esa-sceva/satcom-chunk-collection huggingface dataset:
# MPH-M, AODV-M and DSR-M Performance Evaluation under Jamming Attacks
## 6 Results
### Jamming Attack Detection Scheme  
Table 3 shows the areas bounded by the nodes in each position of the jammer. For each protocol, the table displays which nodes are free of attack (with a \(\checkmark\)) and the nodes that are under attack (with a \(\times\)). From this table, we can observe that the MPH-M protocol makes a better delimitation of the affected area, around 95% of success, because almost all nodes that should form the area were included in it.  
The Table 3 shows the result of the jamming detector for those nodes that are under attack. The table exposes different results of detection for each of the studied protocols, this is because each routing algorithm has different design principles and therefore the performance of the detection mechanism will also differ for each case. We note that MPH-M has a better performance against detection. For the case when the jammer node is near the collector node, MPH-M has an efficiency in detecting the affected nodes of 88.9%, AODV-M has 44.4% and DSR has 88.9%. For the case when the jammer node is in the middle of the topology, MPH has an efficiency of 100%, AODV-M has 77.8% and DSR-M has 88.9%. For the case where the jammer node is far from the collector node (the opposite corner in the topology), MPH has an efficiency of 100%, AODV has 75% and DSR has 100%.
# High-\(Q\) TeO\({}_{2}\)-Si Hybrid Microring Resonators
## 1 Introduction
More generally, propagation losses lower than 0.4 dB/cm for the \(C\)-band and 0.8 dB/cm for the \(O\)-band of silicon wire waveguides have been reported for waveguides with a 440 nm core width, 220 nm core height, and 2 \(\upmu\)m-thick SiO\({}_{2}\) cladding layer defined by a high-resolution immersion lithography process [27]. While all of these approaches to improving \(Q\) factors in silicon MRRs are promising for different applications, they either suffer from performance trade-offs (e.g., multi-mode operation, larger footprint, and/or significantly reduced FSR) or add fabrication cost and complexity. In this paper, we demonstrate a silicon MRR with a \(Q\) factor of 1.5 \(\times\) 10\({}^{6}\) at 1550 nm, corresponding to a propagation loss of 0.42 dB/cm, fabricated with a standard foundry process, plus a low-temperature post-process deposition of a TeO\({}_{2}\) cladding layer. In addition to enabling a straightforward and monolithic low-loss hybrid waveguide structure, TeO\({}_{2}\) has promising optical properties for new functionalities in silicon photonic microsystems. TeO\({}_{2}\) has been shown to be thermally and chemically stable, possess high nonlinearity, and low optical attenuation from visible to mid-infrared wavelengths (0.4-5 \(\upmu\)m), and have a high refractive index (2.1 at 1550 nm) and low dispersion [28]. Furthermore, the unique site variability in the TeO\({}_{2}\) glass matrix enables high rare-earth dopant solubility and leads to large emission bandwidths, motivating its application in integrated optical amplifiers and lasers [29; 30; 31], including the recent demonstration of a hybrid rare-earth laser directly on silicon with an internal quality factor of 5.6 \(\times\) 10\({}^{5}\)[32]. This low-loss platform has significant potential for linear, nonlinear, and active optical applications in silicon photonics.
## 2 Methodology
### External Uncertainties in Load Optimization  
The proposed framework provides effective load optimization based on weather, occupancy, and energy prices. External uncertainties that can affect performance include policy changes, demand fluctuations, and renewable energy intermittency. These are discussed below.  
* **Policy Changes:** Regulatory policies include adjustments in net metering policies, carbon pricing, and/or energy tariffs that affect costs. To ensure adaptability, the framework can integrate periodic policy updates by retraining the model with revised energy pricing and regulatory data.
* **Demand Fluctuations:** Unpredictable occupant behavior, seasonal variations, and external grid constraints may cause deviations from expected load patterns. To address this, the framework updates continuously using occupancy-driven forecasts based on real-time IoT and environmental data to enable fast load adjustments.
