esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Analysis about the Life Cycle Assessment of Carbon Supply Chains
### Life Cycle Assessment of Carbon Capture and Utilization Supply Chains
[34], where carbon dioxide is captured from flue gas and converted into solid carbonates or sand, which can then be used for purposes such as land reclamation in Singapore. The results show that the carbon dioxide mineralization technology abates 115.78 kgCO2-eq per ton of CO2 in input. The results also indicate that the major sources of emissions are from the land and sea transportation of serpentine mineral feedstock, the thermal activation of the feedstock, and carbon capture processes. However, despite the use of fossil fuel-based energy for the transportation of serpentine and ammonia, and the generation of electricity consumed by the carbon dioxide mineralization processes, the technology still has a net positive carbon abatement. In addition to these works, Cuellar-Franca and Azapagic [35] review different utilization systems already analyzed. The life cycle assessment analysis is developed for carbon capture and utilization supply chains, where carbon dioxide is used to produce diesel from microalgae [36; 37; 38; 39; 40; 41; 42; 43; 44], to produce dimethyl carbonate [45] or formic acid [46] and it is used for mineral carbonation [47; 48; 49; 30]. However, the authors show that global warming potential values are lower for a carbon capture and storage supply chain, characterized on average by 276 kgCO2 eq/tonCO2 removed, even if they could have higher values for other categories of impact. This consideration about the better performance of a CCS in terms of carbon dioxide reduction is also reported in [PERSON] et al. [46]. However, compared to CCS systems, CCU has a better economic potential and lower fossil consumption. Worse conditions for a CCU supply chain are on the other hand suggested by [PERSON] et al. [42]. where carbon dioxide is used to produce diesel from microalgae: the global warming potential is in fact higher compared to the conventional petroleum-based route (2.9 kgCO2 eq/1 MJ of combusted fuel compared to the petroleum diesel with 0.12 kgCO2 eq/1 MJ of combusted fuel). However, the environmental analysis is conducted considering a very low algal productivity (3 g/m\({}^{2}\)/day), while a much higher productivity (20-30 g/m\({}^{2}\)/day) is reported in other sources. From the above analyses, it can be seen that CCU supply chains could reduce not only carbon dioxide emissions but could ensure the reduction in other impact categories. However, the good scale of the production plant should be considered.
## 2 Theoretical Supports
### Uncertainty and PDF of Earthquake Magnitude  
After identifying the earthquake source and characterizing the corresponding source zone, it is necessary to turn towards the expected sizes (i.e., magnitudes) of the earthquake that the source zone can be expected to produce. Each source produces different sizes of an earthquake up to the maximum earthquake and smaller earthquakes occur more frequently than larger ones. This uncertainty can be solved by the distribution of the earthquake sizes in a given period of time with a well-established recurrence law. Usually, the probability density function for a given size of an earthquake followed the Gutenberg and Richter [38] recurrence law, which is expressed as:  
\[\lambda_{m}=10^{-8m}\exp(\alpha-\beta m) \tag{3}\]  
where \(\lambda_{m}\) indicates the annual rate of exceedance of magnitude \(m\), \(a\), and \(b\); values are known as the Gutenberg-Richter recurrence parameters, \(\beta=2.303b\) and \(\alpha=2.303a\). If the earthquakes are greater than the threshold magnitude \(m_{min}\), the mean annual rate of exceedance can be explained [39] as follows:  
\[\lambda_{m}=v\exp[-\beta(m-m_{min})]\hskip 28.452756 ptm>m_{min} \tag{4}\]  
where \(v=\exp(\alpha-\beta m_{min})\).  
