esa-sceva/satcom-chunk-collection huggingface dataset:
# FANET Routing Protocol Analysis for Multi-UAV-Based Reconnaissance Mobility Models
## 4 Experimental Setup
### Research Question  
To analyze protocols under the consideration of FANET characteristics and reconnaissance scenarios, we asked the following research questions:  
1. [label=RQ0]
2. Which routing protocol of AODV, DSDV, or OLSR shows the highest PDR performance in the reconnaissance scenario?
3. How does the connectivity of nodes in a network affect the performance of a protocol?
4. Which mobility model shows the highest PDR performance in the reconnaissance scenario?
5. How does the reconnaissance rate of a mobility model affect the performance of the mobility model in the reconnaissance scenario?RQ5. How does the GCS location affect the performance of the mobility model in the reconnaissance scenario?  
We first identified the first two questions to understand the performance of protocols that can be used in FANETs where a network topology frequently changes. In the evaluation of FANET protocols, we anticipated that a reactive routing protocol AODV would show higher PDR performance than other proactive routing protocols OLSR and DSDV. We also anticipated that, as the number of nodes \(N\) in FANETs increased, the connectivity of nodes \(NC\) improved; however, at some point, due to the overhead for updating a routing table, the PDR performance of routing protocols could decrease.  
\[N\propto NC,\ NC\
ot\propto PDR \tag{1}\]  
We then identified the last three questions to understand the effect of other factors, such as mobility models, their reconnaissance rates \(R_{t}\), and GCS locations on the PDR performance of FANET protocols. We first anticipated that SWRP yields the best PDR performance in the five mobility models, because the mobility SWRP model randomly chooses the destination and flies to the destination in a straight line. The mobility patterns of the nodes could have many chances to pass through the center of the reconnaissance area, and the nodes have high possibility of being close to the GCS in the center of the reconnaissance area.  
\[\frac{1}{distance(GCS,nodes)}\propto PDR \tag{2}\]  
However, if we consider the reconnaissance rate as one of the performance factors of a mobility model, we anticipated that a mobility model with a high reconnaissance rate \(R_{t}\), RDPZ, would show higher RPDR performance than other mobility models. Last, we anticipated that the GCS location could affect the performance of a routing protocol. It is because the previous study [33] observed that a mobility model tends to have a biased direction in the reconnaissance area. The movements of nodes to the biased direction place nodes far from or close to the GCS location. The distance of a node to GCS could make a difference.
## 3 Fundamentals of RF Energy Harvesting
### Components of an RF Energy-Harvesting System
#### 3.2.2 Matching Network
Recent advancements have introduced tunable and reconfigurable matching networks that employ varactors, MEMS switches, and machine learning algorithms to dynamically adjust impedance in real time, further optimizing energy-harvesting efficiency.
# Dynamic Information Flow Tracking: Taxonomy, Challenges, and Opportunities
## 2 Background
### Tag Propagation  
One fundamental and important operation in DIFT is the propagation of the tag from source to destination. In the hardware-based DIFT, the tag propagation can be divided into two types: (i) the tag propagation operation accompanied with instructions in the pipeline [21; 22]; and (ii) dedicated hardware logic for tag propagation, e.g., coprocessors [24; 25; 27; 28]. Within the scope of hardware-based DIFT, in gate-level DIFT methods, the tag is propagated in parallel with the data flow through gate logic. For micro-architecture-level DIFT design, the tag is propagated between registers and memory. For SoC-level DIFT design, the tag is propagated through the bus in parallel with the corresponding bus request or data transfer. In order to support the tag propagation, the bus needs to be extended. Alternatively, the tag may be transmitted at the bus transaction initialization phase.  
In software-based DIFT design, the information related to the target program is extracted using binary instrumentation or static analysis. Then the DIFT-related instructions are inserted to propagate the tag from one entity to another entity. The granularity of an entity depends on the implementation details, e.g., variables, basic blocks and threads [2]. Tag propagation in software and hardware co-design needs the cooperation between software and hardware. Extra DIFT-related ISA [31] is introduced to perform the tag propagation in the hardware. Additionally, the security policy can be defined using software and the tag propagation is automatically executed at the hardware layer according to the defined security policy.  
