esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Clothing Framework for Health Monitoring Applications
## 4 Challenges and Future Development
### Technical
#### 4.1.2 Lack of Flexibility, Foldability and Comfortability  
The sizing of wearable products is important for smart clothing as it is difficult to adjust large sensors within clothing for collecting signals. Sometimes, sensors are difficult to bend flexibly and adapt to smart clothing [145]. It is necessary to develop flexible sensors that are as thin as possible for embedding and bending with clothing to collect physiological data accurately. In addition, a new tailoring strategy will help in making flexible smart clothing.  
Unfoldable and nonadjustable sensors with smart clothing are not capable of providing accurate information. In smart clothing, electrodes can be used that are foldable and are not seen from the outside as they are covered by the textile.  
The continuous contact of wearable devices with human skin can cause discomfort for users. The minimum contact level during moving the body should be maintained to collect data while ensuring user's comfortability to wear for a longer period as regular daily clothes [7].
## 4 Anaerobic Digestion Process
### Methanogenesis  
In the final phase of the methanogenesis stage, acetic acid is converted into methane and carbon dioxide using bacteria called methanogens, which are anaerobes with a high vulnerability to limited amounts of oxygen. In addition, carbon dioxide is a product that reacts with hydrogen gas to produce more methane. On the other hand, ethanol undergoes decarboxylation to form methane. Two types of bacteria--the acetophilic methanogenic (with _Methanosarcina_ and _Methanosaeta_ active specie) and the hydrogenophilic methanogenic (with _Methanospirilum_, _Methanobacterium formicium_, _Methanoplanus_, and _Methanobreivheterium_ as the dominant specie)--exist in this stage [55]. The former is responsible for the decarboxylation of acetate to methane and the latter for methane formation through a reaction of carbon dioxide and hydrogen [53]. The final product of the anaerobic digestion process is biogas, which is composed of methane and carbon dioxide.  
\[\text{CH}_{3}\text{COOH}\ \rightarrow\ \text{CH}_{4}+\text{CO}_{2} \tag{11}\] \[\text{CO}_{2}+4\text{H}_{2}\ \rightarrow\ \text{CH}_{4}+2\text{H}_{2}\text{O}\] (12) \[\text{2 CH}_{3}\text{CH}_{2}\text{OH}+\text{CO}_{2}\ \rightarrow\ \text{CH}_{4}+2\text{CH}_{3}\text{COOH} \tag{13}\]
# Microseismic Data-Direct Velocity Modeling Method Based on a Modified Attention U-Net Architecture
## 3 Numerical Tests and Results
### Label Datasets  
#### 3.1.1 Multi-layer Random Stratigraphic Models  
In numerical tests, the dataset creation process involved employing a two-dimensional discretization method to discretize the seismic velocity model into a grid under TTI media condition. The grid had a horizontal and vertical grid spacing of 10 m. We also used Perfectly Matched Layer (PML) that absorbs boundaries to reduce the impact of boundary effects on the simulation results, thereby enhancing numerical accuracy. We have maintained a constant density to simplify the model. To ensure dataset complexity, velocity models were randomly generated using a program. The vertical grid consisted of 128 points, while the horizontal grid had 192 points, so the depth of the individual model obtained is 1.28 km, and the horizontal distance is 1.92 km. The number of layers varied randomly between 2 and 4, with velocity increasing gradually from top to bottom as the  
Figure 2: The workflow of this paper.  
number of layers increased. The minimum velocity was set at 1500 m/s, and the maximum velocity was set at 4000 m/s. A labeled dataset of 3000 velocity models was generated, which included both anticlinal and synclinal structures. Figure 3 shows six of these velocity models, with two models each having two, three, and four layers.