According to the Youngs and Coppersmith [10], the CDF and PDF for Gutenberg and Richter [38] law with the possible maximum (\(m_{max}\)) and minimum (\(m_{min}\)) magnitudes can be expressed [37] as:  
\[F_{M_{i}}(m)=P[M<m|m_{min}\leq m\leq m_{max}]=\frac{1-\exp[-\beta(m-m_{min})]}{ 1-\exp[-\beta(m_{max}-m_{min})]} \tag{5}\]  
\[f_{M_{i}}(m)=\frac{\beta\exp[-\beta(m-m_{min})]}{1-\exp[-\beta(m_{max}-m_{min}) ]}\hskip 28.452756 ptfor\hskip 28.452756 ptm_{min}\leq m\leq m_{max} \tag{6}\]  
Therefore, the seismicity parameters \(a\), \(b\), \(m_{min}\), \(m_{max}\) of each source, the model is a pivotal part of probabilistic seismic hazard analysis. [PERSON] and [PERSON] [10] gave an equation for the annual rate of earthquake occurrence for the exponential magnitude distribution model by the following expression:  
\[\lambda_{m}=\frac{\mu a_{r}S(c-d)[1-\exp[-\beta(m_{max}-m_{min})]}{bM_{0}^{max} \exp[-\beta(m_{max}-m_{min})]} \tag{7}\]  
Figure 3: 3D fault source discretization.  
where \(\mu\) is the shear modulus, \(a_{r}\) is the rupture area, \(S\) is the slip rate, \(M_{0}^{max}\) is the seismic moment for \(m_{max}\)[11].  
In the case of only one magnitude value (m), the seismic moment rate (the number of earthquakes of magnitude equal to m in one year) can be expressed as:  
\[\lambda_{m}=\frac{M_{0}}{10^{1.5m+16.05}} \tag{8}\]  
\[M_{0}=\mu As \tag{9}\]  
where shear modulus, \(\mu=30\times 10^{11}\left(\text{dyne/cm}^{2}\right)\), \(A\) = source area (km\({}^{2}\)) and \(s\) = average slip rate on the fault (cm/year).
## 6 Fault Detection and Classification Case Studies
### Variable Fault Impedance  
The effects of the changing fault impedance on the proposed technique are investigated by utilizing all categories of faults with various impedances involved in the fault events, which include 0 \(\Omega\), 5 \(\Omega\), 10 \(\Omega\), 15 \(\Omega\), 20 \(\Omega\), and 25 \(\Omega\). The fault distance for all types of faults is considered to be 30 km from the sending end terminal. The incidence angle of the fault is maintained at zero for all kinds of faults at the time of the varying fault impedance. The FIs for all kinds of faults with different impedances are presented in Figure 9.  
Figure 8: Effects of varying the fault location for detecting and classifying (**a**) L-G, (**b**) LL, (**c**) LL-G, and (**d**) LLL-G faultsFrom Figure 9a, it is clear that the peak magnitude of the FI associated with Phase A is greater relative to the pre-set threshold. The FI associated with Phases B and C is lower relative to the pre-set threshold at the moment of the L-G fault on Phase A for the different quantities of the impedance. It is revealed in Figure 9b that the peak of the FI related to Phases A and B is greater compared to the pre-set threshold. This FI pertaining to Phase C is lower relative to the pre-set threshold at the moment of fault of an LL nature with different quantities of the impedance. In Figure 9c, it is clear that the peak quantities of the FI related to Phases A and B are greater relative to the threshold value, whereas the FI related to Phase C is lower relative to the pre-set threshold at the time of fault of an LL-G nature for all quantities of the impedance. Figure 9d indicates that the peak quantities of the FI related to all phases is greater compared to the threshold quantity at the moment of fault with an LLL-G nature with different values of the fault impedance.