For software and hardware co-design-based DIFT, the tag is often implemented at the hardware level. The DIFT operations are inserted into the target program to instruct the processes of tag propagation and tag checking. The authors in [31] used binary instrumentation to convert all implicit flows into explicit flows. Every instruction in standard ISA is assigned corresponding IFS ISA to instruct the tag-related operations at the micro-architecture level. In addition to the extended ISA, the authors in [41] modified the kernel to support a timestamp which defines the life cycles of corresponding data. Furthermore, the tag at the micro-architecture level will be propagated according to the timestamp defined at the kernel level. In [33], DIFT operations are converted to symbolic values instead of actual instructions. Processor pipelines will be extended to support the symbolic execution and corresponding tag propagation.
# Estimation of Terrestrial Water Storage Changes at Small Basin Scales Based on Multi-Source Data
## 5 Discussion  
### Uncertainty  
Uncertainties in ET and runoff can come from multiple sources: model misrepresentation or parameterization, forcing data, and other variables [62; 63]. Due to the complexity of the model, it is difficult to estimate the uncertainty of ET and runoff caused by the uncertainty of the input precipitation forcing dataset. To quantify the uncertainty of the hydrological model output data, [PERSON] et al. [64] evaluated water cycle components from 18 state-of-the-art water resources reanalysis (WRR) datasets derived from different hydrological models, meteorological forcing, and precipitation datasets. They found that the uncertainty in the estimation of the water cycle components from different products is mainly attributable to the differences in the schemes used by the different models, while different precipitation forcing datasets have less impacts on the precision of the WRR output data. Therefore, in this study, we assumed that each variable is independent and estimated the uncertainties of TWSC.  
Uncertainties in the monthly estimates of TWSC were assessed by combining the individual uncertainties for precipitation, ET, and runoff with the propagation of errors [65] (Equation (3)).  
\[\sigma_{\text{TWSC}}=\sqrt{\sigma_{P}^{2}+\sigma_{ET}^{2}+\sigma_{Q}^{2}} \tag{3}\]  
where \(\sigma_{P}^{2}\), \(\sigma_{ET}^{2}\), and \(\sigma_{Q}^{2}\) represent the error of precipitation, ET, and runoff, respectively.  
Due to data limitations, we simply calculated the uncertainties of the TWSC for the whole sub-basins in the GRB. We used monthly precipitation measurements from three precipitation stations in the GRB from the CMA to evaluate the accuracy of the CMFD precipitation estimations. The uncertainty of ET was derived from the RMSE between PML_V2 ET and the ET estimations from the water balance method in the GRB [66]. We used monthly runoff measurements from Waizhou station to evaluate the accuracy of the runoff estimations. The \(\sigma_{P}\), \(\sigma_{ET}\), \(\sigma_{Q}\), and \(\sigma_{TWSC}\) are \(\pm\)17.3 mm/month, \(\pm\)21.2 mm/month, \(\pm\)28.7 mm/month, and \(\pm\)39.6 mm/month, respectively. The uncertainty of TWSC estimated with this method is close to the RMSE between the mean TWSC of all sub-basins and TWSC from CSRM in the GRB.
# Financial Optimization of a Solar-Driven Organic Rankine Cycle
## 2 Material and Methods
### The Examined System
#### Financial Analysis Formulation  
The system investment cost (\(C_{0}\)) is calculated as:  
\[C_{0}=K_{col}\cdot A_{c}+K_{tank}\cdot V+K_{orc}\cdot P_{el} \tag{20}\]  
The yearly system cash flow (\(CF\)) can be written as:  
\[CF=E_{el}\cdot K_{el}-K_{O\&M} \tag{21}\]  
The operation and maintenance cost (\(K_{O\&M}\)) is estimated as 1% of the capital cost:  
\[K_{O\&M}=0.01\cdot C_{0} \tag{22}\]  
The net present value (\(NPV\)) of the investment is calculated as:  
\[NPV=-C_{0}+R\cdot CF \tag{23}\]  
where the equivalent project life (\(R\)) is expressed as:  
\[R=\frac{(1+r)^{N}-1}{r\cdot\left(1+r\right)^{N}} \tag{24}\]  
The simple payback period (\(SPP\)) is defined as:  
\[SPP=\frac{C_{0}}{Time\cdot CF} \tag{25}\]The payback period (_PP_) is calculated as:  
\[PP=\frac{\ln\left[\frac{CF}{CF-C_{0}\tau}\right]}{\ln(1+r)} \tag{26}\]  
The levelized cost of electricity (_LCOE_) can be calculated as below:  
\[LCOE=\frac{C_{0}+N\cdot K_{\text{O\&M}}}{N\cdot E_{el}} \tag{27}\]  
Table 1 includes the data of the financial analysis [14; 32; 33]. It has to be said that, according to Ref. [32], the electricity price in Greece for a CSP with thermal storage is 0.28485 \(\epsilon\)\(\text{kW}_{\text{hel}}\)\({}^{-1}\), which is a very satisfying value.
