esa-sceva/satcom-chunk-collection huggingface dataset:
# Graphene-Based Flexible Electrode for Electrocardiogram Signal Monitoring
## 2 Research on Graphene-Based Flexible ECG Electrodes
### Modified-Graphene-Based ECG Electrodes
#### 2.5.5 Hybrid Copper Nanoparticles-Graphene Oxide ECG Electrode  
In ECG electrodes, graphene cannot only be used as a conductive material, but also as auxiliary material to help conductive materials achieve better performance. Copper nanoparticles (CuNPs) are inexpensive metal particles with high conductivity and are harmless to skin [73]. However, their large surface area makes them vulnerable to oxidation when exposed to air, resulting in a rapid reduction of their electrical conductivity. Besides, agglomeration leads to uneven conductivity in CuNP-based electrodes. To improve their air stability and dispersion uniformity, [PERSON] et al. used CuNPs, graphene oxide, and polyethylene glycol-trimethylolpropane-trimellitic anhydride (PTT) to fabricate a CuNP/PTT/GO composite ECG electrode [74]. As the stabilizer of CuNPs, PTT absorbed the CuNPs through noncovalent van der Waals force and ionic-charge interactions of the noncovalent bond. Most significantly, the epoxy groups on GO can form an electrostatic attraction force to the Cu\({}^{2+}\), which makes the CuNPs realize nanoscale stabilization (Figure 4e). The organic/inorganic hybrid GO/CuNPs shows long-term stability and ultrahigh conductivity (0.906 \(\Omega\)/square after 30 days of storage), which are suitable for long-term wearable ECG monitoring.
# A Survey of of Side-Channel Attacks and Mitigation for Processor Interconnects
## 5 Distance-Based Interconnect Side-Channel
### Distance-Based Side Channel  
Multi-core processors employ distributed LLCs to enhance memory-level parallelism by allowing concurrent requests to different banks. However, from a negative perspective, the latency of accessing different banks may vary depending on the distance from the requesting core. Hence, attackers can exploit addresses with significant differences in LLC hit times to construct side-channel or covert channels. Specifically, adjacent entries in a large array may be mapped to different LLC slices based on physical addresses and slice hash algorithms, and hence located in banks at different distances. By accessing these two adjacent entries, the attacker can infer sensitive information through measurable differences in LLC access times.  
**Covert Channel.** In covert channels, the sender and receiver may agree upon a shared timer for the receiver to measure the time taken by the sender's cache access operations. The sender induces a private cache miss and sends requests to LLC slices located on banks at different distances. When sending a bit \"1\", the sender accesses array entries mapped to LLC slices located farther away, causing the receiver to observe longer access delays through the timer. Conversely, when the sender accesses local or closer LLC slices, the receiver observes lower delays, decoding this signal as a bit \"0\".  
**Side Channel.** Distance-based side-channel attacks require attackers to determine the timing of sensitive operations in the victim's program through interaction with the victim, such as message exchange via networks or inter-process communication (IPC), detecting changes in shared variables, or utilizing other side channels. By exploiting timing differences, attackers can deduce whether the victim's program accessed the LLC on a closer or farther bank, implying access to different cache locations, as shown in Figure 5. In some encryption algorithms, such access pattern information can be used to infer critical secrets, such as AES algorithm key bits. Compared to contention-based side-channel attacks, distance-based side-channel attacks do not require the construction of contention or interference but demand the ability to directly determine the timing of victim operations and acquire fine-grained timing information about individual memory accesses or at least memory access groups.
## 2 Thermodynamic Analysis of the Solar ORC System with Zeotropic Mixtures  
### Description of the System  
The solar ORC system comprises essentially six elements, i.e., a pump, an evaporator, an expander, an optional regenerator, a condenser, and a generator for electricity production. Figure 1 illustrates a basic schematic of a solar-powered regenerative ORC system. Figure 2 shows a diagram of the equivalent cycle in temperature-entropy (T-s) diagram. The pump transfers the organic WF to the evaporator, where the fluid is preheated by the regenerator and vaporised. The vapour flows into the expander where it is expanded to the condensing pressure and lastly, it is condensed to saturated liquid in the condenser by rejecting heat to atmosphere. A heat exchanger working as regenerator is employed to recuperate the heat at the expander outlet and preheat the compressed liquid before inflowing in the evaporator to enhance the system efficiency. This system contains a solar energy collecting system, which includes a solar flat plat collector (FPC) or evacuated tube collector (ETC); a thermal energy storage system which is utilised when solar radiation is not sufficient, and to supply the ORC. In general, solar FPCs can deliver hot water at 50-90 \({}^{\circ}\)C and solar ETCs can provide 60-150 \({}^{\circ}\)C. When its temperature falls below the reference temperature, a standby heater is used to rise the temperature of the storage tank to the suitable setting point.