* **Renewable Energy Intermittency:** Variations in solar irradiance affect the availability of renewable energy. The framework mitigates this by incorporating probabilistic forecasting to anticipate fluctuations and by dynamically managing battery storage to compensate for variability. Historical weather data and real-time solar radiation measurements can also be used to improve prediction accuracy.
## 5 Results and Discussion
### Flooding Day
#### 5.1.2 Travel Distance  
The most commonly mentioned impact of a disrupted transportation network could be interpreted as the travelled distance because it does not necessitate the use of a traffic model and can be analyzed using road network assumptions. In a congested urban region, the additional travel distance may not be very important because there are many available alternatives. On the other hand, flooding may result in several closures in the same location, thereby fragmenting a network and making reroutes longer. The key statistics about the travelled distance in the two peak durations of demand are shown in Table 5. When the number of rerouted cars is compared to the number of cars taking longer routes, it is obvious that rerouting does not always imply higher distances traveled. More than half of the rerouted vehicles took longer routes between 12:00 and 13:00. Since in route choosing  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Travel Time** & **8–9 h** & **12–13 h** & **Overall (0–24 h)** \\ \hline  & Dry condition (h) & 23,572.9 & 23,957.6 & 109,200.7 \\  & Flooded condition (h) & 26,431.7 & 31,539.3 & 130,854.4 \\  & Difference (h) & 2858.8 & 7581.7 & 21653.7 \\  & variation (\%) & 12.1 & 32.1 & 20.0 \\  & Vehicles percentage & 23.8 & 37.6 & 28.1 \\ With 10\% & Average delay/journey time (\%) & 43.2 & 59.2 & 42.3 \\  & No variation vehicles (\%) & 45.7 & 38.3 & 27.2 \\ With 20\% & Average delay/journey time (\%) & 21.6 & 23.7 & 10.3 \\  & Average delay/journey time (\%) & 43.6 & 47.9 & 56.8 \\  & No variation vehicles (\%) & 43.2 & 37.8 & 33.4 \\ With 50\% & Vehicles percentage & 11.8 & 12.6 & 2.7 \\ delay & Average delay/journey time (\%) & 61.5 & 67.9 & 127.9 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Flood effects on travel time.  
shorter travel time is preferred over shorter travel distance, perhaps some cars after a route change can reduce their travel distance. As another reason, in case of a car stuck in traffic, the flooded road segment may be reopened to traffic by the time that car reaches that segment. As it is shown in Table 5, the additional travelled distance (e.g., 24% for travel time between 8:00-9:00) and number of rerouted cars did not increase proportionately to each other (19.3%).
## Abbreviations  
The following abbreviations are used in this manuscript:  
**Abbreviation**: **Full Form**:  
ANN & Artificial neural network  
CSO & Cuckoo Search Optimization  
DC & Direct current  
FLC & Fuzzy logic control  
GA & Genetic algorithm  
GPP & Global peak power  
IBC & Interleaved boost converter  
IC & Incremental conductance  
MPP & Maximum power point  
MPPT & Maximum power point tracking  
P&O & Perturb and observe  
PSC & Partial shading condition  
PSO & Particle Swarm Optimization  
PV & Photovoltaic  
SSA-PSO & Salp swarm algorithm-Particle Swarm Optimization
# CSNOMA: Carrier Sense Non-Orthogonal Multiple Access
## 6 Concluding Remarks  
In this paper, we investigated the possibility of cross-layer design of MAC with considering PHY and MAC layers. We proposed a new multiple access scheme, Carrier Sense Non-Orthogonal Multiple Access (CSNOMA). The proposed scheme utilized the interference cancellation technique, and shows better area spectral efficiency (ASE) performance than the CSMA scheme. The backward compatibility is important in new multiple access scheme design. An algorithm to derive an optimal carrier sensing range that maximizes ASE is presented through analysis according to the stochastic geometric theory. We designed the practical signaling process and the control packet frame structure compatible with IEEE 802.11 DCF mode in order to colocate the node applying CSNOMA and the node applying IEEE 802.11 DCF. In this protocol design, additional information is exchanged through hierarchical modulated CTS messages to reduce signaling overhead. Through this, higher ASE was achieved by finding nodes with good channel condition and performing IC. We also proposed a reactive mode that operates conservatively to reduce signaling overhead and energy consumption. The reactive mode was effective when a hidden node problem occurred as the carrier sensing range was relatively short. Although the transmission probability of a node that has seized a transmission opportunity through carrier sensing may be high, a node that is activated by receiving additional information from CTS is not protected by the carrier sensing range, so the transmission success rate may decrease. To compensate for this case, a receiver protection mode that conservatively performs node activation was presented. Our research direction is to build a testbed by implementing CSNOMA on real equipment. Through this, we will practically explore the convergence of the PHY layer and the MAC layer, and continue to try to combine with other PHY technologies.  