# Waiting Time Distributions of Transport through a Two-Channel Quantum System
## 4 Conclusions  
We are the first to employ the Born-Markov approximation to obtain a reduced particle-number resolved master equation for a two-channel QD system. Then, we investigated the WTD statistics of electron transport through a two-channel quantum system in a strong Coulomb blockade regime and for non-interacting dots. The influences of the phase differences between the two channels, the asymmetry of the dot-state couplings to the left and right electrodes, and Coulomb repulsion on the WTD statistics were investigated. The results show that these factors have obvious effects on the WTD statistics of the system.  
First, the two-channel QD system in the strong Coulomb blockade regime is a single reset and renewal system, while the two-channel QD system in the non-interacting dots is a multiple reset and nonrenewal system.  
Second, the phase differences between two channels, the asymmetry of the dot-state couplings to the left and right electrodes, and Coulomb repulsion have obvious effects on the WTD statistics of the system. In a certain parameter range, the system manifests the coherent oscillatory behavior of WTDs in a strong Coulomb blockade regime, and the phase differences between the two channels is clearly reflected in the oscillation phase of the WTDs. This oscillatory behavior is an analog of the optical double-slit interference fringe. The effect of the level spacing \(\delta\varepsilon\) is an analog of slit spacing in optical double-slit interference.  
Third, the electron waiting time of the system is negatively correlated when the two-channel QD system is for non-interacting dots. The different phase differences between the two channels can obviously enhance this negative correlation.  
In brief, the short-time physics and correlations in mesoscopic systems can be characterized by WTDs, which are not accessible for FCS. The WTD statistics can be affected by the different phase differences between the two channels. The multiple reset system exhibits nonrenewal characteristics. These results deepen our understanding of the WTD statistical properties of electron transport through a mesoscopic QD system and help pave a new path toward constructing nanostructured QD electronic devices.  
[PERSON] prepared the manuscript, presented the concepts and theoretical derivation, and ran numerical calculations in this article. [PERSON] contributed to data analysis and manuscript preparation. [PERSON] carried out the study and collected important background information. All authors read and approved the final manuscript.  
This work was funded by the National Natural Science Foundation of China (grant no. 61774062, no. 11674109); the Project of Department of Education of Guangdong Province; Science and Technology Planning Project of Guangdong Province, China (grant no. 2017A020219007); 2019 Natural Science Project of Guangzhou College of Technology and Business (grant no. KA201931); and the Research Fund Program of Guangdong Provincial Key Laboratory of Nanophotonic Functional Materials and Devices.
# The Multi-Branch Deep-Learning-Based Approach to Heart Dysfunction Classification
## 2 Proposed Approach
### Core Networks  
Each branch consists of the core network, which is the main processing engine (see Figure 2). As the core network, one may use different deep-learning models. Data processing by the core network can be performed multiple times on more than a single block of the same network, which increases model efficiency, as shown in [18]. In our research, we tested three models used as the core network: LSTM, GRU, and N-BEATS.  
The LSTM architecture, proposed in 1997 by [PERSON] and [PERSON] [32], is a variant of a recurrent neural network in which each memory cell holds its state with a self-connection (the constant error carousel), while trainable input and output gates decide when to write new data into the cell and when to release its contents. By guaranteeing uninterrupted gradient flow over extended sequences, this design overcomes the vanishing gradient problem. It lets the model retain or discard information selectively, making it exceptionally well-suited for capturing long-range dependencies in sequential data. It is one of the most commonly used classifiers [33].  
The gated recurrent unit (GRU) was first introduced in 2014 by [PERSON] et al. [34]. While retaining LSTM's gated approach to managing information flow, the GRU relies on just two gates--an update gate that blends the previous hidden state with a newly proposed activation and a freshly introduced reset gate to control incorporation of the previous state--eliminating separate memory cells, lowering parameter count, easing training, yet still ensuring robust gradient propagation for capturing long-range dependencies.  
