esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
E21 has good overall properties, being one of the only few magnesium-based materials to meet the FAA's survivability requirements; good chemical resistance due to the tight control of Zn content; good mechanical, physical, and ignition properties due to the addition of Nd, Gd, and Zr. In this investigation, the effects of B4C in E21 alloy were studied with the goal of further improving the properties of E21 alloy to be used in a wider range of applications in the aerospace sector. From the results and analysis, the following conclusions can be drawn:  
Figure 6: (**a**) Thermo-gravimetric analysis of materials to determine their ignition temperatures; and (**b**) ignition temperature vs. effective thermal conductivity of the materials following a polynomial fit. It is to be noted that the effective thermal conductivity is calculated using the rule of mixtures.  
1. The presence of B4C nanoparticles in E21 alloy did not significantly increase the density of nanocomposites but reduced the grain sizes of E21 alloy by 29%.
2. The mechanical strength (i.e., compressive yield strength and ultimate strength of the nanocomposites, particularly E21-2.5B4C) increased significantly without compromising on ductility.
3. The damping capacity of E21-2.5B4C is the highest, with the fastest time taken to stop the vibration. This makes E21-2.5B4C a promising material to be used in the aerospace sector without the need for any special energy absorbers.
4. The addition of B4C nanoparticles in E21 alloys also led to the promising behavior of E21 alloy in terms of their ignition response. E21-2.5B4C demonstrated the highest ignition temperature of 798 \({}^{\circ}\)C, which is about 57 \({}^{\circ}\)C higher than the E21 alloy, showcasing the positive role of boron carbide nanoparticles.
5. Thus, E21-2.5B4C nanocomposites with boron carbide nanoparticles area desirable and suitable material for the aerospace industry to be used in in-cabin applications, as well as for gearbox housing applications.  
Conceptualization and design of experiments, S.T. and M.G.; performed the experiments and analyzed the data, S.T. and N.J.Y.; material selection and ideation, S.T., M.H. and M.G.; writing--original draft preparation, S.T.; writing--review and editing, S.T. and M.G.; project administration, S.T.; funding acquisition, M.G.  
This research was funded by Singapore Ministry of Education (MOE) Tier 2, grant number R265-000-622-112.  
The authors thank [PERSON] for his assistance with extrusion and [PERSON] for his assistance with experiments on TGA.  
The authors declare 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.
# Designing a C-ITS Communication Infrastructure for Traffic Signal Priority of Public Transport
## 3 Methodology and Results  
### Communication Range  
#### 3.1.1 Facts and Background Information  
Due to the different ranges of frequency and the resulting differential of physical properties, communication via C-ITS G5 (approx. 5.9 GHz) [18; 19; 20] has lower communication ranges compared with analog radio in the 2 m bandwidth [21]. As a result, the first question that needs to be answered when transitioning from the status quo to C-ITS is the determination of which communication range is necessary to send in the form of minimum requirements, as all registration points are defined by C-ITS today.  
In general, it must be ensured that the vehicles are able to send the corresponding registration points to the traffic light control module. To comprehensively answer the question of the necessary and available ranges, radio propagation planning needed to take place on the one hand, and radio measurements were needed on the other hand; these were both carried out at all nodes along the U5 area of study.  
For radio propagation planning via C-ITS G5, a methodology based on the research results of [22] was used. With the help of a newly developed communication planning tool, the radio propagation was estimated for all traffic light-controlled intersections in the U5 area of study outside the tunnel. Next, a check was performed to determine whether all registration points that were being used for public transport prioritization within the network and that would be replaced by linear detection in the future [6] were also in the communication range of the new C-ITS G5 radio technology. This ensures that public transport prioritization will be able to work in the future. As a result of the linear detection of the public transport vehicles and the transition from the registration point method to linear influence using the estimated time of arrival object, the combination of the C-ITS services TSP and GLOSA will result in highly noticeable improvements for public transport prioritization and operational stability.