Conceptualization, D.M.K.; methodology, D.M.K.; formal analysis, D.M.K. and S.-L.K.; writing--original draft preparation, D.M.K. and S.-L.K.; writing--review and editing, D.M.K. and S.-L.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Soonchunhyang University Research Fund (No. 20180998). This research was supported by a grant to Bio-Mimetic Robot Research Center Funded by Defense Acquisition Program Administration, and by Agency for Defense Development (UD190018 ID).  
The authors declare no conflicts of interest.
# Adaptive Truncation Threshold Determination for Multimode Fiber Single-Pixel Imaging
## 4 Discussion
### The Design Goal of ATTD  
The design goal of ATTD is to determine a truncation threshold that approaches the best recovery under a certain noise level without prior knowledge of noise levels. Common knowledge is that the recovery quality of TSVD-based MMF-SPI decreases as noise increases. Therefore, the performance of ATTD was evaluated using correlations relative to the clairvoyant reference, which provide the best recovery under certain noise levels, rather than the similarity relative to ground truth. ATTD exhibited a higher correlation relative to the clairvoyant reference than traditional fixed truncation and SURE under high noise levels, although it achieved poor recovery performance compared with the ground truth.
# Multifrequency Impedance Method Based on Neural Network for Root Canal Length Measurement
## 3 Results
### Discussions Compared with EALs  
The development of EALs has been a long and continual process, which seems to lag behind the rapid development of modern medical technology. After the impedance ratio method was established, only a few studies researched means by which to select two proper frequencies to enhance the performance of third-generation EALs [25; 26; 27]; however, no landmark improvement has emerged in the field of root canal length measurement until the present study. Although the measurements of fourth-generation EALs seem to be more accurate, their actual performance does not present a great improvement, compared with the benchmark of the third-generation product--Root ZX--according to product comparison experiments [28; 29]. In addition, in actual surveys, many dentists have provided feedback that they are more inclined to use third-generation products (e.g., Root ZX) as, in clinical settings, the third- and fourth-generation EALs differ little in accuracy; furthermore, the older models are more stable and cheaper. Root ZX is an excellent product, but it is not perfect; doctors often must use radiography as an aid to obtain accurate results when employing RootZX [30].  
Obviously, much room still exists for improvement in the development of EALs. Machine learning is a popular subject, which has been widely used in the medical field and has promoted the rapid development of medical technology [31]. The method proposed in this paper combines the multifrequency impedance ratio method and neural networks. In fact, an EAL can be regarded as a prediction system or nonlinear regression model, considering that the measuring conditions have a critical influence on the results. Therefore, the impedance ratios remaining after feature selection and the numerical measuring factors can be used as the features. Neural networks possess great advantages in formulating such prediction models.  
