esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
To address the shortcomings of the SIF method, including the inability to measure the ChlF parameters and being affected by the plant's Chl content, we have introduced a new technique using a joint Fraunhofer line depth and laser induced saturation pulse method (FLD-LISP) to measure photosynthesis signals directly [PERSON] et al. (2015). In this paper a more comprehensive study of FLD-LISP was presented to better evaluate the performance of the suggested methodology for the measurement of ChlF parameters. We evaluated the performance of FLD-LISP to measure \(\Phi_{\text{PSII}}\), NPQ, and ETR under different actinic PPFDs in three C3 and C4 plant species having different responses to light. The PAM method was used to verify and understand the FLD-LISP. The effect of PPFD on \(\Phi_{\text{PSII}}\), NPQ, and ETR exhibited the same trend regardless of the method used to estimate ChlF parameters in all three plant species. The \(\Phi_{\text{PSII}}\), NPQ, and ETR values derived using FLD-LISP and PAM were very well correlated, confirming the good accuracy of FLD-LISP to estimate ChlF parameters regardless of growth stages (variable Chl content) and differences in species confirming the usefulness of FLD-LISP to measure ChlF parameters and plant photosynthesis activity.  
Finally, the FLD-LISP method does not need accurate pulse-synchronized and modulated fluorimetric techniques as the PAM and LIFT methods do. For long-distance (canopy scale) measurement of ChlF parameters using FLD-LISP, the combination of FLD-LISP and LIFT's lighting technique, which is interpolated to a maximum fluorescence level using a series of low-intensity laser pulses and a fluorescence model, would be recommended.  
This work was supported by the Japan Society for the Promotion of Science (Grant No. 24248043).  
[PERSON] ([PERSON]), [PERSON] ([PERSON]) and [PERSON] ([PERSON]) conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments and analyzed the data; [PERSON]-[PERSON], [PERSON], and [PERSON] wrote the paper and discussed the results.  
The authors declare no conflict of interest.
# The Applications and Challenges of Digital Twin Technology in Smart Grids: A Comprehensive Review
## 5 Challenges and Limitations
### Cost and Complexity  
#### 5.3.1 Limitations  
The cost and complexity associated with developing and deploying Digital Twin systems pose significant barriers to adoption, particularly for smaller organizations. Companies face challenges in accurately defining the Return on Investment (ROI) for Digital Twin implementations, as the technology is still relatively new, and cost estimates can be difficult to determine. While Digital Twins are expected to reduce product development and operational costs over time, the upfront investment required for building digital representations of physical entities is often prohibitive. These costs include data acquisition, sensor deployment, and the integration of IoT technologies, all of which can add substantial expenses.  
Furthermore, the complexity of integrating multiple data sources and technologies, such as simulation, enterprise IT, operational technology (OT), and IoT-connected devices, exacerbates the cost and complexity issues. A proportion of 72% of early Digital Twin adopters report difficulties in unifying disparate data types, leading to challenges in maintaining system interoperability and ensuring robust cybersecurity practices. This fusion of technologies adds to the already high cost of creating and maintaining Digital Twin environments.  
Despite these challenges, the potential for Digital Twin technology remains substantial, driving organizations to make difficult investment decisions. However, due to the nascent nature of the technology, best practices for cost management and complexity reduction are still evolving, and there is a degree of risk and experimentation involved in the deployment of Digital Twins.
## 3 Physical Description of the On-Chip, Optical Wireless Link  
Regardless of the many technological stages involved in its overall manufacturing process, an integrated circuit is often roughly represented as a layered structure as shown in Figure 1. The first stage of the process flow (front-end-of-line--FEOL) aims at the deployment of the electrical/electronic circuit components into the semiconductor (e.g., silicon) layer. Some metallization and dielectric layers are then interleaved in the next steps (back-end-of-line, BEOL), and holes are finally etched through the insulating material to connect the metallic layers, which provide the required interconnects among the circuit elements underneath [26].  