# Improved Localization and Recognition of Handwritten Digits on MNIST Dataset with ConvGRU
###### Abstract  
Video location prediction for handwritten digits presents unique challenges in computer vision due to the complex spatiotemporal dependencies and the need to maintain digit legibility across predicted frames, while existing deep learning-based video prediction models have shown promise, they often struggle with preserving local details and typically achieve clear predictions for only a limited number of frames. In this paper, we present a novel video location prediction model based on Convolutional Gated Recurrent Units (ConvGRU) that specifically addresses these challenges in the context of handwritten digit sequences. Our approach introduces three key innovations. Firstly, we introduce a specialized decoupling model using modified Generative Adversarial Networks (GANs) that effectively separates background and foreground information, significantly improving prediction accuracy. Secondly, we introduce an enhanced ConvGRU architecture that replaces traditional linear operations with convolutional operations in the gating mechanism, substantially reducing spatiotemporal information loss. Finally, we introduce an optimized parameter-tuning strategy that ensures continuous feature transmission while maintaining computational efficiency. Extensive experiments on both the MNIST dataset and custom mobile datasets demonstrate the effectiveness of our approach. Our model achieves a structural similarity index of 0.913 between predicted and actual sequences, surpassing current state-of-the-art methods by 1.2%. Furthermore, we demonstrate superior performance in long-term prediction stability, with consistent accuracy maintained across extended sequences. Notably, our model reduces training time by 9.5% compared to existing approaches while maintaining higher prediction accuracy. These results establish new benchmarks for handwritten digit video prediction and provide practical solutions for real-world applications in digital education, document processing, and real-time handwriting recognition systems.  
Keywords:handwritten digits; ConvGRU; video location prediction; GAN; action prediction
# Leveraging IPv6 and ICMPv6 for Delay-Tolerant Networking in Deep Space
## 3 Architecture Proposal
### Custom ICMPv6 Messages
#### 3.2.1 Type and Code Messages  
To maintain a clear distinction between the standard signaling of ICMPv6 and DTN signaling, this work proposes the introduction of four new ICMPv6 message types:  
* DTN-PCK-RECEIVED: Indicates that a packet has been successfully received by a node. Sent in the reverse direction, from the receiving node back to the sender or an intermediate node.
* DTN-PCK-FORWARDED: Confirms that a packet has been forwarded to the next node in the network. Sent by the forwarding node to the sender or an intermediate node as a status update.
* DTN-PCK-DELIVERED: Signals that a packet has reached its final destination. Sent from the destination node to the original sender or an intermediate node as a delivery confirmation.
* DTN-PCK-DELETED: Reports that a packet has been deleted due to specific conditions (e.g., lifetime expiration or storage constraints). Sent from the node deleting the packet to the designated reporting node.  
These message types utilize the code field to convey detailed status information. Although a comprehensive list of codes cannot be finalized at this stage, the BPv7 codes defined in the IANA subregistry can be used as a reference. A standardized pairing conforming to the requirements of ICMPv6 should be investigated through implementation and testing. There exists a standardized process. First, it is necessary to present an IETF draft, and when approved and published as an RFC, the IANA will assign the requested ICMPv6 types/codes, and update the ICMPv6 registry with the new entry. The impact on real-world environment is low since in any case, specific protocols must be deployed for DTN. The advantage is the complete compatibility with IP nodes. Table 2 list the BPv7 status report Reason Codes and Table 3 the custom ICMPv6 types and codes.
## 4 Results and Discussion  
### Mix Design Properties  
Table 5 represents the mix design properties of the CB, CRMB, and PMB mixtures.  
The bulk density (Gmb) values of the CB, CRMB, and PMB mixtures were found to be 2550, 2380, and 2410 kg/m\({}^{3}\), respectively. The results clearly indicate that a lower density was observed for the CRMB and PMB mixtures compared to the CB mixtures. For instance, a reduction in density of 6.7% and 5.5% was noticed with the CRMB and PMB mixtures with respect to the CB mixture. According to the specifications of SP-02 (Asphalt Institute, 2001), at 4 \(\pm\) 0.1% VTM, the optimum asphalt contents (OACs) of the CB, CRMB, and PMB mixtures were found to be 5.5%, 6.5%, and 6.0%, respectively, indicating that the addition of modifiers requires a higher asphalt content to achieve the target VTM.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Mix** & **ACdesign (\%)** & **VTM (\%)** & **VMA (\%)** & **VFA (\%)** & **Gmb (kg/m\({}^{3}\))** \\ \hline CB & 5.5 & 3.9 & 14.1 & 71.5 & 2550 \\ CRMB & 6.5 & 4.1 & 17.1 & 77.5 & 2380 \\ PMB & 6 & 4 & 16.5 & 75.8 & 2410 \\ Requirement a & - & 4.0\(\pm\)0.1 & \(\geq\)13.0 & 65-78 & - \\ \hline \hline \end{tabular}  
* SP-02.  