Neural Basis Expansion Analysis for Interpretable Time Series (N-BEATS) was introduced by [PERSON] et al. in 2019 [35; 36] as an architecture for forecasting time series. It is based on a multi-layer fully connected (FC) network, with non-linearities introduced through the ReLU activation function. The network predicts the basis expansion coefficients in both the forward (forecast) and backwards (backcast) directions, with individual blocks arranged into stacks through a doubly residual stacking strategy, potentially incorporating layers that share backcast and forecast functionalities. After removing the backcast from the original signal, the result is passed to the next block, so successive blocks learn to interpret exclusive parts of the signal. In [37], N-BEATS, which was previously used only for forecasting, was proposed to diagnose cardiac problems and compared with commonly used LSTM and GRU networks for such problems. N-BEATS achieved similar results in the experiments and worked exceptionally well for a low number of ECG leads.
## 6 Pitfalls and Potentials
### Virtualization Feasibility of Network Functions
#### 6.2.2 Core Functions Complex for Virtualization  
The User Plane Function (UPF) handles packet routing and forwarding, and it is crucial to ensuring uninterrupted operation [129; 130]. Even though UPF can be virtualized, doing so may impact QoS and QoE, especially in scenarios with high data rates or strict latency requirements [134]. Therefore, careful consideration and proper resource allocation are necessary to avoid degradation of QoS and QoE [134]. Similarly, the Access and Mobility Management Function (AMF) is responsible for managing connections and mobility [129; 130]. Like UPF, AMF is time-sensitive as it must maintain seamless connectivity for mobile users [129; 130]. Although AMF can be virtualized, achieving low latency and high QoS/QoE in a virtualized environment could be challenging [135]. Additionally, the Session Management Function (SMF) handles session establishment and maintenance. While SMF can also be virtualized, maintaining high QoS/QoE presents challenges due to its need for rapid response to session changes.
## 4 Proposed System
### Equation of Motion  
As discussed in the previous section, the IMS greatly simplifies the test setup and specimen preparation. Nevertheless, the specimen's loading and stiffness are impacted through the P-\(\Delta\) effect at large displacements. The significance and extent of this effect can be examined by the dynamic equation of motion for the entire device depicted in Figure 8. For a non-linear single-degree-of-freedom (SDOF) system with constant viscous damping [16] subjected to horizontal earthquake motion, the dynamic equation of motion can be written as:  
\[m\ddot{x}(t)+c\dot{x}(t)+k(t)x(t)=-m\ddot{x}_{g}(t) \tag{1}\]  
where \(m\) is the oscillator's mass; \(c\) is the damping coefficient; \(k(t)\) is the stiffness; \(x\), \(\dot{x}\), and \(\ddot{x}\) are the oscillator's displacement, velocity and relative acceleration, respectively; and \(\ddot{x}_{g}\) is the ground motion acceleration.  
Figure 7: IMS reaching the maximum displacement limit under specimen collapse.  
For the system shown in Figure 8, Equation (1) can be rewritten as:  
\[m\ddot{x}(t)+c\dot{x}(t)+\bigg{(}k(t)-\frac{W^{\prime\prime}}{H}\bigg{)}x(t)=-m \ddot{x}_{g}(t) \tag{2}\]  
For Equation (2), \(m\) is the lateral effective mass of the complete system and is equal to:  
\[m=m_{1}+m_{2}+m_{3}+\frac{m_{4}}{2} \tag{3}\]  
where \(m_{1}\) is the IMS mass, \(m_{2}\) is the mass of the rigid link system, \(m_{3}\) is the axial load system's mass on the column, \(m_{4}\) is the specimen mass, \(H\) is the specimen height, and \(W^{\prime\prime}\) is the IMS effective weight, which, in turn, is calculated as:  
\[W^{\prime\prime}=\bigg{(}m_{1}+\frac{m_{2}}{2}\bigg{)}g \tag{4}\]  
where \(g\) is the gravity acceleration (9.81 m/s\({}^{2}\)). The total displacement of the whole system, \(x_{abs}\), is defined as:  
\[x_{abs}(t)=x(t)+x_{g}(t) \tag{5}\]  
where \(x(t)\) is the relative column displacement and \(x_{g}(t)\) is the shake table displacement. The values of the above variables are listed in Table 1.  
Figure 8: IMS mass locations.
# The Power System and Microgrid Protection--A Review
## 2 DG and Microgrid Challenges
### Sensitivity and Response Time  
A microgrid has a very fast response time and low system inertia, especially in islanded mode. This is due to the lack of high reserves in DG sources, especially reactive power. A protection system must be able to detect quickly and accurately, as small disturbances cause catastrophic consequences in the system [59].
