esa-sceva/satcom-chunk-collection huggingface dataset:
# Data-Driven Construction of User Utility Functions from Radio Connection Traces in LTE
## 4 Estimation of QoS Thresholds on a Per-Service Basis  
A novel method to automatically estimate QoS thresholds for different services is described in this section. In this work, only the threshold that determines the worst network performance tolerated by users before terminating the connection is estimated. Depending on the service, this critical value corresponds to \(QoS_{i,th_{min}}^{(s)}\) or \(QoS_{i,th_{max}}^{(s)}\). Estimation is carried out by a heuristic approach based on user behavior observed in connection traces. The inputs to the method are the following descriptors, collected for each connection: (a) the QCI value; (b) the Radio Resource Control (RRC) connection time; (c) the total downlink (DL) and uplink (UL) traffic volume at the packet data converge protocol level; (d) the DL traffic volume ratio transmitted in the last transmission time intervals (TTIs) [31]; (e) the DL activity ratio, computed as the ratio between active TTIs (i.e., those with data to transmit) and the effective duration of the connection; (f) the DL session throughput, computed as the volume transmitted in the DL divided by the effective duration of the connection; (g) the mean downlink delay, \(\tau\), defined as the sum of DL mean connection delays in Radio Link Control (RLC) and Medium Access Control (MAC) layers; and (h) the mean DL Packet Data Control Protocol (PDCP) connection throughput, \(TH_{PDCP,DL}\), excluding the last TTIs. The output of the method is an estimate of the QoS threshold for each indicator \(i\) and service \(s\), \(QoS^{(s)}_{i,th_{min}/max}\).  
Two main steps are required to estimate QoS thresholds for each service: (1) the classification of connection traces on a service basis and (2) the estimation of QoS thresholds for each service by analyzing user behavior.
## 5 Research and Development Challenges
### Topology Control to Reduce Flight Time  
Topology control is a major issue for reducing drone flight time. In multi-UAV systems, selecting the path with the most effective route is a challenging issue that must be solved to minimize the overall energy consumption and travel distance [33]. The joint consideration of the topology and routing to select the optimal path is shown to be successful in minimizing delay as well as energy consumption [64; 65]. Thus, to meet the excessive parcel delivery requirements, multi-UAV deployment can be a potential solution [66].
## 1 An Overview of the Topic and Its Ramifications
### Nonlinear Waveguides
is a huge research area in many branches of physics [52], including optics [47], matter waves in atomic BECs [53], and BECs of quasi-particles (in particular, excitons-polaritons) [54], plasmas [55], ferromagnetic media [56], long Josephson junctions in superconductivity [57], acoustics [58], etc. In many cases, waveguiding settings offer media in which many species of solitons can be created and/or stabilized if the solitons do not exist (or exist but are unstable) in the respective uniform media. Characteristic examples are various methods elaborated for the stabilization of three-dimensional spatiotemporal solitons (\"light bullets\" [59]), which are subject to strong instabilities in both two- and three-dimensional uniform media [60; 61; 62]. It was demonstrated experimentally that both fundamental spatiotemporal solitons [63] and ones with embedded vorticity [64] can be made stable (in fact, as semi-discrete solitons) in the above-mentioned systems created as bundles of parallel waveguing cores in bulk silica samples [26]. In fact, the commonly known stability of temporal optical solitons in nonlinear fibers [47] is also an example of the stabilization of a localized mode which is--strictly speaking--a three-dimensional one, with the self-trapping in the temporal (longitudinal) direction induced by the nonlinearity, while the transverse trapping is secured by the fiber's guiding properties, which are not essentially affected by the nonlinearity. Furthermore, the stability of matter-wave solitons in cigar-shaped trapping potentials [53] is provided by a similar mechanism, in spite of a completely different physical nature of the latter setting: the longitudinal self-trapping is induced by the self-attraction of the condensate, due to attractive interactions between atoms, while the confining potential prevents spreading of the condensate's wave function in the transverse directions. Moderate deviation from the effective one-dimensionality essentially affects the shape of the matter-wave solitons, but still relies upon the trapping potential to prevent the collapse of the three-dimensional self-attractive condensate [65].
# Resource Management in Cloud Radio Access Network: Conventional and New Approaches
## 4 Challenges and Open Research Issues
### Multi-Objective Resource Management  
With the convergence of information and communication technology, the available types of resources in C-RANs would comprise computational resources, network resources, radio resources, and cache resources. Most of the ongoing researches has considered only one type of resource in the C-RAN architecture while developing resource management schemes. Optimizing multiple resources would simultaneously facilitate the achievement of higher system performance in this network. The system model for linking various types of resources needs to be explored to simplify the simultaneous optimization. A significant challenge for multi-dimensional resource management would be the handling of multiple resources that are allocated at different time scales. This is known as a two-timescale problem [75]. This challenge needs to be addressed in the future for efficient multi-objective resource allocation.