The experimental results indicated that the proposed measurement method is relatively robust and can improve the effects of measuring factors on the results. The experiments in this study may not have been perfect, however. Further improvements in accuracy can be considered based on the following  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Frequency Combination (kHz)** & **Group 1** & **Group 2** & **Group 3** & **Total** \\ \hline
5/0.5 & 66.67\% & 83.33\% & 100.00\% & 85.71\% \\
10/0.5 & 50.00\% & 83.33\% & 100.00\% & 80.95\% \\
10/1 & 50.00\% & 83.33\% & 89.89\% & 76.19\% \\ Multifrequency & 83.33\% & 100.00\% & 100.00\% & 95.24\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performances of the impedance ratio method and neural network-based multifrequency method.  
aspects: improving the neural network structure, using different judgment strategies, using different optimization methods and, most importantly, expanding the data set.
# Cross-View Outdoor Localization in Augmented Reality by Fusing Map and Satellite Data
## 2 Related Work
### Small-Scale Localization with Deep Learning  
Recently, work has been conducted to find an image's precise pose on a single satellite image. Vigor [19] simultaneously optimized for the satellite image retrieval task as well as the pose estimation task. Other research split satellite data into patches and used cosine similarity to match a deep representation of the ground image; this was then used to obtain a probability density map for the localization [17]. In a follow-up work, they introduced rolling descriptor matching and created a probability density map over multiple levels to refine their estimations [16]. Research has been also been conducted using transformers with the aim of achieving results beyond those obtainable by CNN-based models [18].  
Furthermore, the localization estimate can be updated over time by utilizing particle filters [25; 26].  
To close the large modality gap between ground-based images and satellite images, [PERSON] et al. used polar-transformed aerial imagery to match both cropped and uncropped 360 degree panoramic images [14]. In a follow-up work, [PERSON] et al. introduced a model that utilized a U-net-based architecture for both the ground-level image and satellite image [13]. They used a projection step with estimated position and orientation and calculated the mean square error of the projection and deep representation of the ground-level image. Their model optimized the estimated pose using the Levenberg-Marquardt optimization algorithm within several iterations [20]. Their network was fully differentiable, which allowed the model to backpropagate the pose loss over the weights of the feature extractors.  
Afterwards, SliceMatch [11] was proposed, which used masking to efficiently calculate the cosine similarity between the ground-level image and many different poses on the masked satellite image.  
Other methods have included the use of multiple ground-level images and a perspective view to bird's eye view transformation to create uncertainty-aware visual localization [10] and have used LIDAR for better pose estimation [15].  
The current best approach, however, comes from utilizing OpenStreetMap data instead of satellite data [31]. These data include rich tagged information and are noticeably less noisy. By extracting node, line, and area data as segmented data and using a largertraining dataset, OrienterNet models a probability map that outperforms satellite-only approaches [12].
## 3 Underwater Channel Characterization
### Electro-Magnetic Properties of Water
#### 3.2.1 Magnetic Permeability of the Medium  
The magnetic permeability is the ability of a medium to store magnetic fields [11]. As water and air, both are non-magnetic in nature, and their relative permeability is same [11; 19]. Thus, permeability of water has no effect over the EM propagation.