## 5 Conclusions  
Based on several simultaneously measured positions of smartphones, the present study shows that the position accuracy under difficult conditions, such as in the forest, increases due to technical progress. It turns out, the multi-frequency reception and the availability of GNSS raw data of the smartphones, in particular, still have a lot of potential to increase the position accuracy under a canopy.  
The current development of smartphone positioning accuracy is not yet complete. Although it is already sufficient for many applications, a higher smartphone location accuracy will result in many benefits and new uses, especially under challenging conditions such as canopy coverage. The trend towards higher accuracy in the mass market has become evident with the introduction of multi-constellation, multi-frequency smartphones. It will undoubtedly continue with, e.g., improvements in the new generation of GNSS chipsets and the smartphone positioning algorithms [49] or other solutions formerly intended for high-end receivers, such as RTK solutions for smartphones [98]. Therefore, there is still a need for development and research in the future.  
This research was funded by the Association to Promote Research in Forestry and Wood Economics at the of the University of Freiburg (GFH). The article processing charge was funded by the Baden-Wurttemberg Ministry of Science, Research, and Art and the University of Freiburg in the funding program, Open Access Publishing.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The author declares that there are no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Nighttime Image Dehazing Based on Point Light Sources
## 3 Proposed Method
### Night-Time Dehaze
#### 3.4.2 Estimating Transmittance  
We will use different transmittance estimation methods according to different regions, and obtain their respective transmittances in the near light source area and the non-near light source area. According to the two fusion and refinement, we can obtain the final transmittance.  
* Non-near light source area. It is less affected by artificial light sources, and the transmittance of the dark channel prior is superior, so we use the DCP method to estimate it: \[t_{i}=1-w\min_{j\in\Omega}\left[\min\frac{I^{c}(j)}{A^{c}}\right]\] (16)
* Near light source area. Due to the influence of the halo effect caused by the transmittance of the dark channel, we propose to re-optimize the transmittance to accurately estimate the transmittance and reduce the Halo effect. \(S_{i}\) is assumed to be the minimum value of a channel in the near light source area, and since the transmittance is continuous in the local area of the image and suddenly changed at a depth of field, the calculation is converted into finding the maximum transmittance: \[\arg\max\int t-\lambda\|\
abla t\|^{2}\] (17) \[0<t_{i\in\Omega}<S_{i}\] (18) where \(\lambda\) represents the smoothness of the optimized transmittance. In addition, use edge-preserving filtering on the transmittance to make it have the same gradient information as the original image.
* Transmittance Fusion. \[t=w_{i}t_{i\in\Omega}+t_{i\in\Omega}\] (19) \(w_{i}\) is the adjustment correction coefficient. \[w_{i}=\frac{\max(K_{i},t_{i})}{t_{i}}+\alpha\] (20) \(\alpha\) is the fog-free image is the adjustment correction coefficient \[J_{i}=\frac{I_{i}^{\lambda}-A_{i}^{\lambda}\cdot\max(K_{i},1-t_{i})}{\max(t_ {i},K_{i})}\] (21) In this paper, the transmittance is corrected by defining the near and non-near light source area and fusing the transmittance of the area to solve the halo phenomenon near the light source.
## 3 Results and Discussion
### Amino Acid and Soluble Protein Content  
Proteins and amino acids are the final products of plant N metabolism, and their content can preliminarily evaluate the absorption and utilization of N by plants. In shoots, only 50 nm CuO NPs at 10 mg/kg increased the amino acid contents by 41.4%, while other treatments had no impact (Figure 4A). In roots, both sizes of CuO NP treatments increased amino acid contents significantly. At 1 mg/kg, 20 and 50 nm CuO NP treatments increased the amino acid content by 41% and 30%, while increasing it by 53% and 72.1% at 10 mg/kg, respectively, relative to the control (Figure 4B). Amino acids, as the basic units of protein, play a critical role in plant metabolic processes [38]. It is demonstrated that TiO2 NPs increase the amino acid contents and enzymatic antioxidant activities in salt-stressed plants [40]. Thus, the addition of CuO NPs within appropriate use doses could maintain the healthy status of legume crops via elevating amino acid levels. Similarly, CuO NP treatments also increased the protein contents in shoots. For instance, 20 nm CuO NP treatments increased the protein contents by 10.3% and 19.5% at 1 and 10 mg/kg, while 50 nm CuO NP treatments increased them by 9.2% and 15.1%, respectively, relative to the control (Figure 4C). In roots, only 50 nm CuO NP treatments increased protein contents by 81.2% relative to the control, while others had no impact (Figure 4D).  