## 2 Experimental
### Reaction Equipment and Product Analysis  
The automated reaction equipment used (PID Eng. and Tech. Microactivity) (Figure 1) is illustrated elsewhere [35]. It is provided with a high-pressure isothermal 316 stainless steel reactor, with an internal diameter of 9 mm and 10 cm of effective length. The reactor is located inside a ceramic chamber, heated by an electric resistance, and can operate up to 100 atm, 700 \({}^{\circ}\)C, and 5 g of catalyst. The catalyst is mixed with an inert solid (carborundum with 0.035 mm average particle size), to attain a sufficient bed height under low space time conditions, as well as to ensure the isothermality of the fixed-bed.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Catalyst**} & \multicolumn{4}{c}{**Physical Properties**} & \multicolumn{4}{c}{**Acid Properties**} \\ \cline{2-7}  & \(\mathbf{S_{BET}}\) & \(\mathbf{V_{micropore}}\) & \(\mathbf{V_{p}}\) & Acid Strength & Total acidity & B/L * \\  & **(m\({}^{2}\) g\({}^{-1}\))** & **(cm\({}^{3}\) g\({}^{-1}\))** & **(cm\({}^{3}\) g\({}^{-1}\))** & **(k) mol\({}_{\text{NHS}}\)\({}^{-1}\)** & **(mmol\({}_{\text{NHS}}\) g\({}^{-1}\))** & **at 150 \({}^{\circ}\)C** \\ \hline CZMn & 121 & 0.002 & 0.284 & - & - & - \\ S-18 & 480 & 0.160 & 0.390 & 130 & 0.42 & 0.15 \\ CZMn/S-18 & 213 & 0.060 & 0.278 & 99 & 0.12 & - \\ \hline \hline \end{tabular}  
* Bronsted/Lewis sites ratio.  
\end{table}
Table 1: Physical and acid properties of the individual metallic and acid functions and of the bifunctional catalyst.  
Figure 1: Reaction equipment; FIC: Flow Indicator Controller; TIC: Temperature Indicator Controller; LIC: Level Indicator Controller; PC: Pressure Controller; FT: Flow Transmiter.  
Reactant and reaction product samples (diluted in a He stream of 25 cm\({}^{3}\) min\({}^{-1}\)) are continuously analyzed in a Varian-CP4900 gas micro-chromatograph provided with three analytical modules: (i) Porapak Q (PPQ) (10 m \(\times\) 20 m), where light products (CO\({}_{2}\), methane, ethane, propane, methanol, DME, water and butanes) are separated; (ii) a molecular sieve (MS-5) (10 m \(\times\) 12 m) to separate H\({}_{2}\), CO, O\({}_{2}\) and N\({}_{2}\); (iii) 5 CB (CPSiL) (8 m \(\times\) 2 m), to identify the presence of possible C\({}_{5}\)-C\({}_{10}\) fraction (not observed).  
Runs have been carried out in a wide range of reaction conditions: temperature, 250-350 \({}^{\circ}\)C; pressure, 10-40 bar; space time, 1.25-20 g\({}_{\rm cat}\) h (mol\({}_{\rm C}\))\({}^{-1}\); H\({}_{2}\) + CO + CO\({}_{2}\) feeds, with a H\({}_{2}\)/CO\({}_{\rm x}\) molar ratio of 3-4, and a CO\({}_{2}\)/CO molar ratio of 0-1.
## 1 Introduction
### Paper Organization  
The rest of this paper is organized as follows. Section 2 describes the previous related research and identifies the research contributions of this paper. Section 3 describes the system model in cellular communication and defines the parameters and variables used in this paper. Section 4 analyzes the flying base station's positioning and channel capacity and Section 5 analyzes the capacity gain beyond the stationary base station. Section 6 presents our simulation results for more concrete numerical analyses. We discuss future directions in Section 7 and conclude the paper in Section 8.
## 3 Methodology
### The Development of GRU-LightGBM  
#### 3.4.1 The GRU Method  
The GRU model is a variant of the RNN, which was proposed by [42]. It is used to deal with the problem of gradient vanishing or exposure in long-time-series models [43]. The structure of GRU is similar to that of the standard RNN. GRU data transmission at adjacent time is shown in Figure 2.  