Various viable solutions have been proposed in the literature for the inclusion of the optical layer in the chip stack. For example, the optical layer can be placed on-top of the electronic interconnection layers [27; 28] as shown in Figure 1, or in plane with it [29; 30; 31]. The layer stack is finally completed by additional upper layers accounting for possible passivation/filling materials and for the outer package enclosure. It is worth mentioning that a further layer might be present, either on the top or at the bottom, corresponding to a possible heat sink.  
The on-chip optical wireless communication indeed occurs in the optical layer, as sketched in Figure 1, which can be regarded as a wireless evolution of the on-chip optical wired link outlined  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Interconnect Solution** & **NoC Technology** & **Related References** \\ \hline \multirow{2}{*}{Physical Interconnection} & Networks-on-Chip (NoC) & [1; 5; 20; 21] \\  & Optical Networks-on-Chip (ONoC) & [5; 6; 11; 20; 22] \\ \hline \multirow{2}{*}{Wireless Networks} & Millimeter wave and THz (WiNoC) & [4; 5; 8; 17; 23; 24] \\  & Optical (OWiNoC) & [9; 10; 18; 19; 24] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Survey on Networks-on-Chip -related studies.  
Figure 1: Layered representation of the chip structure.  
in [6]. According to the OWiNoC paradigm, the optical layer is further split into a wireless and a wired part, the former consisting of the optical antennas spreading the core-to-core signals into the wireless channel, the latter including the optical elements interfacing the cores to the optical antennas.  
This sketch of possible implementations of OWiNoC aims at introducing the main, representative features of the optical layer layout further addressed in the following subsections.
## 2 Design Principles for Mesh Networks
### Multi-Input, Multi-Output  
A MIMO configuration is applied in novel wireless multi-radio access technologies (Multi-RATs), Wi-Fi, LTE, and NB-IoT. While wireless Wi-Fi devices can boast two to four antennas, smart meters typically accommodate no more than two antennas. The augmentation of antennas on diminutive terminals hinges upon the sophistication of superconducting materials.  
The expanse for MIMO's proprietary advancement seems somewhat constrained. Lately, the technology spotlight has gravitated toward methods employing a solitary antenna to achieve heightened gain across multiple directions, eclipsing the prominence of superconducting materials. Furthermore, MIMO technology embodies non-line-of-sight transmission capabilities, transcending the mere consideration of transmitting and receiving antenna quantities.  
At present, MIMO functions as a conduit for point-to-multipoint communication between communication nodes and smart meters. Multipoint-to-multipoint communication techniques must be extended to commensurate with environmental prerequisites, optimizing both bandwidth utilization and communication steadfastness. Consequently, the application of MIMO configurations within mesh networks is poised to dominate forthcoming communication landscapes.  
MIMO technology endows the prospect of array gain, interference abatement, diversity gain, and multiplexing gain, promising an amplification of system coverage, heightened link quality, augmented system capacity, and spectral efficiency enhancement, thus elevating data transmission speeds. Additionally, in tandem with the swift evolution of modern digital signal processing technology and the perpetually escalating processing capabilities of digital computing chips, the computational intricacies accompanying MIMO have experienced significant reductions, rendering it more practicable than ever.  
Figure 2: Hierarchy of global M2M connectivity technology.  
In envisioning the requisites of prospective wireless communication systems, MIMO proffers dual categories of gain--spatial diversity gain and spatial multiplexing gain. These, respectively, augur enhancements in link quality and data transmission speeds.
## 3 Preliminaries
### The Charging Station Location Problem  
The distribution of electric vehicle (EV) charging stations on real-world networks necessitates the introduction of a definition for the road network. In this context, we consider a directed graph as the representation of the city's road network. Let \(G=(V,E)\) represent the entire road network, where \(V\) denotes the set of vertices and \(E\) represents the set of edges. The vertices and edges correspond to Points of Interest (POIs), such as road junctions or charging stations, and the roads that connect these nodes in \(V\), respectively. The coordinates of a vertex \(v\) are denoted by \(\tau(v)\).  