Figure 3: NH4+ content (**A**,**B**), GS enzyme activity (**C**,**D**), and GOGAT enzyme activity (**E**,**F**) in soybean shoots and roots in the 20 and 50 nm CuO NP treatments at 1 and 10 mg/kg for three weeks. Error bars represent the standard error of 5 replicates, and the different letters in each panel represent significantly different values at \(p<0.05\).
## 1 Introduction
The main target of the Delphi4 LED H2020 ECSEL European research project [20] was to develop appropriate multi-domain LED measurement and modeling methodologies, along with an LED product development workflow in which such compact models are used. A major result of the Delphi4 LED project was the refinement and reformulation of a prior multi-domain LED model that was implemented and tested under different environments, with the major purpose of supporting the virtual prototyping of LED-based luminaires [21; 22]. This multi-domain model has 27 parameters that should be fitted to a set of measured isothermal IVL characteristics of LEDs. According to the original recommendations of the Delphi4 LED project, the isothermal IVL (current-voltage-luminance) characteristics had to cover the typical operational domain of a power LED determined by 33 pairs of \(\left(I_{F},T_{f}\right)\) forward currents and junction temperatures [23]. The huge amount of measurement time needed to obtain the recommended amount of test data and the problems associated with the brute-force fitting techniques used to extract the model parameters have impeded the widespread adoption and use of the proposed multi-domain LED model. Therefore, one of the major targets of the AI-TWILIGHT H2020 ECSEL project [1] (which followed Delphi4 LED) was to improve both the measurement processes and the model parameter extraction, both in terms of speed and robustness. Important aspects of the parameter extraction are the number of LED package samples to be measured and a method of finding a single set of model parameters to describe the \"LED-type\" represented by the measured samples, which was already addressed by the Delphi4 LED project [23]. The aim of this paper is to present our recent results that aim to properly address these issues. As will be shown in Section 2, this model is based on nonlinear formulas with a wide range of possible values in the 27-dimensional parameter space that makes the parameter estimation a real challenge in computing. Our approach, described here, aims to provide a robust, fast, and precise method to extract the model parameters using the least possible sets of test data. Using the extracted parameters and the thermal model described above (the process of thermal model generation is well-established and thus not further detailed here), a digital twin of the LED chip can be developed in a SPICE-like format. This digital twin can be seamlessly integrated with the driver's model at the electrical port and with the thermal model of the surrounding structure at the thermal port.
# Detection and Treatment Methods for Perfluorinated Compounds in Wastewater Treatment Plants
## 3 Results and Discussion
### Proposal of an Optimal PFCs Treatment Process  
Based on the analysis results of each unit process and the pilot test effluent, we reviewed an efficient treatment process for removing PFCs and list the PFCs process efficiencies and cumulative removal efficiencies in Table 3. PFCs are hard to degrade and remove through the existing biological treatment methods and are thus considered non-degradable [9].  
Figure 6: Variations in PFCs concentration by granular activated carbon (GAC) EBCT.  
For the activated carbon adsorption, the longer the length of the carbon ring of the PFCs, the more the adsorption force of the activated carbon increases. If the lengths of the carbon rings are identical, the adsorption force of the activated carbon is stronger with the sulfone substituent than with the carboxyl substituent [14] As for the adsorption and removal of PFCs using zeolite, the higher the Si content in zeolite, the more the PFCs adsorption capacity increases. However, this treatment is reported to have a smaller adsorption capacity than treatment with activated carbon [15]  
Activated carbon adsorption showed a relatively higher PFCs removal than the other treatments. Therefore, it would be effective for PFCs removal in the wastewater treatment process. The PFHxS removal in the single ozone process or the single activated carbon process was low, but improved with the continuous ozone treatment process followed by the activated carbon process. Accordingly, the implementation of an oxidization process, such as the ozone process followed by an adsorption process using activated carbon, for example, is considered to be the most desirable process with a higher chance of removal.