# Metal Recovery from Wastes: A Review of Recent Advances in the Use of Bioelectrochemical Systems
## 3 BES Coupling with Other Technologies for Metal Recovery
### MFC-MEC Coupling System  
Metal recovery from wastewaters containing ions with varying redox potentials benefits significantly from the coupling of MFCs with MECs. Depending on its origin, some metal-polluted wastewaters contain metal ions with high redox potentials, such as Cr6+, V5+, Cu2+, or precious metals (i.e., Ag+, Au3+, and platinum group metals), which can be readily reduced at the cathode of an MFC, producing energy. Conversely, metals like Zn2+, Pb2+, or Ni2+, which have low redox potentials, require external energy input for reduction, which is achieved by operating under MEC mode.  
The coupling of MFC and MEC systems offers a synergistic approach where the electricity generated by the MFC can partially or fully power the MEC, enabling the reduction of low-redox potential metals. This dual configuration facilitates the simultaneous recovery of metals with both high and low redox potentials, optimizing energy efficiency while addressing the complexities of treating wastewater containing diverse metal species [100; 101]. Moreover, stacking multiple MFCs--via series and parallel configurations, as depicted in Figure 4a--can significantly enhance the power output, providing sufficient energy to drive MEC reactions at rates that are both efficient and practical for metal recovery applications.
## 3 Blockchain Technologies for Maritime Applications/Use Cases
### Blockchain for Ports: Rotterdam Port  
The first-generation blockchain can only record simple banking transactions. However, second-generation blockchains like Ethereum can run smart contracts and decentralized applications (dApps) and endlessly apply changes to the supply chain business. Blockchain enhances the efficiency of logistics transactions by allowing transparent and immediate tracking of the location and status of goods from origin to destination. Various aspects of the supply chain currently employ blockchain technology. Everledger certifies luxury goods like diamonds and wine by tracking their origins via blockchain. Walmart's food supply chain and UPS's logistics network chain also use blockchain.  
The Port of Rotterdam, one of the world's largest ports, is a smart port that runs on multiple IT technologies. The Port of Rotterdam achieves real-time management with the Naviporta blockchain platform in the supply chain. The blockchain uses immutability to record items being transferred, but in a way, the quick moves of warehousing goods allow real-time tracking of status and location. In addition, processing times are greatly reduced by using digital copies in place of paper files, resulting in substantial cost savings.  
Maritime transport between Korea and the Netherlands was used to test blockchain technology by the Port of Rotterdam in cooperation with ABN Amo Bank Samsung SDS. Thanks to Samsung SDS's supply chain technology platform, Nexledger, they were able to monitor the movement and status of cargo in real time. Moreover, tracking the location and status of smart containers makes container management run smoothly [72].
# Performance Analysis of FSO-UWOC Mixed Dual-Hop Relay System with Decode-and-Forward Protocol
## 5 Simulation Results and Analysis
### Average BER Simulation Results and Analysis
The atmospheric transmission distance is set to two different values, 200 and 500 m, while the underwater transmission distance is fixed at 20 m. Since the reduction in the transmission distance of the FSO link can lead to a reduction in the loss of this link path, it is not difficult to see from the simulation results in the figure that, under the same conditions of the system parameter settings, the reduction in the transmission distance of the FSO link can effectively improve the average BER performance of the whole system. Figure 6b shows the average BER curves as a function of the average SNR, with the atmospheric transmission distance fixed at 200 m and the underwater transmission distance varied at 20 and 30 m. The simulation results mirror the OP trends observed in Figure 4b: in the low-SNR regime, the UWOC link's transmission distance significantly degrades the  
Figure 5: Average BER of mixed FSO-UWOC system with different oceanic turbulence scintillation indices (\(\xi_{1}=1.14\), \(\xi_{2}=4.00\)). Figure 6: Effect of different link transmission distances on the average BER of the FSO-UWOC system: (**a**) varying the FSO link transmission distance \(Z\); (**b**) varying the UWOC link transmission distance \(d\). average BER, while its influence diminishes and becomes asymptotically negligible as the average SNR increases. This behavior can be analyzed using the asymptotic BER formula derived in Equation (43), which identifies the dominant fading parameters governing system performance. Under the current simulation configuration--GG turbulence coefficients \((\alpha,\beta)=(3.99,1.70)\), oceanic turbulence parameter \(ac=3.60\), and pointing error parameters \(\beta_{1}^{2}=1.30\) and \(\xi_{2}=4.00\)--the pointing error of the FSO link \(\xi_{1}^{2}\) emerges as the smallest among all system parameters. In high-SNR regions, this makes the asymptotic average BER exclusively dependent on \(\xi_{1}^{2}\), rendering UWOC link parameters (including transmission distance) irrelevant to performance. The close overlap between theoretical closed-form BER predictions and asymptotic values across different detection schemes in the high-SNR domain further validates the accuracy of the derived expressions.