This study takes into account the scenario where multiple charger plugs are available in a single charging station. An EV charging station is denoted as \(s\), and its location is specified as \(v(s)\). The status of chargers with different types in a station is recorded as \(t(s)=(t_{1},t_{2},\ldots,t_{m})\), where \(m\) represents the number of charger plug types and \(t_{i}\) denotes the number of chargers of the \(i\)-th type. The set of all EV charging stations is denoted as \(S\).  
A charging plan is defined as \(p=\{s_{1},s_{2},\ldots,s_{n}\}\), which is a combination of selected charging stations. The collection of all possible charging plans is denoted as \(\mathcal{P}\). The objective of this study is to find the optimal plan \(p^{*}\) from \(\mathcal{P}\) that maximizes benefits or minimizes costs in the real-world context.
## 5 Results
### Energy Efficiency  
Table 7 shows the energy consumption (in Joules) across different deep learning models and data distributions required by each FL algorithm to reach the accuracy threshold of 80%. Note that results showing a \"-\" value indicate that the system failed to converge after the maximum of 400 training rounds we conducted. Overall, the results demonstrate that FedSAGE consistently consumes the least amount of energy, highlighting its efficiency in both training and resource utilization.  
For the CNN model, under uniform data, FedSAGE achieves 80% accuracy with just 16,812.44 J, significantly outperforming other methods such as FedAvg (273,348.52 J),  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multirow{2}{*}{**DNN Model**} & \multirow{2}{*}{**Data Distribution**} & \multicolumn{4}{c}{**Energy Consumption on Each FL Algorithms (Joule)**} \\ \cline{3-7}  & & **FedAvg** & **FedSAGE** & **FedRand** & **FedSpeed** & **FLIS** \\ \hline \multirow{3}{*}{CNN} & Uniform & 273,348.52 & 16,812.44 & 41,486.30 & - & 50,673.33 \\ \cline{2-7}  & Non-uniform & 175,649.49 & 9484.04 & 32,677.66 & 15,375.52 & 58,383.31 \\ \hline \multirow{3}{*}{BiLSTM} & Uniform & 33,878.16 & 6834.96 & 12,500.02 & 10,503.85 & 48,610.55 \\ \cline{2-7}  & Non-uniform & 54,085.36 & 6002.80 & 16,979.58 & 8923.32 & - \\ \hline \multirow{3}{*}{Transformer} & Uniform & - & 65,601.83 & 153,981.76 & - & - \\ \cline{2-7}  & Non-uniform & 884,910.56 & 53,835.56 & 185,082.63 & 92,980.70 & - \\ \hline \hline \end{tabular}
\end{table}
Table 7: Energy consumption (Joules) required to achieve the accuracy threshold of 80%.  
Figure 7: Client-side execution time (in seconds) for local training and VAE inference under heterogeneous CPU core configurations.  
FedRand (41,486.30 J), and FLIS (50,673.33 J). Notably, FedSpeed is unable to reach the accuracy threshold even after 400 training rounds, indicated by the value \"-\". In the non-uniform scenario, FedSAGE only requires 9484.04 J, again showing the best performance, while the other methods consume two to five times more energy.  
Similar trends are observed with the BiLSTM model. For uniform data, FedSAGE consumes 6834.96 J, compared to FedAvg (33,878.16 J) and FLIS (48,610.55 J). In the non-uniform case, FedSAGE consumes only 6002.80 J, the lowest among all approaches.  
For the more complex Transformer model, FedSAGE remains the most energy efficient. Under uniform distribution, it requires 6560.83 J, while FedRand and FLIS consume over 150,000 J. In the non-uniform setting, FedSAGE achieves 80% accuracy with 53,835.56 J, whereas all other methods require significantly more energy--FedAvg, for example, exceeds 880,000 J. Again, several methods (including FedSpeed and FLIS in certain cases) fail to reach the 80% accuracy threshold even after exceeding the maximum of 400 rounds.  
