esa-sceva/satcom-chunk-collection huggingface dataset:
# Electrospark Method in Galvanic Wastewater Treatment for Heavy Metal Removal
## 4 Discussion  
Analysis and comparison of the Table 10 data allow drawing the following conclusions. Flow-through treatment of galvanic wastewaters to MPC standards using the electrospray method, granular metal loading, and a low voltage (up to 1000 V) source of discharge currents is possible and technologically feasible. This electrospray method allows achieving high results of wastewater treatment from galvanic industries from heavy metals. The achieved degree of galvanic wastewaters purification in the case of moderate concentrations of heavy metal ions (Zn\({}^{2+}\), \(\Sigma\)Cr, Cu\({}^{2+}\)) is higher, sometimes by several times in comparison with the MPC standards in different countries of the world.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Country** & **Zn\({}^{2+}\), mg/dm\({}^{3}\)** & **Cr\({}^{3+}\), mg/dm\({}^{3}\)** & **Cu\({}^{2+}\), mg/dm\({}^{3}\)** \\ \hline USA & 1.48 & - & 2.07 \\ EU & 0.50 & 0.50 & 0.50 \\ Ukraine & 1.00 & 2.50 & 2.50 \\ Belarus & 5.00 & 2.50 & 1.00 \\ Russia & 2.00 & 1.00 & 0.50 \\ \hline \hline \end{tabular}
\end{table}
Table 10: The maximal permissible concentrations (MPC) of the studied metals in galvanic wastewaters in different countries.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Value** & **Zn\({}^{2+}\)** & **Cr\({}^{3+}\) + Cr\({}^{6+}\)** & **Cu\({}^{2+}\)** \\ \hline \(\Delta_{1}\)**mg/dm\({}^{3}\)** & 0.26 & 1.27 & 0.06 \\ \(\Delta_{1}\)**mg/dm\({}^{3}\)** & 0.11 & 1.27 & 0.05 \\ \(\Delta_{1}\)/\(\Delta_{2}\) & 2.36 & 1.00 & 1.20 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of purification results with changing the scheme of fluid flow implementation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**The Stored Energy in a Pulse, J** & **Zn\({}^{2+}\), mg/dm\({}^{3}\)** & **Cr\({}^{3+}\) + Cr\({}^{6+}\), mg/dm\({}^{3}\)** & **Cu\({}^{2+}\), mg/dm\({}^{3}\)** & **pH** & **Color** \\ \hline - & 0.26 & 1.27 & 0.06 & 7.45 & light yellow \\
4.5 & 0.00 & 0.0002 & 0.003 & 7.34 & colorless \\
5.5 & 0.04 & 0.0017 & 0.005 & 7.96 & colorless \\ \hline \hline \end{tabular}
\end{table}
Table 9: Influence of energy in a pulse on the degree of purification of galvanic wastewater.  
The main influence on the degree of galvanic purification, with the correct choice of the parameters of the discharge pulse, has specific processing energy. For moderate concentrations of pollutants, it may be less than 65 kJ/dm\({}^{3}\). Other parameters are a tool for purification processes or a means of regulating the modes of operation of electrical equipment.  
The issues of checking the effectiveness of galvanic purification at industrial frequencies of discharge pulses are promising, i.e., 1 kHz in the flow (single-flow) implementation to identify threshold, critical, undesirable, or positive effects. Such effects will occur at pulse frequencies above 100 Hz. The introduction of the method into the technological process at lower frequencies is impractical from the standpoint of productivity.
# FOXS-GSC--Fast Offset Xpath Service with HexagonS Communication
## 3 Proposed Solution
### Message Routing Addressed to Cloudlet
Figure 4 shows a flowchart describing the behavior of the protocol when sending the message from a vehicle to a cloudlet. The protocol receives a message (MSG) that can originate from the vehicle itself (e.g., a route request) or be a retransmission message from a neighboring vehicle (stage A in Figure 4). The protocol first checks whether the message is destined for an adjacent sector (B). If it is, the message is sent directly (G). Otherwise, Algorithm 1 is executed to choose a retransmission node (C), then MSG is sent (D). After sending the message in stage (D), a timer is activated to wait for retransmission by the relay node chosen by Algorithm 1 (E). If the message has been listened to, the protocol returns to the beginning and starts again (H). If not, recovery mode is activated (F) and the protocol checks whether \(MSG\_recoverytag\) is less than a defined N value. If it is, the \(MSG\_recoverytag\) (1) is added and Algorithm 1 is executed again (C). If not, the protocol proceeds to (H). When stage (H) is reached, the protocol returns to stage (A) to wait for a new message. Figure 4: Flowchart of message routing addressed to a cloudlet.