## 6 Does the Initiation Hyperstructure Contain Glycolytic Enzymes?  
Metabolism is coupled to DNA synthesis through nutrient richness and growth rate in a variety of ways. One way this occurs is via (p)ppGpp [46], with the transcription of _dnaA_ in _E. coli_[47] and the level of DnaA protein in _C. crescentus_[48] being lowered by (p)ppGpp. Another way is via a central carbon metabolism that, in _E. coli_, can: (1) promote DnaA to its active DnaA-ATP form and its binding to _oriC_ by cAMP (a regulator of this part of metabolism) [49]; (2) suppress the defects of the _dnaA46_ mutant by changes in pyruvate and acetate metabolism [50]; (3) inhibit DnaA conversion to DnaA-ATP and its binding to _oriC_ (via acetylation of DnaA, with acetyl-CoA and acetyl-phosphate as donors) (for references see [51]). Evidence for the involvement of the central carbon metabolism in DNA replication in _Bacillus subtilis_ includes: (1) subunits of pyruvate dehydrogenase (Pdhc) and related enzymes bind the origin of replication region, DnaC and DnaG inhibit the initiation of replication; (2) mutations in the genes of central carbon metabolism suppress initiation and elongation defects in _dnaC_, _dnaG_, and _dnaE_ mutants; (3) mutations in _gapA_ (which encodes glyceraldehyde 3-phosphate dehydrogenase) perturb the metabolic control of replication; (4) pyruvate kinase (PykA) can both inhibit initiation and stimulate elongation via proposed interactions with DnaC, DnaG, and DnaE as modulated, perhaps, by phosphorylation [51; 52].  
Given that metabolic enzymes can exist as hyperstructures in their own right, a question that arises here is whether metabolic enzymes are also part of an initiation hyperstructure. And a related question is whether the metabolites themselves are directly part of initiation and/or replication hyperstructures, not just by binding to the constituent proteins (as in the case of DnaA and cAMP [49]) or by being used to modify the protein (as in the case of DnaA and acetyl-CoA), but also by binding directly to RNA or DNA. If metabolites were indeed to bind the origin region, this would make the connection with the _Ring World_, an origins-of-life scenario in which small, double-stranded DNA rings were selected firstly because they catalysed the reactions of central carbon metabolism [53].
# The Effect of Carbon Nanofibers Surface Properties in Hydrogenation and Dehydrogen Reactions
## 3 Results
### Cinnamyl Alcohol Dehydrogen  
The performance of the Pd nanoparticles supported on different CNFs has been tested also on the cinnamyl alcohol dehydrogenation reaction in an inert nitrogen atmosphere. The main products of the reaction are cinnamaldehyde (CAL), hydrocinnamyl alcohol (HCOH) and 1-phenyl-1-propene (PPE), although further side products can potentially be formed, such as hydrocinnamaldehyde (HCAL) and 1-phenylpropane (PPR) (Figure 8). As per the CAL hydrogenation, the catalytic activity seems to be related to both surface oxygen content and nanoparticles dimension, with the Pd/CNFs-HHT being the most active (TOF 6144 s\({}^{-1}\)). In all the cases, the main product was CAL, with HCOH and PPE as side products. Only a very small amount of PPR and HCAL were detected in the reaction mixture (reported as \"others\" in Table 7). It is interesting to notice that the product selectivity, at 90% iso-conversion, is mainly influenced by the presence of oxygen functionalities. A decrease in the oxygen content, in fact, results in an increase in CAL production from 52% with Pd/CNFs-PS-OX to 70% with Pd/CNFs-HHT. A similar trend was observed with HCOH, confirming that in an inert atmosphere and in the absence of oxygen functionalities the disproportion mechanism is favoured. With the increase of acidic surface groups, however, the hydrogenolysis mechanism starts to become  
Figure 6: Reusability of the Pd/CNFs-HHT catalyst in the cinnamaldehyde hydrogenation reaction. Reaction conditions: 25 \({}^{\circ}\)C, 1 bar H\({}_{2}\), 0.3 mol L\({}^{-1}\) cinnamaldehyde in p-xylene, substrate: metal molar ratio 1:1000. Time of reaction 90 min.  
Figure 7: XPS analysis of the Pd/CNFs-HHT catalyst before (top) and after (bottom) reaction.  
evident, as suggested by the increase in PPE selectivity from 9% with the Pd/CNFs-HHT to 24% with the Pd/CNFs-PS-OX. The plot of conversion as a function of time of the four catalysts is reported in Figure S2 in the Supplementary Information.