\end{table}
Table 5: Results of the mix design properties.  
Similar findings were noticed by [PERSON] et al. [14] and [PERSON] and [PERSON] [15], with the use of polymers and recycled crumb rubber in HMA mixtures, indicating that the OAC for the asphalt mixtures with polymers and recycled crumb rubber is approximately 0.5-1% higher than for the control mixtures. The specifications of SP-02 (Asphalt Institute, 2001) suggest that the Voids in Mineral Aggregate (VMA) values should be a minimum of 13% and the Voids Filled with Asphalt (VFA) values should be in the range of 65-78%. The results clearly indicate that the CB, CRMB, and PMB mixtures had VMA values greater than 13%, and the VFA values were within the limits. Further, it can be noticed that the addition of modifiers resulted in higher VMA and VFA values.
## 5 Mitigation of Pharmaceutical Pollutants by Nanomaterials
### Biochar-Based Adsorbents
#### 5.1.2 Chitosan-Based Nanoparticles  
Chitosan nanoparticles, which are highly degradable and durable biopolymeric nanoparticles, are another commonly utilized adsorbent [105]. Chitosan nanoparticles are recognized by their functional groups, such as hydroxyl and amino, which are essential  
Figure 4: SEM images of (**a**) GO, (**b**) MWCNTs, (**c**) 0.5-rGO-MWCNTs, (**d**) 1.0-rGO-MWCNTs, (**e**) 1.0-\(\beta\)-CD/rGO-MWCNTs-1, (**f**) 1.0-\(\beta\)-CD/rGO-MWCNTs-6, (**g**) 1.0-\(\beta\)-CD/rGO-MWCNTs-12, and (**h**) 1.5-rGO-MWCNTs adapted with permission from [99].  
Figure 5: SEM analysis images of P-BC (**a,b**) before adsorption and (**c,d**) after adsorption adapted with permission from [104].  
for adsorption efficacy. These particles also have a large surface area and controllable porosity [105]. They have been widely employed for adsorption of carbamazepine, ketotifen fumarate, and other drugs [30]. Furthermore, magnetic chitosan nanoparticles have recently been used to remove pharmaceuticals such as ibuprofen, tetracycline [106], diclofenac, clofibric acid, and carbamazepine [107]. The magnetic chitosan had effective sorption affinity for diclofenac and clofibric, but no sorption of carbamazepine was observed. The sorption capacities of clofibric and diclofenac in the individual solutions were 191.2 and 57.5 mg/g, respectively. Sorption kinetics revealed a rapid equilibrium within 2 min. Lower pH values in the solution were discovered to be beneficial to the adsorption process. Clofibric sorption efficacy decreased significantly as inorganic salt concentration increased. However, the sorption performance of diclofenac remained stable under varying ionic strength conditions. As the SEM photo in Figure 6a illustrated, magnetic chitosan cannot be defined as a porous material. Specific surface area as low as 0.5 m\({}^{2}\)/g, measured through the BET-N\({}_{2}\) test, also supported the point. Microphotography of TEM in Figure 6b shows how Fe\({}_{3}\)O\({}_{4}\) and polymerized chitosan joined and their size distribution. Light- colored beads were polymerized chitosan, while dark dots embedded inside are Fe\({}_{3}\)O\({}_{4}\) particles. Magnetic chitosan was discovered to have a high sorption affinity for pharmaceuticals with carboxyl group(s). Adsorption was pH dependent, and sorption capacity decreased with increasing solution pH or ionic strength by affecting the form of existence of target compounds in solution.