# Multiplexing Perfect Optical Vortex for Holographic Data Storage
## 3 Results  
### Optical Setup  
In order to validate the efficacy of the proposed holographic data storage method, a proof-of-principle experiment was conducted. The optical setup for the reconstructed process is illustrated in Figure 3.  
In our experimental setup, the wavelength of the laser was 632.8 nm and its intensity could be changed with a variable neutral density filter (NDF). A halfwave plate (HWP) was used to adjust the polarized direction of the beam to match the spatial light modulator (SLM, Holoeye PLUTO-2). The process is as follows. As the laser beam traverses through the beam splitter (BS1), it is directed towards SLM1. At SLM1, a single perfect optical vortex (POV) hologram is imprinted with an inverse topological charge of \(\ell_{\text{reading}}=-5\) and the axicon period of \(d_{\text{POV}}=-0.8\) mm. The beam reflected from SLM1 serves as the POV reading beam and is further reflected towards SLM2 by the beam splitter (BS2). The SLM2, which is responsible for imprinting the POV-multiplexing hologram, consists of a grid of \(9\times 9\) cells (\(p=q=9\)) in each point-data page. It is illuminated by the POV reading beam. The reflected beam from SLM2 carries the information of the \(k\)th data page. To obtain the reconstructed image, the reflected beam undergoes Fourier transformation using a Fourier lens with a wavelength of 150 mm. The resulting reconstructed image is captured using a charge-coupled device (CCD) as depicted in Figure 4. The pixel size of CCD is 6.45 \(\upmu\)m \(\times\) 6.45 \(\upmu\)m.  
Figure 3: The optical setup for the reconstructed process.
# Performance Analysis of the Supercritical Carbon Dioxide Re-compression Brayton Cycle
## 3 Results and Discussion  
### Thermal Efficiency (\(\eta_{th}\))  
Figure 4 shows thermal efficiency, \(\eta_{th}\) of the \(sCO_{2}\) system. As expected, the increment of \(T_{7}\) increases the efficiency of the system, meaning that more power output can be derived for the same heat input. The optimal value for the split mass fraction (\(x\)) is the one for which the thermal efficiency shows the maximum value for each \(T_{7}\) case. Moreover, as \(T_{7}\) increases, the optimal value of \(x\) slightly shifts to the right. The best efficiency is at \(x=0.74\) and \(T_{7}=700\)\({}^{\circ}\)C.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Molar Mass** & **Critical Pressure** & **Critical** & **ODP** & **GWP** \\
**(g/mol)** & **(MPa)** & **Temperature (\({}^{\circ}\)C)** & **ODP** & **GWP** \\ \hline
44.01 & 7.4 & 31.0 & 0 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Typical properties of \(CO_{2}\).  
Figure 4: The thermal efficiency of the \(sCO_{2}\) cycle at different turbine inlet temperatures (\(T_{7}\)).
## 3 Results and Discussion
### Effects of Radial Gap Ratio on the Impeller Performance  
Figures 27 and 28 illustrate the performance of the impeller according to various radial gap sizes. Changing the radial gap ratio varies the efficiency of the impeller. For the same mass flow rate, the impeller efficiency decreases with an increase in the radial gap ratio, resulting in a lower pressure ratio. However, the impeller enthalpy slightly changes. As the tip-speed Mach number increases, the improvement of the efficiency for various radial gap sizes also increases. The improvement of impeller efficiency is by 0.24%, 0.26%, 0.2  
When \(\mathrm{M_{u}}=1.16\), the effects of varying the radial gap size on the impeller enthalpy are larger than other tip-speed Mach numbers, where the enthalpy increases by increasing the radial gap ratio. Therefore, the effect of a radial gap ratio on the impeller pressure ratio at \(\mathrm{M_{u}}=1.16\) is negligible. The efficiency differences that are due to the differences in total temperature values creates a higher work input for the impeller as the radial gap ratio decreases from 1.14 to 1.04. [PERSON] [38] demonstrated that the impeller-diffuser interaction may affect the recirculation and slip. The difference in work input for the various radial gap ratio that is recognized by the enthalpy difference cannot be caused by slip as the relative flow angle is almost the same for the different radial gap ratios. Therefore, it can be realized that this discrepancy in work input value for different radial gap ratios is because of the recirculation that is a factor of work input generation in the impeller. As a result, the recirculation in impeller increases by reducing the radial gap ratio [18]. However, the impeller performance characteristics at \(\mathrm{M_{u}}=0.69\) are not affected by changing the radial gap ratio.  