## 3 Results and Discussion
### Results
#### 3.4.5 Control Channel Utilization Factor  
The possibility of having multiple paths implies an increase in the number of PREQ and PREP packets that must be transmitted. To avoid the saturation of the data channels, we dedicated an exclusive channel to control packets. In order to evaluate the impact caused by these new transmissions, the measure of the control channel utilization factor (\(\rho\)) was also carried out. This parameter can be measured by each mesh station, the possible values being one (busy) or zero (idle). These values were then smoothed using an Exponentially-Weighted Moving Average (EWMA) in order to obtain an estimation of the average value and avoid abrupt oscillations.  
The results are shown in Figure 11, where the channel utilization factor for two cases (farthest node and the data concentrator), for two different values of the \(reactivePathTimeout\) parameter (lifetime of reactive routing information values) and for different network sizes (from 9-36 nodes) are presented. It can be observed how, as the network size was increased, more control packets were transmitted, and so, the \(\rho\) value also increased, for both \(reactivePathTimeout\) values. Besides, it is also shown that the \(\rho\) value did not achieve high values (it was always below 0.4), and therefore, the control channel was not congested.  
Figure 7: \(Cont.\)  
Figure 8: \(Cont\).  
Figure 7: Packet delivery ratio (HWMP vs. MPC-HWMP). **(a)** Traffic Type 1; **(b)** Traffic Type 2; **(c)** Traffic Type 3; **(d)** Traffic Type 4.  
Figure 8: Throughput (HWMP vs. MPC-HWMP). (**a**) Traffic Type 1; (**b**) Traffic Type 2; (**c**) Traffic Type 3; (**d**) Traffic Type 4.  
Figure 9: _Cont._  
Figure 10: Routing table size (HWMP vs. MPC-HWMP).  
Figure 9: Network transit time (HWMP vs. MPC-HWMP). (**a**) Traffic Type 1; (**b**) Traffic Type 2; (**c**) Traffic Type 3; (**d**) Traffic Type 4.
# Models versus Datasets: Reducing Bias through Building a Comprehensive IDS Benchmark
## 2 Literature Review
### Deep Learning Classifiers for Sequential Data  
Researchers have been proposing a vast range of ML and DL solutions for network anomaly detection. This paper focused our experiments on only those DL classifiers that are famous, commonly used, and are known to perform well on sequential data. Section 3 presents our proposed framework with various DL classifiers used in this study. Firstly, we implemented a Multilayer Perceptron (MLP), which is a feed-forward Neural Network and consists of an input, hidden, and an output layer. It has frequently been used for network anomaly detection [51; 52; 53; 54]. Autoencoders (A.E.) are robust unsupervised neural networks. A.E. helps avoid data imbalance and dimensionality reduction and reconstruct errors while detecting anomalies [55]. Researchers have shown great interest in A.E. when solving anomaly detection, fault diagnostics, dimensionality reduction, compression, and other related problems.  