By achieving both high accuracy and significant energy savings, FedSAGE proves to be a practical solution for deployment in real-world, resource-limited environments such as edge computing and IoT systems.
# Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial
## 7 Routing for Multi-hop WSNs with a Single Immobile Sink
### Initial Optimization Problem Formulation  
The initial formulation given in [2] is a max-min non-linear program where the objective function is to maximize the minimum of all lifetimes across the nodes in the network. The life time of each node is defined as the quotient of the initial battery energy of the node to the sum of expended energies on each of the node's outgoing flows. A brief explanation of the formulation is given as follows:  
* Decision variables: Continuous flow variables for all links in the network. Each of these variables has a lower bound of zero and an upper bound of the link's capacity.
* Objective function: Maximizes the minimum life time of all nodes given as \(T_{i}\left(r\right)=\frac{B_{i}}{\sum_{j\in N_{i}}E_{ij}r_{ij}}\) where \(B_{i}\) is the initial battery energy for node \(i\), \(E_{ij}\) is the energy spent per bit to transmit data from node \(i\) to node \(j\) on a direct link, \(N_{i}\) is the set of neighbor nodes that have direct arc connections with \(i\), and \(r_{ij}\) is the flow decision variable for link \(\left(i,j\right)\).
* Constraint set: Is a linear equality set of conservation of flow constraints for all the nodes in the network. They simply state that the difference between the outgoing flows from each node and its incoming flows should strictly be equal to the data generated by the node itself.  
To make the problem easier, the minimum term in the objective function is replaced by an auxiliary variable, that is \(T=\underset{i}{min}T_{i}\left(r\right)\). In order to guarantee that the new objective function has an equal value to the previous one, an upper bound constraint is enforced on the new variable \(T\) that represents the energy conservation constraint for each node, that is \(T\leq\frac{B_{i}}{\sum_{j\in N_{i}}E_{ij}r_{ij}}\) for all nodes. This makes the problem a bilinear quadratically constrained program.
# Performance Evaluation of LoRa Considering Scenario Conditions
## 5 Results and Discussion
### Theoretical Coverage Study  
As aforementioned, before performing the experimental study, we used a radio planning tool [8] that permited us to estimate the signal level within the areas covered by the base-station in the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scenario** & **Average Speed (km/h) (Conf. Interval, \(\alpha=0.05\))** & **Min. Speed (km/h)** & **Max. Speed (km/h)** \\ \hline Urban & 22.3 (1.9) & 0 & 53.5 \\ Suburban & 32.6 (2.3) & 0 & 57.2 \\ Rural & 32.5 (1.5) & 0 & 70.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Vehicle speeds during the sampling tests.  
different scenarios. This tool employs topographic maps in order to consider the impact of the terrain elevations on the signal propagation and presents highly configurable options to precisely simulate the characteristics and conditions of the real equipment. Thus, by configuring this tool with the features corresponding to our test-bed configuration, i.e., antennas' gain and height, receiver sensitivity, transmission power, etc. (Table 2), the coverage maps showed in Figures 5-7 have been obtained. Recall that the propagation model applied for obtaining these estimations is the Okumura-Hata model. Observe that the green areas represent the levels of received power above the card sensitivity at the lowest DR, i.e., \(\approx-130\) dBm [4]; for the sake of clarity, we have indicated the length of these theoretical maximum link distances. In turn, the blue areas represent a level of received power that is below the receiver's floor sensitivity. Thus, observe that for both, the urban and suburban scenarios the planning tool predicted a link range about 7 km, while in the rural environment 19 km was the maximum predicted distance. The similar predictions for the two first scenarios can be explained by the presence of obstacles in both of them, which limited the maximum reached range; in turn, the complete absence of obstacles in the rural scenario permitted a notable maximum range that is not usually reachable by traditional IoT communication technologies.  
Figure 5: Theoretical coverage map by considering the Okumura–Hata propagation model. Urban scenario.
