ray photoelectron spectroscopy (XPS) on a PHI Quantera-II SXM (Ulvac-PHI, Chigasaki, Japan).  
Scanning electron microscopy was carried out using the FEI200 scanning electron microscope of the FEI Company, Eindhoven, the Netherlands and the EDAXGenesis2000 energy spectrometer component.
## 4 Transmissibility
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Effect on Vibration Attenuation** & **Effect on Energy Harvesting** & **Remarks** \\ \hline \multicolumn{4}{c}{**Strong Electromechanical Coupling**} \\ \(\theta_{1}\) and \(\theta_{2}\) & Increase in band gap width, mode merging, and reduction resonance peaks. & Power output increases with \(\theta_{1}\) & This is critical for optimizing dual functionality. \\ \hline \multirow{4}{*}{\(R_{1}\) and \(R_{2}\)} & For lower frequency attenuation, smaller \(R_{1}\) and \(R_{2}\) values are preferred, for higher frequency and stronger attenuation, higher \(R_{1}\) and \(R_{2}\) values. & Between \(10^{3}\) and \(10^{5}\) ohms results in power output with minimal drop. & Higher frequency passbands are predominantly impacted. \\ \hline \multirow{4}{*}{\(\beta_{1}\)} & Smaller or higher \(\beta_{1}\) creates broad band gap, which is desirable for vibration attenuation. & Power output in the third passband decreases as \(\beta_{1}\) deviates farther from 0.5. & It affects performance based on configuration type. \\ \hline \multicolumn{4}{c}{**Weak Electromechanical Coupling**} \\ \(\theta_{1}\) and \(\theta_{2}\) & Minimal effect. & Increasing \(\theta_{1}\) and \(\theta_{2}\) raises the local power output peaks. & Less effective for attenuating vibrations. \\ \hline \multirow{2}{*}{\(R_{1}\) and \(R_{2}\)} & Minimal effect. & Increases the local peaks of the power output. & Resistance mainly influences energy harvesting. \\ \hline \multirow{4}{*}{\(\beta_{1}\)} & As \(\beta_{1}\) moves farther from 0.5, the vibration attenuation spectrum increases. The inward configuration suppresses lower frequency vibrations. & Power can be harvested across a broad frequency range by varying \(\beta_{1}\). & It offers tunability for specific applications depending on configuration type. \\ \hline \end{tabular}
\end{table} 
Table 2: Summary of the effects of electromechanical resonators and shunt circuits on the simultaneous vibration attenuation and energy harvesting of hierarchical metamaterials. Figure 14: (**a**) Outer mass displacement transmissibility and (**b**) Normalized total power output of strongly coupled inward configuration at \(\beta_{1}=0.85\) and \(\theta_{1}=0.9\) N/V.
# Intelligent Evaluation of Chinese Hard-Pen Calligraphy Using a Siamese Transformer Network
## 3 Proposed Methodology
### Decoder  
The architecture of the Decoder section, as depicted in Figure 10, is primarily responsible for generating the output sequence, thereby mapping the token sequence back into the pixel-level space. Specifically, the Decoder initially receives a temporally informed feature as input and then proceeds to generate the output sequence progressively, utilizing a self-attention mechanism. In this study, the decoding segment incorporates a Bitemporal Attention mechanism, extending the attention mechanism across two time steps and enabling the model to simultaneously account for relationships between the two temporal instances.  