# A Decentralized Blockchain-Based Trust Management Framework for Vehicular Ad Hoc Networks
## 3 Simulation Results and Discussion
### Discussion  
In this section, the most important features of the proposed framework are discussed and compared to the existing approaches. First of all, unlike classic approaches [13, 14] where trust is based on direct observations and recommendations, the framework proposed in this study does not rely on a recommendation system, yet it allows calculating accurate trust metrics as shown in the previous section and this is due to the accurate tier-based event authentication approach used in the first phase of the framework. The proposed framework solves also, the cold-start problem which is a limitation of the classic approaches such as [14, 15]. It arises when a vehicle enters a new network and has to cooperate with unknown vehicles. This problem is solved using Blockchain which provides a shared ledger used to securely store and share the global trust metrics of the vehicles. Additionally, unlike the existing blockchain-based frameworks [16, 17, 18, 22], the proposed framework does not rely on RSUs to play the role of miners, instead, a set of vehicles randomly selected  
Figure 11: The fraction of malicious vehicles that reach trust metric greater than or equal to 0.8 vs. PM.  
in the network play the role of miners. Only the vehicles are involved in the different phases of the framework and this is for many reasons. First, the vehicles are supplied with sensors and many potential capabilities to adequately monitor their surroundings and perceive hazardous events timely and more accurately than the RSUs. Secondly, the RSUs are very expensive to deploy everywhere on the road mainly in the earlier deployment stages of vehicular networks. More importantly, as explained in the previous section, assigning the local trust metric assessment to the RSUs may delay the global trust metrics convergence and share. Hence, in the proposed framework, the local trust metric calculation is performed by the vehicles then shared with the miners. It is worth mentioning here that it will not cause a problem in terms of resources because the vehicles are equipped with onboard units characterized by sustainable computational resources. Yet, it expedites global trust metrics calculation in the miners since they will receive from the vehicles only the local trust metrics unlike the approaches proposed in [16; 17; 18] where the vehicles share with the miners the data messages, then it is up to these latter to assess and calculate the trust metrics. Moreover, in the proposed approach, global trust metrics update is time-driven contrary to existing approaches [16; 17; 18; 22] where it is performed each time the vehicle transmits a data message. This feature allows the monitors to assess more accurately the local trust metrics of their neighbors, consequently, the global trust metrics converge rapidly to the adequate value mainly for malicious vehicles as explained in the previous section.
## 2 Related Works
### Security Credential Management System for V2V Communications  
In prior investigations, researchers delved into the Security Credential Management System SCMS, and, according to references [4; 5], SCMS proves to be a promising solution for ensuring secure information exchange in V2V scenarios. Despite its objective to provide secure authentication, authorization, and data integrity for V2V communication, the challenges outlined in [6; 7] persist and demand attention. Within the SCMS framework, vehicles are issued 20 certificates weekly to sign messages, with these certificates rotating every 5 min [8]. Consequently, a vehicle utilizes a fresh set of 20 certificates every 100 min, allowing SCMS to analyze all the certificates used by a vehicle in a day. While the SCMS system confirms the entity signing these certificates, verifying the accuracy or reliability of messages from the vehicle remains problematic.  
Figure 2: An Integrated Approach for Reputation, Offline Operation, and Privacy in V2V.  
The authors in [9] introduced a conditional privacy protection authentication scheme using short-term SCMS certificates. However, the process requires certificate exchange, posing a notable drawback. The authors in [10; 11] also proposed SCMS certificate-based authentication protocols for V2V messaging, but their schemes necessitate infrastructure. The work by [12] devised an efficient V2V network scheme with an event trigger mechanism, incorporating PKI-based signatures for emergency message validation and identification of revoked certificates for malicious vehicles. However, addressing the revocation of certificates for malicious vehicles presents challenges. Distributing CRLs to all vehicles is time- and bandwidth-intensive. As the number of revoked vehicles increases, the efficiency of identification diminishes. Ensuring prompt and secure receipt of updated CRL copies poses challenges, especially in offline areas. Some scenarios necessitate solutions beyond relying solely on SCMS to identify misbehaving vehicles.
## 3 Results
### Positioning Results of Vehicle Kinematic Experiments
From top to bottom, the numbers of PPP Float, PPP-AR, and PPP-RTK satellites, and from left to right, vehicles traveling from north to south (Route 1) and from south to north (Route 2) in Region B (in Figure 2), respectively. process in Route (2) lasted for 8 s. Similar to Region A, the PPP-RTK positioning was still the best and quickly reconverged after satellite observation returned to normal. The positioning accuracies were 0.08 m, 0.13 m, and 0.15 m in the north, east and up directions, respectively, for Route (1), and 0.53 m, 0.85 m, and 0.97 m for Route (2), respectively.