The GRU has gated units; thus, we have the update gate \(z\) and the reset gate \(r\). The update gate is computed by  
\[z=\sigma\left(\omega^{z}\cdot\left[x^{t},ht^{-1}\right]\right) \tag{5}\]  
The reset gate is computed by  
\[r=\sigma\left(\omega^{r}\cdot\left[x^{t},ht^{-1}\right]\right) \tag{6}\]  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline \multicolumn{1}{c}{**Step Number**} & \multicolumn{1}{c}{**Activity**} \\ \hline Step 1: Detrending & Remove the trend component of the \(k\)th iteration \(T_{t}{}^{k}\) from \(Y_{t}\), i.e., \(Y_{t}\leftarrow Y_{t}-T_{t}{}^{k}\). \\ \hline Step 2: Cycle subseries smoothing & Each cycle subseries is smoothed via loess and the smoothing results compose the temporary season series \(C_{t}{}^{k+1}\). \\ \hline Step 3: Low-pass filtering of cycle subseries & Low-pass filtering and loess regression are used to handle the temporary season series \(C_{t}{}^{k+1}\). The resulting series is referred to as \(L_{t}{}^{k+1}\). \\ \hline Step 5: Deseasonalization & The seasonal component of the (\(k+1\))th iteration \(S_{t}{}^{k+1}\) is calculated as \(S_{t}{}^{k+1}=C_{t}{}^{k+1}-L_{t}{}^{k+1}\). \\ \hline Step 6: Trend smoothing & To obtain the trend component of the (\(k+1\))th iteration \(T_{t}{}^{k+1}\), the output of Step 5 is smoothed via loess. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Steps of inner loops [38] (the iteration of the loop (\(k+1\)) is assumed).  
After the reset gate is computed, we reset the transfer variable by  
\[h^{\prime}=tanh\Big{(}\omega\cdot\Big{[}x^{t},h^{t-1}*r\Big{]}\Big{)} \tag{7}\]  
Then, we update the transfer variable \(h^{t}\):  
\[h^{t}=1-z\times h^{t-1}+z\times h^{\prime} \tag{8}\]  
The calculation process of the GRU unit is shown in Figure 3.  
The value range of \(z\) is [0, 1]. The closer \(z\) is to 1, the more information is left in memory; otherwise, more information is forgotten. The update gate \(z\) can perform the forgetting and memorization of gates at the same time.
# Parasitic-Based Active Gate Driver Improving the Turn-On Process of 1.7 kV SiC Power MOSFET
## 5 Conclusions  
In this paper, an AGD for a 1.7 kV/325A SiC MOSFET power module was proposed and validated via simulations and experiments. A mix of multi-level gate voltage and proper measurements of the voltage across the parasitic source inductance proved a positive impact on the turn-on process of the transistor. The AGD was able to deplete the turn-on energy of the power SiC MOSFET by 7% on average and shorten the turn-on cycle without interference in the main circuit. Furthermore, experimental validation showed a higher \(dvds/dt\) rate and, subsequently, measurements confirmed that the chosen method has no negative effect on the drain current oscillations and even suppresses them. Moreover, the device shown in this paper was designed based on off-the-shelf integrated circuits, which makes it a low-cost solution. Additionally, due to use of an integrated boost converter, the AGD needs only one level of the positive supply voltage (15 V) which leads it to be less complex in terms of structure. Finally, the proposed driver can also be used in other models of MV SiC MOSFETs where the Kelvin source and the power source of the device are provided. Further research in the case of turn-off cycle improvement with the shown methods is under consideration.  
Main concept, J.R.; simulation study, B.L.; formal analysis and hardware validation B.L., P.T. and R.K.; writing B.L.; review and editing, P.T., J.R. and R.K.; project administration and funding acquisition J.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Science Center, Poland project no. 2017/27/B/ST7/00970.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to further research plans of the improved device discussed in that paper.  
The authors declare no conflict of interest.
# Analysis of Cascading Failures and Recovery in Freeway Network Under the Impact of Incidents
## 2 Literature Review  
### Cascading Failure  
A cascading failure refers to a phenomenon in which the failure of one or more components initiates a sequence of subsequent failures in other interconnected components, ultimately leading to widespread disruption of the entire system [1; 18]. Compared to other network phenomena that occur following an attack, cascading failures emphasize the continuous and dynamic propagation of disruptions, thereby providing a more accurate reflection of the network's actual performance. This phenomenon was initially investigated in transmission systems responsible for handling different types of flows, such as power grids [19; 20] and communication networks [21].  