# Design of Inverted Nano-Cone Arrayed SERS Substrate for Rapid Detection of Pathogens
## 1 Introduction
[17] reported deposited metal film on oxidized porous Al. The Al\({}_{2}\)O\({}_{3}\) layer between substrate and metal film will work as the resonator that traps the light, and thus increases the EF made by the metal film at the top of the structure. The EF in this work is around \(10^{4}\). Another method needs to be proposed to improve EF. Immunocapturing is a method for bacterial capturing from complex samples by applying specific probes or antibodies targeting specific regions of the bacteria [18]. If the probes are decorated on magnetic beads that can be collected by an external magnetic field, it will allow 2-3 orders of magnitude enrichment of bacteria. It is known as immunomagnetic capturing [19; 20; 21]. The antibiotic modified magnetic bead will bind to the bacteria. Then the bacteria-bead cluster can be collected by applying a magnetic field. The captured bacteria will be delivered to Raman substrate (the substrate approach) [15; 16; 17], or directly bound with plasmonic nanoparticles (the colloidal approach) [19; 20; 21]. The colloidal approach is to excite the LSPR of particles, whose EF is limited within \(10^{6}\). As discussed before, even with the assistance of ordered surface structure, the EF in substrate can hardly be higher than \(10^{6}\)Even with the assistance of immunomagnetic capturing, to perform rapid, non-culturing detection of bacteria, the Raman sensor sensitivity should be improved 2 more orders of magnitude. Unfortunately, the current Raman measurement cannot fill this sensitivity gap. The second concern is the blocking of incident light in current Raman measuring setups. In colloidal methods, bead, bacteria and nano metal particles are randomly bound to each other, and incident light can be blocked by the target cells instead of reaching plasmonic materials. For the substrate approach, bacteria will sit at the top of the plasmonic surface, and incident light will come from top to bottom and a large portion of incident light will be blocked and absorbed by the target bacteria.
## Materials and Methods
### Location Selection, System Design and Dimensioning, System Components, and Scenarios
#### 2.2.3 Technological and Economic Characterization of System Components in Two Scenarios  
The technological and economic characteristics of the selected components will be listed according to the latest available figures in two technology development scenarios. The two scenarios, in different time frames, can be characterized as follows:  
* The Near Future scenario uses current state-of-the-art renewable and hydrogen technology and current energy demand for buildings and transportation. It is an all-electric energy system, which means space heating is done using heat pumps, meeting the present heat demand for houses and buildings. Only commercially available hydrogen technologies are used. For all systems, including hydrogen technologies, current technology characteristics and cost figures are used. The Near Future scenario presents a system that could be implemented in 2020-2025.
* In the Mid Century scenario, a significant reduction in end-use energy consumption is assumed. Hydrogen and fuel cell technologies have become mature with mass production and performing on the cost and efficiency targets projected for 2050. Also, for all the other technologies, such as solar, wind, and electrolyzers, the learning curves are taken into account.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Characteristics**} & \multicolumn{2}{c}{**Quantity**} \\ \cline{2-3}  & **Hamburg, Germany** & **Murcia, Spain** \\ \hline \multicolumn{3}{c}{**Common parameters (based on European statistics)**} \\ \hline Gas stations (\#) [114] & 1 & 1 \\ Retail food shops (\#) [112] & 1 & 1 \\ Households and dwellings 1 in smart integrated city (\#) [54] & 2000 & 2000 \\ \hline \multicolumn{3}{c}{**Local parameters (based on national statistics)**} \\ \hline People (\#) [118] & 4310 & 5083 \\ Passenger cars (\#) [57; 119; 120] & 2364 & 1846 \\ Vans (\#) 2 [57; 119; 120] & 115 & 356 \\ Trucks (\#) [57; 119; 120] & 27 3 & 31 4 \\ Tractor-trailers [57; 119; 120] & 10 & 12 4 [FOOTNOTE:4]Footnote 4: Assuming that only one household lives in a dwelling. 2 German data [119; 120] defines a van as a vehicle with a weight of less than 3.5 tons; the Odyssee database [57] used for Spain defines a van as a vehicle with a weight of less than 3 tons. 3 Including commercial vehicles of 3.5–6.0 tons. 4 No distinction is made between trucks and tractor-trailers in [57]; therefore, the same relation between the number of trucks and tractor-trailers as in Germany is used. 5 Based on the surface area of permanently occupied dwellings [54]. 6 The floor area represents the floor space that needs to be heated, cooled, or illuminated [127]. 7 For ease of comparison, the value is kept equal to the Hamburg case. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristics of the modeled smart city areas.  
The detailed technical and cost-related parameters of the system components are presented in Appendix A.2 Tables A.2 and A.3. The technology selection for the system components and sizing methods is based on the component description in [36].