In the Decoder section, given a sequence of features \(X_{i}^{i}\), the Decoder leverages the relationship between each pixel and \(T_{new}^{i}\) to derive refined features \(X_{new}^{i}\). The initial step involves employing the Self-Attention mechanism to learn representations for each temporal instance. Its calculation is illustrated in Equations (7) and (8):  
\[MultiHead\left(X^{i,(l-1)},T_{new}^{i}\right)=concat(head_{1},\cdots,head_{h})W ^{O} \tag{7}\]  
\[where\ head_{i}=Attention\left(X^{i,(l-1)}W_{i}^{Q},T_{new}^{i}W_{i}^{K},T_{new}^ {i}W_{i}^{V}\right) \tag{8}\]  
In these two expressions above, the parameter \(head_{i}\) denotes the output of the \(i\)-th attention head, while the parameter matrices for the \(i\)-th attention head are represented by \(W_{i}^{Q}\), \(W_{i}^{K}\), \(W_{i}^{V}\), and the output matrix is denoted as \(W^{O}\). Subsequently, these are fed into the Bitemporal Attention mechanism to incorporate information across different time steps. Its expression is shown in Equation (9).  
\[BitempAttention(X_{1},X_{2})=soft\ max\left(\frac{QK^{T}}{\sqrt{d_{k}}}\right)V \tag{9}\]  
Following the processing through the attention mechanism, the data are subsequently directed into a feedforward neural network for nonlinear processing, ultimately resulting in the refined features \(X_{new}^{i}\).
# Estimation of Seismically-Induced Crest Settlement of Earth Core Rockfill Dams
###### Abstract  
The settlement at the dam crest induced by earthquake loading is an important design parameter widely used to evaluate the seismic performance of dams. Two-dimensional nonlinear dynamic analyses were performed to calculate the dam crest settlement of earth core rockfill dams (ECRDs). The numerical model was first validated against centrifuge test measurements. Comparisons showed that the numerical model provided reasonable estimates of the measured response. A series of dynamic numerical analyses were performed for a wide range of ground motions. The calculated settlements were revealed to compare well with field recordings of dam crest settlements. The settlements were also plotted against ground motion parameters, including free-field surface peak ground acceleration (_PGA_), peak ground velocity (_PGV_), Arias intensity (\(l_{a}\)), and moment magnitude (\(M\)). Predictive equations for the crest settlement were also presented. It was demonstrated that _PGA_ provided a good estimate of the settlement, but \(l_{a}\) provided the most favorable fit according to the calculations. Additionally, multiple parameters models were also presented to enhance the accuracy of the predictions.  
dynamic analysis; ECRD; crest settlement; ground motion parameter; predictive equation +
Footnote †: journal: Applied Sciences
# Linear Precoding Designs for MIMO VLC Using Multi-Color LEDs under Multiple Lighting Constraints
## 4 Linear Precoding Design
End
```  
**Algorithm 1**Iterative linear precoding design method  
In Algorithm 1, we first create the initial \(\mathbf{d}_{0}\) and \(\mathbf{F}_{0}\) randomly satisfying all the constraints. Then, at the \(n\)-th iteration, the optimal \(\mathbf{W}_{n}\) can be calculated by Equation (24) using \(\mathbf{F}_{n-1}\). Based on the fixed \(\mathbf{W}_{n}\), the optimal solution of \(\mathbf{F}_{n}\) and \(\mathbf{d}_{n}\) would be derived by the optimization Problem (26). Subsequently, the optimal \(\mathbf{W}_{n+1}\) could be obtained at the next iteration. After an amount of iterations, the iterative solution of \(\mathbf{F}_{n_{ite}}\) and \(\mathbf{d}_{n_{ite}}\) would be gained. Since the values of initial \(\mathbf{d}_{0}\) and \(\mathbf{F}_{0}\) are randomly chosen, we would repeat the whole iteration procedure for \(n_{loop}\) times to achieve an accurate solution of \(\mathbf{F}^{*}\). Now, we analyze the complexity of our proposed method in Algorithm 1. After initial \(\mathbf{d}_{0}\) and \(\mathbf{F}_{0}\) are randomly chosen, the main iteration procedure would be executed. At each iteration, the optimal \(\mathbf{W}_{n}\) is calculated using Equation (24), which costs the complexity of \(\mathcal{O}\left(\left(NN_{t}\right)^{3}\right)\) due to the calculation of matrix inversion and the matrix product. After the optimal value of \(\mathbf{W}_{n}\) is achieved, the convex optimization would be solved by the CVX toolbox, which costs the complexity of \(\mathcal{O}\left(T\left(\left(NN_{t}\right)^{2}+2 NN_{t}\right)\right)\), where \(T\) denotes the inner iteration number of the interior point method. Therefore, the total complexity of each iteration is \(\mathcal{O}\left(\left(NN_{t}\right)^{3}+T\left(NN_{t}\right)^{2}+2 TNN_{t}\right)\). Compared with the conventional zero-forcing (ZF) precoder whose complexity is \(\mathcal{O}\left(\left(NN_{t}\right)^{3}\right)\)[30], our proposed method pays more complexity to gain a better performance.