## 3 Petri Net Representation
### Red Truncation Strategy  
The conventional red truncation strategy also begins with an initial fixed red time; each time a transit vehicle is detected, a fixed period of non-transit phase is truncated from the scheduled time until the minimal red time is reached. In the proposed red truncation strategy, a fixed red time will be truncated from the scheduled red time, if and only if the transit vehicle is detected by the RSU sensor at the specific red light period. The difference from the conventional red truncation strategy is whether a red truncation operation is triggered depends on the remaining red time when the transit vehicle arrives at the stop bar.  
The proposed new red truncation strategy is the following:  
1. The residual red time when a transit vehicle is detected is expressed as \(T_{r\_gt}\). The threshold of red time to be truncated is set to \(T_{\delta\_gt}\);2. The three parameters \(D_{RSU}\), \(V_{tv}\), \(T_{e}\) have the same meaning as in Section 3.3;
3. When a TSP request is received by the controller in the current phase, it checks if the detected transit vehicle needs to stop at least once. The more detailed information is summarized into four cases as follows.  
1. If the condition \((T_{r,rt}\leq T_{e})\) is satisfied, it means that the traffic signal will turn green before the transit vehicle travels to the stop bar.
2. If the condition \((T_{r,rt}>T_{e})\) holds, it means that the detected transit vehicle need to stop at least once.
3. For Case 2, if the formula \((T_{e}\geq T_{r,rt}-T_{\delta,rt})\) is also satisfied, then a fixed period of red time \((T_{\delta,rt})\) can be truncated from the current phase. Then, the new remaining red time \((T_{r,rt}-T_{\delta,rt})\) is guaranteed such that this transit vehicle can travel through the stop bar without any stop.
4. If the condition \((T_{r,rt}-T_{e}>T_{\delta,rt})\) holds, it means that the residual red time is quite long, beyond the adjustment range. Hence, the transit vehicle has to wait before the stop bar until the red light turns green.
## 6 Discussion  
### Methodology Effectiveness and Alignment (RQ1)  
The dual methodology approach, employing DBTL for development-oriented projects and reflexive DSR for research-oriented projects, has proven effective in facilitating rapid prototyping, structured reflection, and continuous feedback integration. The DBTL cycle's adaptability has allowed for iterative improvements across each stage, directly addressing RQ1 by establishing a scalable framework that meets both consulting and capstone goals.  
In research-oriented projects, reflexive DSR enabled systematic reflection on exploratory insights, helping researchers evaluate preliminary findings. This structured reflection will be critical in future studies, where it will allow insights to guide subsequent research projects focused on specific components of each development stage. As shown in Tables 1 and 2, the selected methodologies align well with the project goals, yet further studies may incorporate additional methodologies to extend this framework.