## 3 Results and Analyses
Subsequently, TEC enhancements appeared in the IGS profiles from 16 to 18 February. The HSSW on 19 February accompanied a CI increase (~150 kA) in the American sector at 0:00 UT, probably due to enhanced currents near the South Atlantic Anomaly. At this time, the GEC also showed an enhancement of approximately 0.2 GECU. The maximal CI enhancement over 300 kA (appeared during the SSC of 23 February in the Asian sector) accompanied the lowest SYM-H values. Note that, during an SSC, there is a compression in front of the magnetosphere, and the resulting magnetopause current creates the magnetic variations observed at the ground level; these magnetic variations were used to compute the ionospheric currents. From this point, the GEC index shows a gradual increase up to 3.5 GECU at the end of the month, showing the ionospheric response to the complex processes of long-duration energization during this complex multiphase geomagnetic storm. For the final observation, there was increased CI at the African Sector, more prominent in the Northern hemisphere, which indicates the responses to the upcoming SSC at the end of the month. Table 1.1 Space weather indices: (**a**) Dst and SYM-H, (**b**) ASY-D, ASY-H, Am, (**c**–**e**) Earth’s CI, (**f**) GEC from IRI-2012 and from UPCG, and (**g**) GIMs TEC differences between the IGS products and the PCA-based model set for quiet conditions (Am = 6) at the EIA crests and at the magnetic equator (from Figure 5a). The SSCs and HSSW are highlighted with vertical yellow and green lines, respectively.
## 3 Data and Methods
### Identification of Stakeholders' Needs and Capabilities
Key differences among the respondents indicated that Bolivia had a higher proportion of graduates (83%); primarily focused on geography (38%), the environment (22%), and security (22%); and showing greater proficiency in geoinformatics, especially in technical knowledge (44%) and interpretation of images (38%). In contrast, Ecuador had a higher proportion of undergraduates (56%) and a significant number of employees with over 10 years of tenure (22%); was more engaged in agriculture (37%) and geography (21%); and reported lower proficiency in geoinformatics skills such as technical knowledge (24%) and interpretation of images (27%). Although both countries showed robust usage of GIS, Bolivian (77%) respondents declared that they use it more than those in Ecuador (65%). Additionally, Bolivian respondents significantly preferred cloud-based platforms (66%) compared with their Ecuadorian colleagues (34%). This variation in skills and focus areas indicated differing expectations and utilization of CS between the two countries, which were crucial for the design of the curriculum for the training workshops in Ecuador and the strategy for future interventions in Bolivia, which are detailed later in this article. In addition, in-depth interviews were conducted to clarify the needs and capabilities in both Ecuador and Bolivia. This approach involved actively listening to stakeholders  
Figure 2: Demographics, experience, and knowledge from Bolivian and Ecuadoran respondents. during their interventions, asking relevant questions (shown in Appendix B), and taking notes to gather detailed information on the following topics:  
* The integration of CS with current operations and objectives;
* The existing infrastructure and potential adaptations of CS;
* The expected challenges and technical requirements for implementation of CS;
* An evaluation of CS impact on operations and decision-making. In the Ecuadorian case, four stakeholders recommended by key collaborators were interviewed via teleconferencing. In contrast, in Bolivia, the interviews were conducted in person during the event, with the content varying based on the stakeholders' interests. These included domain-specific experiences and discussions on their challenges, needs, and expectations of CS. Notes and open-ended questions from the survey on needs and capabilities were processed using ChatGPT-4 [31] to extract keywords from notes and refine the stakeholders' interests in applications of CS. This method facilitated a comprehensive analysis of the stakeholders' needs, challenges, and expectations beyond the applications of CS considered in closed questions. This enabled the customization of the training program, allowing the training team and capacity-building efforts to meet the diverse needs of the stakeholders involved.
## 3 Preliminaries of IMU and Extended Kalman Filter Used in UWB Positioning
### Extended Kalman Filter  
Extended Kalman filter (EKF) technology is utilized to make an optimal estimation of the state of a nonlinear system. A linear time-varying system can be used to describe a nonlinear system within each sampling interval, and then the linear time-varying system can use the Kalman filter (KF). The corresponding processes are as follows [21].