Figure 29 illustrates the efficiencies over normalized mass flow rate. As the choke mass flow and the surge are not the same for all radial gap ratios for particular rotational speeds, normalized mass flow is used here instead of the corrected mass flow rate. For all cases, 35% of the normalized mass flow occurs nearly at the peak efficiency point. The optimum radial gap ratio can be designated based on the maximum stage efficiency (Figures 14 and 29) and the diffuser performance (Figures 23 and 25).  
Figure 28: The pressure ratio of the impeller at different \(\mathrm{M_{u}}\).  
According to Figures 14, 23, 25 and 29, when the tip-speed Mach number is less than one, the change in the radial gap size has little influence on the maximum stage efficiency and the diffuser performance, indicating that the radial gap ratio could be within a wide range for a centrifugal compressor design. Nevertheless, when the tip-speed Mach number is larger than 1.0, both the maximum stage efficiency and the diffuser performance become very sensitive to the radial gap. In this case, a radial gap ratio close to 1.08 seems to be the optimal choice to obtain better stage efficiency and best diffuser performance.
## 2 Hazard Calculations for Areal Sources
\[\mu_{NE}(M,R,S,Lat_{e},Lon_{e},Lat_{s},Lon_{s})=\mu_{erg}(M,R,S)+\Delta\mu( Lat_{e},Lon_{e},Lat_{s},Lon_{s}) \tag{4}\]  
The net non-ergodic term for the shift in the median ground motion is given by the sum of the non-ergodic site, path, and source terms:  
\[\Delta\mu(Lat_{e},Lon_{e},Lat_{s},Lon_{s})=\delta S2S_{s}+\delta P2P_{es}+ \delta L2L_{e} \tag{5}\]  
in which \(S2S_{s}\) is the non-ergodic site term for site \(s\), \(L2L_{s}\) is the non-ergodic source term for source \(e\), and \(P2P_{es}\) is the non-ergodic path term between site \(s\) and source \(e\). To simplify the notation, we will drop the longitude and latitudes from the \(\Delta\mu\) and just reference the earthquake and site locations using the subscripts \(e\) and \(s\). For a non-ergodic GMM, the \(P(IM>z|M,R,S)\) term is modified to include the shift in the median for the specific source and site location and the reduced aleatory variability, \(\sigma_{NE}(M,R)\). \[P(IM>z|M,R,S)=1-\Phi\left(\frac{ln(z)-\left[\mu_{erg}(M,R,S)+\Delta\mu_{es} \right]}{\sigma_{NE}(M,R)}\right) \tag{6}\]  
The form of the hazard integral given in Equation (3) can be used to compute the hazard for non-ergodic GMMs, but for areal sources, the calculation is much slower than for an ergodic GMM using Equation (1) because the marginal hazard is computed for each source location rather than being computed only once for each \((M,R)\) combination and using the combined rate of events at the same distance. For an integration step size of 1 km over the zone, this can lead to an increase in calculation time of over a factor of 100 for large zones, which can make the non-ergodic hazard calculations impractical using desktop computers.
# Experimental Investigations of a Tunnel Lining Segment Strengthened by In Situ Spraying Mortar
## 3 Experimental Results
### Properties of Spraying Mortar Materials
#### 3.1.2 Interface-Splitting Tensile Strength  
The experiment results of interface-splitting tensile strength are shown in Table 3.  
Figure 12: Compressive and flexural strength of H-70; (**a**) compressive strength; (**b**) flexural strength.  
Table 3 shows that:  
1. The splitting tensile strength between H-70 and concrete is not affected by the curing condition.
2. The strength of the matrix concrete has a certain influence on the interface-splitting tensile strength. The higher the strength of the matrix, the greater the interface-splitting tensile strength.
3. Surface roughness has a significant effect on the split tensile strength. For the C55 concrete-splitting test block cured under standard curing conditions, the splitting tensile strength of the test block treated with orthogonal brushing is 55% higher than that of the test block treated with one-way brushing. Therefore, when using mortar to strengthen the tunnel lining segment, the interface roughness can be increased by chiseling, thereby improving the bearing capacity of the strengthened structure.