# Hybrid FSK-FDM Scheme for Data Rate Enhancement in Dual-Function Radar and Communication
## 5 Simulation Results
### Example 2: Multiple Communication Receiver  
At the communication receiver, we have \(N_{R}=10\) antenna elements with same arrangements as used for radar receiver. In the 2 nd case we only consider one radar target which lies in the main beam fixed at \(\theta_{r}=0^{\circ}\), while four communication receivers are located in the side lobes at \(\theta_{c1}=30^{\circ}\), \(\theta_{c2}=50^{\circ}\), \(\theta_{c3}=-30^{\circ}\) and \(\theta
## 4 Results and Discussion
### Mineralogical Features
_Allantite-_(Ce) (Ca; Ce)\({}_{2}\)(Al; Fe\({}^{2+}\); Fe\({}^{3+}\))\({}_{3}\)(SiO\({}_{4}\))(Si\({}_{2}\)O\({}_{7}\))O(OH) is one of the main light rare earth minerals (LREE) and occurs as zoned euhedral to subhedral tabular prismatic crystals surrounded by zircon and sometimes enclosed zircon grains (Figure 4). It is usually intimately associated with biotite. The EDS data show that allanite is composed essentially of Ce ranging from 21.62 to 33.32 wt%, La 9.35 to 18.73 wt%, Nd 6.76 to 8.29 wt%, and Pr 1.3 to 2.4 wt%, while Y ranges from 4.03 to 6.47 wt%. Si ranges from 10.15 to 23.23 wt%, and Al is between 3.03 and 13.04 wt%. It is mostly coated by iron oxides, and hence Fe ranges from 3.31 to 7.41 wt%. Other elements occur as minor constituents, such as Ca, K, Na, Ti, S, Sm, Eu, Gd, and Nb (Table 1). So, allanite is enriched in both Ce and Fe designating ferriallanite-(Ce) in its chemical composition. It is clarified that the studied area is important for extracting the radioactive material from the surrounding granites in the Wadi Rod Elsayalla area [11]. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Elements** & **A1** & **A2** & **A3** & **A4** & **A5** & **A6** \\ \hline Si & 16.79 & 23.23 & 12.94 & 18.27 & 14.55 & 10.15 \\ Al & 4.64 & 13.04 & 6.12 & 9.39 & 4.95 & 3.03 \\ Ca & 3.32 & 3.2 & 6.5 & 3.25 & 3.24 & 3.98 \\ Fe\({}^{3+}\) & 3.67 & 7.41 & 3.27 & 5.73 & 4.06 & 3.31 \\ K & 0.81 & 2.1 & 1.41 & 3.01 & 1.14 & 0.98 \\ Na & n.d. & n.d. & n.d. & n.d. & 1.99 & 1.79 \\ Ti & n.d. & n.d. & n.d. & n.d. & 11.7 & 14.6 \\ Ce & 32.16 & 21.62 & 33.32 & 26.09 & 22.6 & 24.61 \\ La & 18.73 & 9.35 & 18.67 & 15.28 & 12.91 & 12.62 \\ Nd & 8.29 & 7.41 & 7.06 & 6.76 & 7.66 & 7.23 \\ Pr & 2.28 & 2.51 & 2.26 & 2.25 & 1.30 & 2.40 \\ Y & 4.20 & 4.03 & 4.69 & 6.47 & 4.99 & 5.0 \\ Th & 2.18 & 2.78 & 0.64 & 1.67 & 2.51 & 2.52 \\ U & 0.81 & 1.09 & 0.46 & 0.86 & 1.16 & 1.13 \\ S & 0.34 & 0.29 & 0.39 & 0.48 & n.d. & n.d. \\ Sm & 0.24 & 0.51 & n.d. & n.d. & 1.52 & 1.36 \\ Eu & 0.26 & 0.31 & 0.56 & n.d. & 0.27 & 0.32 \\ Gd & 1.27 & 1.12 & 1.71 & 0.51 & 0.82 & 1.17 \\ Nb & n.d. & n.d. & n.d. & n.d. & 2.63 & 3.8 \\ Total & & & & 100 & & \\ \hline \end{tabular}
\end{table} 
Table 1: The semiquantitative (elements, wt%) results of EDS analyses for allanite crystals from quartz sysenite, Wadi Rod Elsayalla, Southeastern Desert, Egypt. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Elements** & **Zr1** & **Zr2** & **Zr3** & **Zr4** & **Zr5** \\ \hline Si & 28.04 & 24.72 & 32.75 & 35.23 & 24.93 \\ Al & 3.04 & 1.37 & 3.49 & 1.92 & 1.94 \\ K & 2.04 & 0.69 & 1.65 & 1.17 & n.d. \\ Ca & 0.66 & 1.18 & 1.23 & 0.29 & 1.34 \\ Fe\({}^{3+}\) & 6.71 & 9.57 & 4.55 & 4.54 & 1.97 \\ Zr &