## 2 Engineering Materials and Methods
### Regional Geological Setting
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Engineering Position**} & \multirow{2}{*}{\begin{tabular}{c} **Boring Height** \\ **(m)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Orfice Height** \\ **(m)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Deep** \\ **Hole** \\ **(m)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Coating** \\ **Thickness** \\ **(m)** \\ \end{tabular} } & \multicolumn{2}{c}{**The Buiral Depth of Lower Limit of Each**} & \multirow{2}{*}{**Note**} \\  & & & & & & & **Weathering Zone (m)** & \\ \hline \multirow{6}{*}{
\begin{tabular}{c} Tailwater tunnel \\ \end{tabular} } & ZK43 & 147.75 & 140.1 & 0 & 122.3 & 140.1 & — & Alteration zone \\  & ZK49 & 131.6 & 90.15 & 9.6 & 90.15 & — & — & Alteration zone \\  & ZK50 & 114.31 & 100.3 & 7.2 & 11.6 & 24.2 & — & Complete weathering \\  & ZK51 & 104.82 & 80.15 & 0.8 & 5.6 & 7.65 & 66.1 & 48.6-59.9 m shows \\  & ZK52 & 114.63 & 70.5 & 7 & 30.3 & 43.6 & 70.5 & strong weathering \\  & ZK53 & 104.84 & 55.6 & 3.5 & 9.2 & 24.4 & 33.4 & — \\  & ZK54 & 96.48 & 45.4 & 6.3 & 21.4 & 28.1 & 45.4 & — \\  & ZK55 & 91.9 & 45.4 & 3.3 & 13.5 & 14.8 & 40.2 & — \\ \cline{2-8}  & ZK56 & 102.22 & 35.5 & 7.5 & 16.8 & 21.9 & 32.5 & The hole depth of \\ \cline{2-8}  & ZK57 & 93.93 & 45.7 & 3.8 & 8.1 & 12.6 & 35.5 & 35.5-45.2m shows \\  & ZK58 & 89.78 & 35.4 & 4.7 & 10.3 & 17.4 & 35.4 & — \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary statistics of the weathered depths of the drilled holes in the tailwater tunnel. Figure 3: Geological structure and drilling layout of engineering area. lower limit of the medium-weathered zone is 33-50 m deep, and the maximum depth is 70.5 m (ZK52). The completely weathered depths of ZK43 and ZK49 near the alteration zone are at 122.33 and 190.15 m, respectively.
# Enhanced Phase Retrieval Method Based on Random Phase Modulation
###### Abstract  
The phase retrieval method based on random phase modulation can wipe out any ambiguity and stagnation problem in reconstruction. However, the two existing reconstruction algorithms for the random phase modulation method are suffering from problems. The serial algorithm from the spread-spectrum phase retrieval method can realize rapid convergence but has poor noise immunity. Although there is a parallel framework that can suppress noise, the convergence speed is slow. Here, we propose a random phase modulation phase retrieval method based on a serial-parallel cascaded reconstruction framework to simultaneously achieve quality imaging and rapid convergence. The proposed serial-parallel cascaded method uses the phased result from the serial algorithm to serve as the initialization of the subsequent parallel process. Simulations and experiments demonstrate that the superiorities of both serial and parallel algorithms are fetched by the proposed serial-parallel cascaded method. In the end, we analyze the effect of iteration numbers from the serial process on the reconstruction performance to find the optimal allocation scope of iteration numbers.  