## 3 Results and Discussion
### Effect of Alkaloid Extracts in PC-12 Cells Induced by OS with H\({}_{2}\)O\({}_{2}\)  
In this study, we aimed to identify the optimal concentration and duration of H\({}_{2}\)O\({}_{2}\) exposure that would induce oxidative stress in PC-12 cells while causing minimal damage. This would provide an effective experimental model to assess oxidative stress. Additionally, using the MTT test, we sought to determine the concentration and duration of treatment with \(G\). _grandifforum_ alkaloid extracts that would showcase their non-toxic antioxidant properties. Consequently, PC-12 cells were subjected to 200 uM H\({}_{2}\)O\({}_{2}\) for 18 h to induce oxidative stress. To evaluate their protective effects, the cells were treated with alkaloid extracts at concentrations of 100, 250, and 500 ug/mL for 24 h. Preliminary results indicate that the alkaloid extracts offered antioxidant protection to the PC-12 cells from the oxidative damage induced by H\({}_{2}\)O\({}_{2}\). The protective mechanisms of alkaloids against H\({}_{2}\)O\({}_{2}\)-induced oxidative stress as reported in the literature are as follows: Scavenging free radicals [64], inhibiting lipid peroxidation [65], chelating metal ions [66], regulating TDH homeostasis [67], preserving the balance of TOS/TAS/OSI [68], enhancing antioxidant enzyme activity [69], modulating cellular signaling pathways [70], and inhibiting inflammation [71]. In our study, we planned experiments from the alkaloid extract to determine the detailed protective mechanisms against H\({}_{2}\)O\({}_{2}\)-induced oxidative stress.  
Figure 2: Antioxidant activities of alkaloid extracts (CAE: Chloroform alkaloid extract, MAE: Methanol alkaloid extract, WAE: Water alkaloid extract). The figures were presented as mean \(\pm\) SD. [PERSON]’s test was used in the case of \(p<0.05\). * Significantly different from the control (n: 3 for each bar).
# Fewer Dimensions for Higher Thermal Performance: A Review on 2D Nanofluids
###### Abstract  
The current work aims to offer a specific overview of the homogeneous dispersions of 2D nanomaterials in heat transfer base fluids--so-called 2D nanofluids. This data compilation emerged from the critical overview of the findings of the published scientific articles regarding 2D nanofluids. The applicability of such fluids as promising alternatives to the conventional heat transfer and thermal energy storage fluids is comprehensively investigated. These are fluids that simultaneously possess superior thermophysical properties and can be processed according to innovative environmentally friendly methods and techniques. Furthermore, their very reduced dimensions are suitable for the decrease in the size of thermal management systems, and the devices have attracted a lot of attention from researchers in different fields. Some examples of 2D nanofluids are those which incorporate graphene, graphene oxide, hexagonal boron nitride, molybdenum disulfide nanoparticles, and hybrid formulations. Although the published results are not always consistent, it was found that this type of nanofluid can improve the thermal conductivity of traditional base fluids by more than 150%, achieving values of approximately 6500 W-m\({}^{-1}\).K\({}^{-1}\) and interface thermal conductance above 50 MW-m\({}^{-2}\).K\({}^{-1}\). Such beneficial features permit the attainment of increments above 60% in the overall efficiency of photovoltaic/thermal solar systems, a 70% reduction in the entropy generation in parabolic trough collectors and increases of approximately 200% in the convective heat transfer coefficient in heat exchangers and heat pipes. These findings identify those fluids as suitable heat transfer and thermal storage media. The current work intends to partially suppress the literature gap by gathering detailed information on 2D nanofluids in a single study. The thermophysical properties of 2D nanofluids and not of their traditional counterparts, as it is usually encountered in the literature, and the extended detailed sections dedicated to the potential applications of 2D nanofluids are features that may set this research apart from previously published works. Additionally, a major part of the included literature references consider exclusively 2D nanomaterials and the corresponding nanofluids, which also constitutes a major gathering of specific data regarding these types of materials. Upon its conclusion, this work will provide a general overview of 2D nanofluids.  
2D nanomaterials; heat transfer; thermal management; thermal fluids 2023