A Recurrent Neural Network (RNN) is a powerful and famous algorithm to find hidden patterns in sequential data [56]. RNN captures temporal dependencies in the data by storing the data received earlier [57]. RNN's are good at processing sequential data; however, they suffer from gradient vanishing problems, are challenging to train [58], and cannot remember longer sequences [59]. Long Short-term Memory (LSTM) is a specialized RNN that can remember information for a longer period using a memory cell. It overcomes RNN's vanishing gradient problem [60]. LSTM's consist of three gates, i.e., input gate, forget gate, and output gate [57]. The gates control access and information flow in the memory cell and prevent stored information from being overwritten with irrelevant information. Bi-directional LSTM (BLSTM) came to solve some of the problems in traditional LSTM,such as that LSTM cannot operate in both positive and negative directions [61]. Similarly, LSTM does not work for tasks requiring explicit and external memory [60]. BLSTM are good in processing sequential data in both time directions using forward and backward LSTM layers [62].  
A Convolutional Neural Network (CNN) is primarily used for image classification and produces high accuracy when performing complex tasks [56]. A basic CNN consists of one or more convolutional, pooling, and fully connected layers. The 1D-CNN has been used in many IDS studies and produces good results in processing sequential data [63]. CNN's main advantage over a feed-forward neural network is that each neuron in CNN only connects to a subset of input; this reduces the total number of network parameters and improves training time and process [64]. Temporal Convolutional Network (TCN) is a feed-forward network and is a variant of CNN that uses causal convolutions. A CNN architecture combined with causal padding makes it a causal convolution [65]. TCN is known to maintain a temporal sequence of data, which helps in preventing information loss. TCN allows better GPU optimization during training [66].
## 6 Application of Plasma Treatment in Advanced and Emerging Packages
### Hybrid Bonding  
Hybrid bonding is an advanced packaging technique that involves the direct bonding of two surfaces, such as Cu-Cu or Si-SiO\({}_{2}\), without the use of solder. The reviewed literature presented the uses of an Ar and N\({}_{2}\) plasma combination to improve the SiO\({}_{2}\) surface for copper to silicon oxide bonding to occur with the best possible yields. In this study, X-ray photoelectron spectroscopy (XPS) analysis of the SiO\({}_{2}\) surface after the Ar/N\({}_{2}\) plasma treatment revealed that the nitrogen plasma reacted with the SiO\({}_{2}\), substituting the oxygen in Si-O bonds with nitrogen [152].  
This same gas combination was also utilized in processes outlined in the other reviewed literature, which outlined the use of a plasma pre-treatment step that is performed specifically to improve the quality of the subsequent area-selective electroless silver deposition onto the copper pads and enhance the final hybrid bonding results. Here. the treatment was performed in two steps. The first step used argon (Ar) plasma. Its purpose was to enhance the surface cleanliness of the copper pads. This implies removing contaminants like oxides or organics. The second step used nitrogen (N\({}_{2}\)) plasma. Its purpose was to improve the hydrophilicity or water-attracting property of the surface. Improved hydrophilicity was found to lead to a more uniform area-selective deposition reaction during the subsequent electroless silver plating [118].
###### Abstract  
The Global Navigation Satellite System (GNSS) time series non-constructive deformation shows significant seasonal variations, and the study of its periodic term components and possible physical mechanisms has important theoretical significance and application value for the accurate use of the data and more in-depth analysis. In this paper, wavelet transform (WT) is used to extract the seasonal terms of 24 GNSS continuous station time series, Gravity Recovery and Climate Experiment (GRACE) displacement time series and precipitation data in the Sichuan-Yunnan area, and the three data sets are compared and analyzed in terms of amplitude, phase and cross-correlation coefficient (CC), and the results show that the seasonal deformation in the area is strongly related to the precipitation variation. GNSS and GRACE have good consistency in the vertical component, and the seasonal variation is mainly related to the hydrological load; the difference in the horizontal component is obvious, and the amplitude of the seasonal term of GNSS is larger than that of GRACE, indicating that the resolution of GRACE in the horizontal component is lower than that of the vertical component, and the overall estimation accuracy is lower than that of GNSS. There are significant seasonal terms of annual and semi-annual cycles for the three GNSS components, and the vertical component mainly shows the seasonal deformation of the annual cycle with the strongest seasonality; the horizontal component mainly shows the deformation of the semi-annual cycle, and the seasonality of the N component is stronger than that of the E component, and they are negatively correlated with a coefficient of \(-\)0.90.  