random phase modulation; phase retrieval; spatial light modulator +
Footnote †: journal: Applied Sciences
## 5 Simulations
### Performance Evaluation of the ECHR Protocol under Varying Weighting Coefficients
After an extensive series of simulations, the optimal value is located at the area where \(0.5\leq\lambda_{1}\leq 1.5\) and \(3.0\leq\lambda_{2}\leq 4.0\). Here, we find optimum weighting coefficients, \(\lambda_{1}\), equal to 0.7, and \(\lambda_{2}\), equal to 3.3, with the maximum network lifetime under a 100% sensing coverage ratio. Figure 5: Comparison of network lifetimes under different sensing coverage ratios with varying \(\tau_{1}\) when set \(\tau_{2}=3.1\). Table 2 depicts the coverage-lifetime comparison under varying \(\lambda_{1}\) when \(\lambda_{2}\) is set at 3.3. The network in which \(\lambda_{1}=0.7\) provides longer coverage-lifetime under a 100% sensing coverage ratio, compared to other values of \(\lambda_{1}\). For instance, when \(\lambda_{1}=0\), the improvement in the network coverage-lifetime reaches 43%. In this case, the distance factor of the energy-aware hierarchical routing algorithm is not taken into account which means that farther neighboring nodes with higher residual energy can be chosen as the parent node. Under this situation, the energy consumption in data transmission increases. However, this fact causes the network to fast run out of power. On the other hand, the impact of the distance factor of Equation (7) on the energy-aware hierarchical routing mechanism is greater than that of the residual energy factor when \(\lambda_{1}\) is set at a large value, say 50. Figure 6: Comparison of network lifetimes under different sensing coverage ratios with varying \(\tau_{2}\) when set \(\tau_{1}=1\). Figure 7: Plot of network lifetime with 100% sensing coverage _versus_\(\lambda_{1}\) and \(\lambda_{2}\) when \(\tau_{1}=1\) and \(\tau_{2}=3.1\). Such a situation drives the node to choose the nearest node as a parent node which could be a critical node. If the essential node over used as an intermediate node, the node may quickly run out of energy, and thus causing the network cease working. Table 2 also shows the results of coverage-lifetime comparison using different values of \(\lambda_{2}\) when \(\lambda_{1}=0.7\). The ECHR protocol has the best performance when \(\lambda_{2}=3.3\), despite the similarity in the coverage-lifetime under a 100% coverage ratio. If the residual energy factor of Equation (7) is not taken into account (_i.e._, \(\lambda_{2}=0\)), and the distance factor, \(\lambda_{1}\), will be the only factor that influences the result of selecting parent node. This case is similar to the scenario that \(\lambda_{1}\) is set at a large value, as mentioned above. Furthermore, the effect of the residual energy factor is greater than that of the distance factor when \(\lambda_{2}\) is set at a large value, e.g., 50. This means that a node will choose its parent node depending on which one of possible parent candidates has the highest residual energy. Because the distance factor is also considered in this case, the performance of the network where \(\lambda_{1}=0.7\) and \(\lambda_{2}=50\) is better than that of the network where \(\lambda_{1}=0\) and \(\lambda_{2}=3.3\).
## 3 Routing Protocols in WSN  
The protocols that were responsible for data transmission in WSN were broadly ordered into two sets [2], namely, (i) old-fashioned routing, and (ii) opportunistic routing. In the traditional routing, also known as old-fashioned routing techniques, the focus was on finding the route with a minimum number of intermediate nodes from the source to the destination, without taking into consideration some of the important factors like throughput, quality of links, reliability, etc. A small comparison [27] of the routing categories is shown in Table 1.  
As it is clear from the literature that energy consumption of a sensor node had a considerable impact on the lifetime and quality of the wireless sensor network, therefore, it becomes vital to design energy-efficient opportunistic routing protocols to maximize the overall lifetime of the network and also to enhance the quality of the sensor network. There are few methods in the literature listed below that might be useful to save the life of the sensor network.  
* Scheduling of duty cycle
* Energy-efficient medium access control (EE-MAC)
* Energy-efficient routing
* Node replacements (not possible in unattended environments)  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Routing Feature** & **Opportunistic Routing (OR)** & **Traditional Routing (TR)** \\ \hline Transmission type & Broadcast & Unicast \\ Data packets overheard & Yes & No \\ Relay selection & Dynamic & Fixed \\ Number of candidates & Multiple & Relay alone (Single) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative study between opportunistic routing and traditional routing.  
* Energy harvesting
* Energy replenishment
* Energy balance  
Of the above-mentioned methods for energy saving, energy-efficient routing is the most central method for the vitality of the WSN. As this method involved the transmission of signals, i.e., receiving and sending, it took about 66.66 percent of the total energy of the network [28]. Therefore, it became relevant that an opportunistic routing protocol that enhanced the vitality of the sensor network might be designed for enhancing the overall life span of the sensor network.