## 6 Discussion  
Preliminary analysis of the results suggests that further research in radar operation under jamming conditions is both relevant and feasible. Potential research directions include the following:  
1. Improved Adaptive Algorithms. Developing more adaptive algorithms for tracking target trajectories under complex jamming conditions, potentially incorporating deep learning and neural network approaches for greater resilience to dynamic environmental changes;
2. New Jamming Models. Formalizing and modeling more complex jamming scenarios, such as disinformation attacks and electronic interference, to better assess external factors' effects on radar systems;
3. Comparative Analysis of Algorithms. Conducting comparative analyses of tracking algorithms to evaluate their effectiveness under various conditions, including testing on real or simulated data;
4. Technology Integration. Exploring the integration of technologies such as active electronically scanned array systems and advanced signal processing techniques for more efficient radar systems;
5. Weather Impact Assessment. Studying the impact of weather conditions on radar operation, with the goal of developing methods to mitigate their effects;
6. Cross-Disciplinary Research. Combining approaches from telecommunications, control systems, and informatics to create adaptable solutions for radar systems and other technologies operating under uncertainty.  
The article presents results comparing models with a similar approach to mathematical formalization. It is important to note that these models (Kalman filters, Hidden Markov Models, Bayesian networks, etc.) [2; 3; 4; 5; 6; 24; 25; 26; 27] are applied in conditions of uncertainty and interference; however, each has its limitations, especially in complex multilayered systems. For instance, Kalman filters perform well in handling noise in linear systems, but their effectiveness can diminish in complex nonlinear scenarios with high interference density. Similarly, the HMM modeling approach assumes the existence of hidden states and requires significant computational resources for accurate modeling. GERT networks may offer greater flexibility, particularly in interference conditions where not only state probabilities but also the temporal structure of signals is important.  
It is worth acknowledging that the proposed model largely relies on theoretical assumptions. One limitation may be the necessity of using a larger volume of experimental data to validate the proposed model in real-world conditions. There is also a significant potential issue regarding the scalability of the model to more complex systems with a high level of interference. Furthermore, the article generalizes the concept of \"factors affecting the efficiency of radar systems\". This is a rather vague formulation that can be misleading. In the future, it is essential to focus on specific examples of interference that impact the system's performance with clear justification.
## 2 Related Work
### Banner-Based Methods  
The term banner refers to the device attributes contained in the protocol packets, which typically include the device type, brand, and model [16; 32; 34; 35]. Obtaining protocol banners require first sending probe packets for a specific services (i.e., ports) to the target device. If the target device runs the particular service, it will return the response packets containing device information. Since IoT devices run a large number of services, we can obtain a wide variety of banners to improve the accuracy of device identification.  
[PERSON] et al. [11] proposed a cross-layer protocol fingerprinting technique for fine-grained device identification. This approach utilized a convolutional neural network (CNN) and a long short-term memory network (LSTM) to extract and construct feature fingerprints. Nevertheless, the proactive identification method increases the identification time because it depends on the network state.  
[PERSON] et al. [15] proposed an approach for generating fine-grained fingerprints based on the subtle differences between the file systems of various firmware images. They leveraged natural language processing to process the file content and the document object model to obtain firmware fingerprints. The recall and precision of the firmware fingerprints exceeded 90%. However, this approach requires an average of 75 HTTP packets to identify the firmware version of a single device, which is inefficient for large-scale device identification.  