Various models have been proposed to study the mechanisms of cascading failures in networks, including the Sandpile model [22] and the OPA model [23]. The Motter-Lai model [10], one of the most commonly adopted frameworks, posits a linear dependence between a node's capacity and its initial load within the network. When a node in the network is attacked and fails, it must be removed, and its load redistributed. The Motter-Lai model must address three fundamental issues: defining the initial load on components, defining the capacity of components, and determining the strategy for load redistribution after component failure. Based on whether the components considered are nodes or edges, the models can be classified into three categories: (1) Node Dynamics Models [24]; (2) Edge Dynamics Models [25]; and (3) Hybrid Dynamics Models of Nodes and Edges [26].  
Many scholars have explored the phenomenon of cascading failures in real-world networks, revealing numerous valuable insights. Previous studies indicate that the cascading overload process is inherently complex [27]. First, the topology of the network determines the connectivity between nodes and the paths of information propagation. Secondly, the physical attributes of nodes and edges, such as node capacity and edge constraints, determine the network's ability to withstand high loads. Additionally, in man-made networks, load-distribution characteristics like time-varying traffic and abnormal traffic models can impact network stability and the conditions that trigger cascading failures. Therefore, developing a cascading failure model for real networks requires a comprehensive consideration of these characteristics to accurately reflect the cascading phenomena within the network.  
To accurately capture cascading failures in real-world traffic networks, models must integrate network topology, component attributes, and realistic behavioral dynamics. However, most existing approaches do not fully consider these aspects. Addressing this gap is essential for advancing cascading failure modeling in transportation systems.
## 4 The Proposed Model
### Validation  
A cluster model was developed using the dataset provided to quantify the diversity that must be implemented in the proposed model. Cross-entropy and minimax loss functions were used to train the discriminator and generator networks, respectively. An 80:20 ratio was used to divide the datasets into two subsets: training and testing. Adaptive moment estimation with \(\beta\)1 = 0.995 and \(\beta\)2 = 0.99 was used as an optimization algorithm, while backpropagation was used as a learning scheme with a learning rate of 0.001 and 600 epochs.
## 3 Results
### Optimization of In Situ Transesterification Reaction
#### 3.2.5 Effect of Extraction Solvent on Gravimetric Lipid Content and Total Fatty Acid Yields  
A relationship between the gravimetric yield and main FAME components of total extract is shown in Figure 5a. The gravimetric yield of crude extract using mixed methanol and chloroform was 26.40% \(\pm\) 0.34%, while it was only 12.90% \(\pm\) 0.23% when adopting petroleum ether and ethyl ether. This phenomenon is likely due to the propensity of polar solvents to penetrate the cell wall more easily than non-polar solvents. The total FAME content as a percent of total extracted materials in methanol and chloroform extraction was 24.11% \(\pm\) 0.53% and in petroleum ether and ethyl ether extraction was only 12.93% \(\pm\) 0.02%, suggesting that methanol and chloroform extraction could obtain more FAME than petroleum ether and ethyl ether extraction. However, the total extract clearly contains a large amount of other lipids and biomolecules soluble in the solvents in addition to FAME.  
We also compared one-step in situ transesterification with the conventional two-step method that involves solvent extraction followed by acid transesterification after measuring the gravimetrical weight of the crude lipids. From Figure 5b, it could be also found that the EPA and other main FAME yields from the two-step method were lower than that for in situ transesterification, indicating that direct in situ transesterification allows for a more complete measurement of FAME as opposed to the two-step method. In the two-step method, the differential EPA yields were 3.26% \(\pm\) 0.10% using methanol and chloroform as the extraction solvent and 0.78% \(\pm\) 0.01% using petroleum ether and ethyl ether. These values are low compared to the in situ yield of 4.02% \(\pm\) 0.17% EPA presumably due to the following reasons: the lipids which do not convert into FAME account for a significant percentage of extracted crude oil; there are unavoidable losses of sample during the procedures and the lipids cannot be completely extracted by this method. The effectiveness of petroleum ether and ethyl ether on the extract-basis is quite low.