## 2 Materials and Methods
### Probability Densities of Extreme Values
The smallest contribution corresponds to the vertical Up measurements. However, it should be noted that the differences in the weights are insignificant. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \(\beta_{\text{W}}^{(E)}\) & \(\beta_{\text{W}}^{(N)}\) & \(\beta_{\text{W}}^{(\text{II})}\) & \(En_{\text{W}}^{(E)}\) & \(En_{\text{W}}^{(N)}\) & \(En_{\text{W}}^{(\text{II})}\) & \(En_{\text{S}}^{(E)}\) & \(En_{\text{S}}^{(N)}\) & \(En_{\text{S}}^{(\text{II})}\) & \(\beta_{\text{S}}^{(E)}\) & \(\beta_{\text{S}}^{(N)}\) & \(\beta_{\text{S}}^{(\text{II})}\) \\ \hline \(\beta_{\text{W}}^{(E)}\) & 1 & 0.8872 & 0.9315 & 0.9668 & 0.8746 & 0.9269 & 0.9884 & 0.8753 & 0.9336 & 0.9387 & 0.9049 & 0.8469 \\ \(\beta_{\text{W}}^{(N)}\) & 0.8872 & 1 & 0.7922 & 0.9052 & 0.9832 & 0.8161 & 0.8799 & 0.9903 & 0.8129 & 0.8215 & 0.941 & 0.7044 \\ \(\beta_{\text{W}}^{(U)}\) & 0.9315 & 0.7922 & 1 & 0.9129 & 0.7887 & 0.9764 & 0.9193 & 0.7826 & 0.9879 & 0.8994 & 0.8541 & 0.8948 \\ \(En_{\text{W}}^{(E)}\) & 0.9668 & 0.9052 & 0.9129 & 1 & 0.9141 & 0.9308 & 0.9625 & 0.8975 & 0.9253 & 0.9198 & 0.9045 & 0.8154 \\ \(En_{\text{W}}^{(N)}\) & 0.8746 & 0.9832 & 0.7887 & 0.9141 & 1 & 0.8227 & 0.8644 & 0.9851 & 0.815 & 0.8102 & 0.9372 & 0.6936 \\ \(En_{\text{S}}^{(U)}\) & 0.9269 & 0.8161 & 0.9764 & 0.9308 & 0.8227 & 1 & 0.9074 & 0.8041 & 0.9894 & 0.862 & 0.8402 & 0.8261 \\ \(En_{\text{S}}^{(E)}\) & 0.9884 & 0.8799 & 0.9193 & 0.9625 & 0.8644 & 0.9074 & 1 & 0.8701 & 0.9127 & 0.9486 & 0.8988 & 0.8565 \\ \(En_{\text{S}}^{(N)}\) & 0.8753 & 0.9903 & 0.7826 & 0.8975 & 0.9851 & 0.8041 & 0.8701 & 1 & 0.803 & 0.8236 & 0.9477 & 0.7055 \\ \(En_{\text{S}}^{(U)}\) & 0.9336 & 0.8129 & 0.9879 & 0.9253 & 0.815 & 0.9894 & 0.9127 & 0.803 & 1 & 0.8787 & 0.8524 & 0.85 \\ \(\beta_{\text{S}}^{(E)}\) & 0.9387 & 0.8215 & 0.8994 & 0.9198 & 0.8102 & 0.862 & 0.9486 & 0.8236 & 0.8787 & 1 & 0.905 & 0.9326 \\ \(\beta_{\text{S}}^{(N)}\) & 0.9049 & 0.941 & 0.8541 & 0.9045 & 0.9372 & 0.8402 & 0.8988 & 0.9477 & 0.8524 & 0.905 & 1 & 0.8453 \\ \(\beta_{\text{S}}^{(U)}\) & 0.8469 & 0.7044 & 0.8948 & 0.8154 & 0.6936 & 0.8261 & 0.8565 & 0.7055 & 0.85 & 0.9326 & 0.8453 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Matrix of pairwise correlation coefficients between 12 average probability density maps of extreme values of ground tremor statistics. Figure 5: (a)—weighted average map of distribution of extreme values of four properties of the Earth’s surface tremors for three displacement components E, N, and Up, white contour lines extract anomalous regions of Earth’s tremor as 98% quantile of probability density values; (b)—values of the squares of the main eigenvector components of the correlation matrix between the distribution maps of extreme values of four daily tremor properties of the Earth’s surface for three displacement components E, N, and Up on a regular 30 \(\times\) 30 grid of nodes, sorted in descending order.