[PERSON] et al. [36] proposed a scalable framework for physical device profiling that leverages banner grabbing to identify device types and running services before using clock skew to determine a device ID. Although they used multiple protocols to improve the identification accuracy, the approach only ranks the popularity of application layer protocols to identify device types, which increases the communication costs. By contrast, our method balances the success rate with the communication overhead by scheduling multiple probe methods.  
However, there are two shortcomings of banner-based device identification: (1) The target device may not return the corresponding protocol banner after sending a protocol probe packet to the target; (2) The device type can be easily extracted from the banners, but most banners contain incomplete information about device properties, such as brand and model. Combining the various protocol banners supported by the device for device identification will increase the communication and time overhead considerably.
## 3 Principals of Routing Protocols and Their Breakdowns
### Geographical Information-Based Routing Protocols
#### 3.2.5. RD [49]  
The authors presented residual energy-depth (RD)-based routing, a new and fully distributed protocol. In RD, nodes use residual energy and depth information to make dispersed routing decisions and choose the forwarder nodes as shown in Figure 5a. Meanwhile, the forwarders are primarily chosen based on the depth difference due to the priority first portion as shown in Figure 5b. The authors increase the influence of residual energy of the node when the node lowers the residual energy of the low-depth node owing to continual re-selection as shown in Figure 5c. This protocol's holding time computation technique is dependent on both depth and residual energy information at the sensor nodes, as illustrated in Figure 5. The RD protocol solves the primary issue with depth-based underwater routing protocols, which is that they all take the same path to the sink nodes.
## 4 Discussion  
Comparing the results of the X-ray analysis with the stress measurements, it turns out that bombarding of HfO\({}_{2}\) during film growth with high-energy oxygen particles leads to stress-induced crystallization similar to annealing of HfO\({}_{2}\) films [19]. Investigation of rapid thermal annealing of HfO\({}_{2}\) films shows thickness reduction (i.e., volume reduction) when the films undergo the amorphous to crystalline transition [18]. Thus, the effect of the secondary ion beam is twofold. On the one hand, it densifies the film, which leads to stress build-up. On the other hand, the kinetic energy and/or the stress creates favorable conditions for the crystallization, which can reduce part of the stress. After full crystallization, further densification cannot be compensated for anymore. As a consequence, the films crack and delaminate.  
The macroscopic cracking/delamination and eventual blistering can explain why we observed a sharp decline in the net stress, which is not expected according to the [PERSON] or Bilek-McKenzie models [9; 10].  
Figure 4: The laser-induced damage (LIDT) curves for low- (0.5 GPa), moderate (1.5 GPa), and high-stress (3 GPa) specimens.  
Figure 5: Damage sites for (**a**) low- and (**b**) high-stress samples.  
The structure zone diagram (SZD), which takes the generalized deposition temperature, the ion energy as well as the film thickness into account [20] can characterize the deposition of thin films. In the case of U\({}_{\text{assist}}\) = 0, only the primary sputtered molecules from the target reach the specimen surface. A few tens of eV energy characterize these species at most. The substrate temperature is most probably close to the set 100 \({}^{\circ}\)C (\(\sim\)0.1 T\({}_{\text{m}}\) for HfO\({}_{2}\)), and thus the deposition takes place at the border of Zone 1 and Zone T. The mean free path for oxygen molecules and argon atoms for an operating pressure of 3.5 \(\times\) 10\({}^{-4}\) torr is larger than the assisting source-substrate distance, thus most of the species reach the specimen without any energy loss. Accordingly, a deposition at the border of Zone T and Zone 3 takes place. This is in agreement with the observed crystallization of the layers.  
As is observed for ion implantation and amorphization of crystals for waveguide creation, as well as the annealing of HfO\({}_{2}\) films, the amorphous material exhibits a lower refractive index compared to the crystal phase [21; 22]. This is in contrast to our measurements where amorphous films have a higher refractive index. The fact that the refractive index is very sensitive to structural changes may explain why the decrease could be a consequence of nano cracks that are not visible in microscopy inspection.  