# Joint Delay-Doppler Estimation Performance in a Dual Source Context
## 4 Maximum Likelihood and CLEAN RELAX Estimators  
### Dual Source Conditional Maximum Likelihood Estimator  
From the dual source CSM (5) and its pdf (6), the estimator \(\widehat{\mathbf{e}}\) that maximizes the likelihood is such that \((\widehat{\mathbf{\eta}}_{0},\widehat{\mathbf{\eta}}_{1})\) maximizes the projection of the received signal onto the subspace defined by the mixing matrix \(\mathbf{A}\)[22]. As a consequence, the estimated parameters \((\widehat{\rho_{0}},\widehat{\rho_{1}},\widehat{\phi_{0}},\widehat{\phi_{1}})\) directly depend on the delay/Doppler estimation. Finally, \(\widehat{\rho_{n}^{2}}\) is simply the orthogonal projection value:  
\[(\widehat{\mathbf{\eta}}_{0},\widehat{\mathbf{\eta}}_{1}) =\arg\max_{\mathbf{\eta}_{0},\mathbf{\eta}_{1}}\left\|\mathbf{P}_{\mathbf{ A}(\mathbf{\eta}_{0},\mathbf{\eta}_{1})}\mathbf{x}\right\|^{2} \tag{24}\] \[\widehat{\rho_{i}} =\left|\left[\left(\mathbf{A}^{H}(\widehat{\mathbf{\eta}}_{0}, \widehat{\mathbf{\eta}}_{1})\mathbf{A}(\widehat{\mathbf{\eta}}_{0},\widehat{\mathbf{\eta}} _{1})\right)^{-1}\mathbf{A}^{H}(\widehat{\mathbf{\eta}}_{0},\widehat{\mathbf{\eta}}_{1 })\mathbf{x}\right]_{i}\right|\] (25) \[\widehat{\phi_{i}} =\arg\left\{\left[\left(\mathbf{A}^{H}(\widehat{\mathbf{\eta}}_{0}, \widehat{\mathbf{\eta}}_{1})\mathbf{A}(\widehat{\mathbf{\eta}}_{0},\widehat{\mathbf{\eta}} _{1})\right)^{-1}\mathbf{A}^{H}(\widehat{\mathbf{\eta}}_{0},\widehat{\mathbf{\eta}}_{ 1})\mathbf{x}\right]_{i}\right\}\] (26) \[\widehat{\sigma_{n}^{2}} =\frac{1}{N}\left\|\mathbf{P}_{\mathbf{A}(\mathbf{\eta}_{0},\mathbf{\eta} _{1})}^{\perp}\mathbf{x}\right\|^{2} \tag{27}\]  
where the projectors are defined as \(\mathbf{P}_{\mathbf{A}}=\mathbf{A}\left(\mathbf{A}^{H}\mathbf{A}\right)^{-1} \mathbf{A}^{H}\), \(\mathbf{P}_{\mathbf{A}}^{\perp}=\mathbf{I}-\mathbf{P}_{\mathbf{A}}\).  
Even though this estimator is known to be computationally complex, it is also known to be asymptotically efficient (e.g., in the high-SNR regime [34]) for the dual source Gaussian CSM. This property makes it a powerful tool to validate the CRBs derived in Section 3.
## 2 System Model
### Time Jitter Effect  
The incoming, optical pulses are often detected out of sync, causing bit stream errors, especially when the available detection slot is relatively short. In order to estimate the TJ influence on the detected signal, the GGD is engaged [42], supposing that \(T=0\) sec as the ideal detection instant at the received pulse's center and that incoming pulses are distributed around mean value, \(\mu_{T}\), with variance \(\sigma^{2}=\alpha^{2}\Gamma(3/\beta)/\Gamma(1/\beta)\), as follows [42]:  
\[f_{TJ}(T)=\frac{\beta}{2\alpha\Gamma(1/\beta)}\exp\Biggl{[}-\biggl{(}\frac{|T- \mu_{T}|}{\alpha}\biggr{)}^{\beta}\Biggr{]} \tag{7}\]  
where \(\alpha,\beta>0\) are the scale and the shape parameters of the distribution, respectively. Intuitively, the known normal distribution with mean value \(\mu_{T}\) and variance \(a^{2}/2\) is derived when \(\beta=2\).
## 1 Introduction
Thus, investigation of the relationship between seasonal NDVI dynamics and GPP requires quantitative estimation of landscape heterogeneity to improve the estimation of plant functional type (PFT) changes in GPP, and we consider landscape spatial heterogeneity versus homogeneity to be essential in inter-site comparisons of flux, particularly when remotely sensed data are derived from contrasting spatial resolutions. Here, our aim was to evaluate up-scaled in situ and time series data as proxies for ecosystem and landscape-scale GPP. The specific objectives were to:  
1. Describe in situ NDVI (NDVI\({}_{is}\)) seasonal dynamics using Fourier time series modeling and analysis;
2. Compare multi-temporal NDVI\({}_{is}\) time series dynamics derived from radiation measurements using Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data (NDVI\({}_{MD}\)) computed for contrasting spatial scales and biomes;
3. Quantify landscape heterogeneity from flux tower footprints;
4. Use these approaches to model GPP seasonal dynamics.