# Recent Web Platforms for Multi-Omics Integration Unlocking Biological Complexity
## 2 Methods
### Platforms for Multi-Omics Visualization and Pathway Exploration
#### 2.2.1. multiSLIDE: A Real-Time Visualization Tool for Transcriptomics and Proteomics Pathways  
By integrating transcriptomics and proteomics data, multiSLIDE is designed for pathway-level analysis, with heatmap visualizations that reveal inter-omics relationships [18]. For example, its clustering tools have been used to correlate gene expression with proteomic changes in signaling pathways during breast cancer progression. However, its limitation to transcriptomics and proteomics may reduce its utility for more comprehensive omics integration. The visualization of heatmaps and clustered data facilitates the analysis of how gene expression changes correspond to protein abundance across different pathways. Real-time manipulation of data enables filtering, reordering, and clustering of omics-related information, allowing for the discovery of patterns and interactions within biological pathways.  
Clustering algorithms and heatmaps are leveraged to group molecular features based on expression or abundance patterns, aiding researchers in identifying meaningful molecular relationships across various omics layers. The real-time interaction and manipulation of data enhance user accessibility, even for those without advanced bioinformatics expertise. The platform's strength lies in enabling real-time visualization and interaction, making it a highly accessible option for researchers focused on transcriptomics and proteomics within a pathway context. However, its limitation to two omics types may reduce its usefulness for researchers aiming to integrate additional layers, such as epigenomics or metabolomics.
# Secure Data of Industrial Internet of Things in a Cement Factory Based on a Blockchain Technology
## 2 Related Works
In addition, we exploit the features of the blockchain to secure data at the device level. Using the proposed lightweight nodes (STM32F427), we achieve real-time encryption and data collection, processing, and storage in a blockchain network, and through the use of a private blockchain/smart contract, our architecture provides resistance to many common IoT attacks such as anonymity [28], tampering, impersonation, replay, repudiation, MIM, Sybil, 51%, Double spending, and DoS attacks [14; 15]. The elliptic curve digital signature algorithm (ECDSA) is adopted due to its low complexity and low storage requirements [29]. In comparison to existing schemes, our experimental results show that we have created an efficient, secure architecture for the cement industry that satisfies the resource constraints by achieving the lowest execution time with the lowest energy consumption by utilizing ECDSA on an ARM Cortex-M4, as discussed in Section 5. Instead of PoW, we use a PoAh consensus mechanism to generate a highly scalable, fast, and energy-efficient scheme [30; 31]. In our scheme, we successfully realized the integration of blockchain technology with IoT devices in the industrial sector at the device level, which is considered to be a challenging point in many research papers. The IoT devices classified as resource constraints (sensors, actuators) that founded new requirements, such as lightweight security scheme, lightweight algorithms, and low power consumption [20]. Our architecture satisfies these new requirements by building a lightweight security scheme, designed by utilizing a low-power ARM Cortex-M4 processor. Moreover, it adopts fast, highly scalable, low energy proof of authentication (PoAh) as a consensus mechanism that helps to enhance computational performance. In addition, our architecture brings many benefits and features to the internet of things network in an industrial sector, concerning increased overall system security, stability, offer information traceability, scalability, secure remote access to valuable data, transparency, and tamper-resistance (by using smart contract), and provides a real-time status monitoring system. The data collected from IoT sensors and actuators that could not be changed by any network participants (immutable) after recording them in a blockchain network, can also be used to predict future production capacity, maintenance, and many other factors, with excellent data management. In addition, this integration enables fast reactions to the newly received data, which helps enhance the quality and reduce the wastage of raw materials.