# Ionic Liquids Hybridization for Carbon Dioxide Capture: A Review
## 4 Application of ILs Hybrid Material in Carbon Dioxide
### ILs/Activated Carbon
The grafted AC was dried under vacuum at 95 \({}^{\circ}\)C for 8 h. Based on morphology analysis, the pore structure of AC/IL prepared by grafting method is more porous than AC/IL prepared using impregnation technique. High loading of IL into AC during impregnation process led to pore blockage. Besides that, the result indicated that the modified AC exhibits better CO2/N2 selectivity than raw AC. Data analysis had revealed impregnated AC showing high CO2/N2 selectivity due to higher IL loading compared to AC prepared via grafting method. Further observation on breakthrough curve showing the CO2 absorption capacity increased as pressure increased. Other than that, grafted AC/ILs showed higher CO2 absorption capacity than raw AC and impregnated AC due to less pore blockage by ILs compared to the other two samples. In another study, [PERSON] and co-workers conducted a study on the effect of amine-functionalized using protic and amino ILs for AC impregnation towards CO2 adsorption [93]. The impregnated AC was prepared using physical immobilization with different percentage of ILs loading. Initially, an appropriate amount of AC was added into the mixture of IL and methanol. The mixture was gently shaken at room temperature for 24 h and methanol was slowly removed under vacuum condition to ensure successful immobilization. The resulting impregnated AC was then dried at 110 \({}^{\circ}\)C for 2 days. The surface area and pore volume of impregnated AC had reduced, thus indicating ILs' successful impregnation. On the contrary, the AC/ILs had more CO2 adsorption capacity than raw AC due to the increment in pore size resulting from addition of smaller micropore filling by ILs. Besides that, due to acidic nature of CO2, it was expected that the presence of two amine functionalities at ILs with [Arg]\({}^{-}\) and [Lys]\({}^{-}\) anion on AC will provide an extra active site for gas adsorption compared to glycinate [Gly]\({}^{-}\). However, the CO2 adsorption by AC/[VBTMA][Arg] and AC/[VBTMA][Lys] is lower than AC/[VBTMA][Gly]. Low CO2 adsorption capacity in [Arg]\({}^{-}\)-and [Lys]\({}^{-}\)-based ILs is possibly due to the blockage of internal pore structure caused by the presence of extra amines group. This, in turn, prevented the CO2 access into ILs active site.
## 4 Simulation Results  
The simulation results produced from the proposed method which is addressed to diagnose the open-circuit faults of GSC are evaluated in this section.  
Simulink is used to simulate 22 states of GSC of PMSG wind turbine, as shown in Figure 4. The three-phase current \(I_{A}\), \(I_{B}\) and \(I_{C}\) varying with wind speed are extracted, where the subscripts  
Figure 3: The mission profile of open-circuit fault diagnosis with proposed novel method.  
A, B and C are the each phase. 1000 samples are extracted under each state. The length of time of each sample is between \(T\) and \(1.15T\), where \(T\) is the period of the phase current. 800 samples out of 1000 samples under each state are randomly selected to compose training set, and the remaining 200 samples are used to compose test set. So, the sum of entire samples is 22,000, including 17,600 in training set and 4400 in test set.  
Table 2 is the parameters of main simulation components.  
Where \(U_{\text{d}}\) denotes the voltage of DC-Link; \(U_{AB/BC/CA}\) is the line voltage of GSC; Voltage of sine wave is utilized to generate trigger signals for IGBT modules; Frequency of sine wave is equal to the rated frequency of phase current; Frequency of triangular wave is equal to the switching frequency of IGBT modules; Phase difference of each phase denotes the phase relationship between any two phases.
# Scaling of FAPAR from the Field to the Satellite
## 3 Methodology
### Temporal Normalization  
Limited by time, labor and available instruments, mobile measurements were performed in a sequential manner across multiple field sites. The problem naturally arose as how to match the FAPAR values measured at varying hours to the time of satellite overpass. Since the effects of subpixel cloud  
Figure 2: Methodology of scaling Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) from the field to the satellite.  
could be neglected in fine-resolution ASTER data, the time of _in-situ_ measurement should ideally be normalized to 10:30 a.m. local time [13]. Thus, the aim of temporal normalization is to normalize the FAPAR measured at different time to the time of satellite overpass.  
For the purpose of temporal normalization, a simple equation that can characterize the FAPAR measured at a specific time (at a given SZA) as a function of canopy structures, SZA and illumination conditions is preferred. Based on this, temporal normalization can be achieved by deriving the relationship between the instantaneous FAPAR at different time. The section is organized into: (i) the introduction to the FAPAR-P model (Section 3.2.1); (ii) the estimation of skylight proportion from the 6S model (Section 3.2.2); (iii) the simplified FAPAR model integrating FAPAR-P and 6S models (Section 3.2.3); and (iv) the derivation of the simplified SZA correction model (Section 3.2.4).