As for the LIDT with 1064 nm wavelength, nanosecond laser pulses on IBS and e-beam deposited films indicate that the laser irradiation causes damage via thermal-induced stress, e.g., [23; 24]. We observed the expected behavior, and due to the cracks, the highly stressed HfO\({}_{2}\) is characterized by a lower LIDT.
# Mars to Earth Data Downloading: A Directory Synchronization Approach
## 2 DTNbox
### Bilateral Synchronizations: Owner and Local Copies  
In \"bilateral\" synchronizations only two nodes are involved: the \"owner\" of the directory that is synchronized, and the other node, which has only a local copy. Bilateral synchronization can be either bidirectional or unidirectional. In the former case (Figure 1a), all the changes made on either of the two paired directories are mirrored in the other. By contrast, in the latter (Figure 1b), changes on the local copy are not mirrored on the owner directory.  
The concept of owner is paramount in DTNbox. Synchronized directories must be in a specific position in the file system (e.g., /DTNbox/FoldersToSynch/nodeA/photos). This constraint is justified by two advantages: (a) it allows DTNbox to clean the file system automatically when a synchronization is closed (the original on the owner node is maintained, while local copies are cancelled; (b) it greatly facilitates the establishment of multilateral synchronizations.
# PV Sizing for EV Workplace Charging Stations--An Empirical Study in France
## 5 Sizing Procedure Examples  
### Price Examples  
Since there are several stakeholders participating in the collective self-consumption scheme, the electricity would be billed differently according to stakeholder interactions. As assumed in Section 4.1.1, these bills are computed with flat rates and the cost of the electricity subscription is not considered.  
The price of the electricity coming from the grid in this example is calculated by the Energy Regulation Commission (CRE) in France for 2023 [29]. The price structure of \(Pr_{ESEV-CPO}\) (424 \(\upbar\)/MWh in total) is then composed of:  
* Energy Component: 322 \(\upbar\)/MWh corresponding to \"non-residential blue\" tariff (version B, power subscription less than 36 kVA, included transportation, \"supplementary power case\" also called 'alloproduit' in french), as described in Annex B2 of [29];
* Contribution Component: 31 \(\upbar\)/MWh corresponding to the electricity consumption tax (Taxe Interieure sur la Consommation Finale d'Electricite) or TICFE until 31 January 2022. This contribution since then has been reduced to 1 \(\upbar\)/MWh as a result of electricity subsidy for French consumers (called \"bouclier tarifaire\" in french). Such a subsidy lasts until the end of 2024;
* Tax Component: 71 \(\upbar\)/MWh corresponding to 20% of the VAT on the energy, transport and contribution parts.  
PV fit-in-tariff is a selling price also regulated by the CRE and is currently at 120 \(\upbar\)/MWh (Tariff Tc, 100 kWp \(<P_{peak}<\) 500 kWp in 2023) [30]. The purchase price \(Pr_{PVO-LP}=Pr_{PVO-ESPV}\) is thus supposed to be equal to 120 \(\upbar\)/MWh as well. We consider  
Figure 12: Relationship 4: _PTC_ vs _SPR_, computed for different PV peak powers (0, 1, 25, 50, 75, 100, 125, 150, 175, 200) over a more than 6-year period.  
that LP cells this energy at this price (i.e., without margin) but increased with transport, contribution and taxes. The price structure of \(Pr_{LP-CPO}\) (197 \(\upxi\)/MWh in total) is as follows:  
* Energy component: \(Pr_{PVO-LP}=120\)\(\upxi\)/MWh;
* Transportation component: 13 \(\upxi\)/MWh (as explained in [31]);
* Contribution part: 31 \(\upxi\)/MWh corresponding to TICFE;
* Tax Component: 33 \(\upxi\)/MWh corresponding to 20% of the VAT on the energy, transport and contribution.  
A summary of these prices' structure described above is given in Table 5.