## 2 Atmospheric Laser Communication Link Model
The signal-to-noise ratio (SNR) of the received signal under the influence of aero-optics can be determined as follows [22]:  
\[R_{SNR-\mathrm{aero}}=\frac{(RP_{\mathrm{Rx}})^{2}}{2\sigma_{n}^{2}}=\frac{[R \times U(h,v,x,y)]^{2}{D_{\mathrm{R}}}^{2}}{2\sigma_{n}^{2}}. \tag{8}\]  
Expanding on this, taking into account the impact of atmospheric turbulence, \(\gamma_{\mathrm{4 tm}}\) denotes the channel parameter affected by atmospheric turbulence, representing its attenuating effect on the optical signal. The gamma-gamma model is used to characterize the distribution of the optical signals [23], as follows:  
\[f(\gamma_{\mathrm{4 tm}})=\frac{2(\alpha\beta)^{\frac{(\alpha+\beta)}{2}}}{ \Gamma(\alpha)\Gamma(\beta)}\gamma_{\mathrm{4 tm}[^{\frac{(\alpha+\beta)}{2}} ]-1}K_{\alpha-\beta}(2\sqrt{\alpha\beta\gamma_{4 tm}}), \tag{9}\]  
In Equation (9), \(Kn(\cdot)\) denotes the second-type modified Bessel function, \(\Gamma(\cdot)\) represents the gamma function, and \(\alpha\) and \(\beta\), respectively, stand for the large-scale and small-scale eddy parameters. Consequently, the bit error rate (BER) at the receiver can be formulated as follows [24]:  
\[R_{BER}=\frac{1}{2}\int\limits_{0}^{\infty}f(\gamma_{\mathrm{atm}})erfc(\frac{ R_{SNR-\mathrm{aero}}\times\gamma_{atm}}{2})d(\gamma_{atm}), \tag{10}\]  
Substituting Equations (8) and (9) into Equation (10), the expression for the BER is obtained, as follows:  
\[K_{\rm GD}(\lambda)=2.23\times 10^{-4}(1+7.52\times\frac{10^{-3}}{\lambda^{2}}). \tag{2}\]0  
As discussed above, the motion characteristics of the aircraft and the flight environment have implications for the distribution of the optical field, \(U(h,v,x,y)\), and the atmospheric turbulence effect influences the channel parameters. Equation (11) provides a means to analyze the performance of the airborne laser communication system across different altitudes and velocities.
###### Abstract  
Safety is a major concern for autonomous vehicle driving. The autonomous vehicles relying solely on ego-vehicle sensors have limitations in dealing with collisions. The risk can be reduced by communicating with other vehicles sharing sensed information. In this paper, we study a real-time multisource data fusion scheme based on Dempster-Shafer theory of evidence (DS) through cooperative vehicle-to-vehicle (V2V) communications. The classical DS can produce erroneous outputs when confidences are highly conflicting. In particular, a false negative error on object detection may lead to serious accidents. We propose a novel metric to measure distance between confidences on the collected data taking the asymmetry in the importance of sensed events into account. Based on the proposed distance, we consider the weighted DS combination in order to fuse multiple confidences. The proposed scheme is simple, and enables the vehicles to combine confidences in real-time. From real-world experiments, we show that it is feasible to lower the false negative rate by the proposed data fusion scheme, and to avoid accidents using the proposed collision warning system.  
autonomous vehicle; Dempster-Shafer evidence theory; data fusion; cooperative V2V communication; collision warning +
Footnote †: journal: Applied Sciences
# CS-FL: Cross-Zone Secure Federated Learning with Blockchain and a Credibility Mechanism
## 3 System Model
### Threat Model  
As the number of ENs increases, the FL system becomes increasingly vulnerable to threats originating from ENs. To simulate the most representative threats in EC scenarios, which specifically involve malicious attacks from the edge layer within Zone-Chain, the threat models are categorized into three primary types. These types are classified based on three dimensions: the quality of node data or models, the quality of node communication resources, and the presence of malicious behaviors exhibited by nodes.  
* Local poisoning model: Attackers from the edge layer of Zone-Chain send false or modified parameters to the server, either individually or in collusion, to sabotage the accuracy of FL. Data tampering attacks and model poisoning attacks are two of the most common local poisoning models. Since these attacks originate from edge side, Zone-Chain is required to incorporate mechanisms for detecting anomalous updates from ENs. By introducing the probe mechanisms and selection algorithms, Zone-Chain ensures that only the most reliable ENs contribute to the FL training process. In addition, Zone-Chain integrates a blockchain ledger-based security indexes mechanism, where the data quality and model contributions of each EN are immutably recorded as metric values. This enables the identification and prevention of recurring local malicious activities through the evaluation of these metrics, thus minimizing the impact of poisoned updates (as discussed in Section 4).
* Communication delay model: Wireless channels between ENs and ZSs are subject to both objective real-time fluctuations and subjective malicious behaviors. Attackers may deliberately increase upload latency or monopolize communication resources,  
Figure 2: Designed structure of blocks in Zone-Chain.  
which is often overlooked in related research. To counter these threats, Zone-Chain must ensure the stability of cross-node communication through robust monitoring of EN states and resource allocation. The network should also prioritize adaptive node selection algorithms that dynamically exclude nodes causing significant delays.
* Malicious behaviors model: The introduction of Zone-Chain to the FL framework exposes it to common blockchain-related threats, such as identity forgery attacks and collusion attacks. These behaviors can compromise the overall trust and privacy within the network. To address these challenges, Zone-Chain needs to incorporate secure identity verification mechanisms and for ENs. This ensures the authenticity and reliability of participating nodes while enabling the early detection of malicious activities that could disrupt the network's operations.