Global Navigation Satellite System; Gravity Recovery and Climate Experiment; seasonal deformation +
Footnote †: journal: Applied Sciences  
1
Footnote 1: The First Monitoring and Application Center, China Earthquake Administration, Tianjin 300180, China; [EMAIL_ADDRESS]  
2
Footnote 2: The First Monitoring and Application Center, China Earthquake Administration, Tianjin 300180, China; [EMAIL_ADDRESS]  
3
Footnote 3: The First Monitoring and Application Center, China Earthquake Administration, Tianjin 300180, China; [EMAIL_ADDRESS]  
4
Footnote 4: Correspondence: [EMAIL_ADDRESS]
## 2 Study Area and Data
### Phenological In-Situ Data
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Crop Type** & **BBCH Code** & \begin{tabular}{c} **DWD Phase** \\ **Description** \\ \end{tabular} & **DWD Phase ID** & \begin{tabular}{c} **DWD Phase** \\ **Distribution** \\ \end{tabular} & \begin{tabular}{c} **N\({}^{\circ}\) of DWD** \\ **Obs** \\ \end{tabular} & \begin{tabular}{c} **N\({}^{\circ}\) of In-Situ** \\ **BBCH Obs** \\ \end{tabular} \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Winter \\ wheat \\ \end{tabular} } & 10 & emergence & 12 & 12 & 259 & 9 \\  & 31 & shooting & & 206 & 15 \\  & 51 & heading & 18 & 264 & 2 \\  & 75 & milk ripening & 19 & 216 & 17 \\  & 87 & yellow ripening & 21 & 237 & 2 \\  & 99 & harvest & 24 & 286 & 27 \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Corn \\ 99 \\ \end{tabular} } & 10 & emergence & 12 & 12 & 416 & 11 \\  & 31 & stem elongation & 67 & 319 & 9 \\ \cline{1-1}  & 75 & milk ripening & 19 & 274 & 4 \\ \cline{1-1}  & 87 & yellow ripening & 21 & 227 & 2 \\ \cline{1-1}  & 99 & harvest & 24 & 26 & 332 & 6 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Phenological in-situ data collection summary, including crop types, DWD phases, BBCH Codes, and the number of observations for both BBCH and DWD. DWD Phase IDs are color-coded, and the DWD Phase Distribution column shows the distribution with polar graphs colored according to these IDs over three years.
# Implementation of a Cross-Layer Sensing Medium-Access Control Scheme
## 3 Cross-Layer CS-MAC Design  
The proposed cross-layer CS-MAC has two key ideas. First, the destination receives combined requests called \"compressive complex requests\", which utilize physical layer characteristics to accurately identify active nodes and grant their transmission. Second, asynchronous requests/grants MAC can be implemented because the reconstruction process is executed in a complex field of the physical layer where there is no need for bit synchronization or frame synchronization.
# All about Delay-Tolerant Networking (DTN) Contributions to Future Internet
## 2 Concept and Challenges
* Routing and Forwarding: Determining optimal paths for message delivery in scenarios with dynamic network topologies, intermittent connectivity, or limited routing information requires the utilization of adaptive routing protocols and forwarding strategies. * Security and Privacy: Ensuring data confidentiality, integrity, and authenticity across heterogeneous, decentralized, and potentially adversarial environments is crucial, particularly when transmitting sensitive information. * Heterogeneity: Need to support seamless communication among diverse devices and networks with varying capabilities and characteristics, e.g., different protocols or data rates. * Quality of Service (QoS): Due to the inherent constraints of DTN environments such as network disruptions and intermittent connectivity, there is a need for mechanisms to ensure reliable message delivery while meeting specified performance criteria, e.g., throughput, latency, and reliability.