esa-sceva/satcom-chunk-collection huggingface dataset:
# Entropy-Regularized Federated Optimization for Non-IID Data
## 4 Experimental Setup  
We implemented all algorithms in Python 3.11.4 (Python Software Foundation, Wilmington, DE, USA). Model definition and training were carried out with PyTorch 2.3.0 (PyTorch Foundation, San Francisco, CA, USA) and orchestrated through FedML 0.8.1 (FedML Inc., Palo Alto, CA, USA). GPU acceleration relied on CUDA 12.4 and cuDNN 9.0 (NVIDIACorporation, Santa Clara, CA, USA). Statistical analyses used NumPy 1.26.4 (NumPy Developers, Austin, TX, USA) and SciPy 1.13.0 (SciPy Developers, Austin, TX, USA).  
We conduct experiments on two datasets, UNSW-NB15 and PneumoniaMNIST, using a consistent federated training configuration unless noted otherwise. We simulate a federation of 5 clients and train for 100 communication rounds in all experiments. For each round, every client performs 1 local epoch of training on its own data (mini-batch size: 64) using the Adam optimizer (learning rate: \(1\times 10^{-3}\)) for model updates (for methods where the server uses a different optimizer, local training still follows this protocol). The UNSW-NB15 dataset (network intrusion detection with multiple attack categories) is partitioned in a _non-i.i.d._ manner: we sample a Dirichlet distribution with a concentration of \(\alpha=0.5\) to allocate the training examples of each class among the 5 clients, resulting in heterogeneous client data distributions.
## 4 Experiment and Results Analysis
### The Number of Task-Sharing Layers  
In order to study the optimal value of the number of shared layers \(l\) when BERT is used as the task-sharing layer, this paper conducts a comparative experiment. The experiment is carried out on three datasets using the BERT model, except that the number of task-sharing layers \(l\) is inconsistent, and other parameter settings are consistent. In this paper, the number of layers shared by tasks is experimented on from 1 to 12, the performance of the model under the number of layers shared by different tasks is tested, and the optimal value is determined by comparing the F1 values. The results are shown in Table 4.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Laptop14**} & \multicolumn{3}{c}{**Restaurant14**} & \multicolumn{3}{c}{**Twitter**} \\ \cline{2-10}  & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline Full model & 67.73 & 63.56 & 65.58 & 76.92 & 77.05 & 76.98 & 62.22 & 60.66 & 61.43 \\ w/o ATI & 65.01 & 61.83 & 63.37 & 75.00 & 75.54 & 75.26 & 61.43 & 60.01 & 60.71 \\ w/o MSC & 65.78 & 62.15 & 63.90 & 76.53 & 75.71 & 76.12 & 61.95 & 60.31 & 61.12 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of ablation experiments (%).  
As can be seen from Table 4, the optimal number of task-sharing layers \(l\) is not the same for different datasets. On the Laptop14 dataset, the performance is best at layer 10; on Restaurant14, the performance is best at layer 11; and on the Twitter dataset, the performance is best at layer 10. From this experiment, it can be concluded that the output of the later layers of BERT contains more information features required for this task, but it is not that the higher the number of layers, the better the performance. However, this optimal number of shared layers is susceptible to various influences and cannot find a common value.
## 3 Results and Discussion
### Antioxidative Properties of Yanggaeng  
Table 5 presents the TPC, free radical scavenging rates, and FRAP values of Yanggaeng prepared with CF and alternative sweeteners. The TPC was significantly higher in the Alu and Xyl groups than in the Sug group. Similarly, to the TPC, the DPPH and ABTS radical scavenging activities increased significantly with the addition of alternative sweeteners (Alu and Xyl). Notably, the DPPH scavenging activity of the Xyl group increased 10.5-fold, and the ABTS scavenging activity increased 1.5-fold compared to those of the Sug group, showing the highest radical scavenging ability among all groups. However, stevia-treated Yanggaeng exhibited lower DPPH and ABTS radical scavenging activities than other groups, whereas both the Alu and Ste groups showed significantly lower DPPH and ABTS radical scavenging activities than the Sug group. The FRAP value was higher in the Xyl group than the other groups, whereas both Alu and Ste showed significantly lower values than the Sug group.  
Different alternative sweeteners exhibited distinct antioxidant activities, with Xyl showing superior radical scavenging abilities compared to Sug, Alu, and Ste. The exceptionally high DPPH and ABTS radical scavenging activity of Xyl suggest its strong potential as a potent antioxidant, likely due to its unique structural properties that enhance its ability to neutralize free radicals. Xylose, a five-carbon monosaccharide, contains hydroxyl groups on each carbon atom, which enable strong interactions with free radicals through hydrogen bonding [37]. Moreover, the pronounced crystallization properties of xylose contribute to the formation of a more stable and robust structure, which may further enhance its ability to interact with and neutralize free radicals [37; 38]. Alu showed the highest TPC among the other groups, indicating a higher content of phenolic compounds, which contributed to  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Items** & **Sug** & **Alu** & **Ste** & **Xyl** \\ \hline pH & 5.45 \(\pm\) 0.04 [FOOTNOTE:4]Footnote 4: The  
its antioxidant capacity, although the observed radical scavenging activity was lower than that of Xyl. Interestingly, despite the widespread use of stevia as a natural sweetener, Ste demonstrated the lowest antioxidant activity in all assays. This suggests that although Ste may offer other health benefits, its contribution to antioxidant defense may be relatively limited. The FRAP assay corroborated these results, with the Xyl exhibiting the highest reducing power. Overall, these findings indicated that xylose may be a promising sugar substitute in terms of its antioxidant potential, whereas stevia, although commonly used, may not provide significant antioxidant benefits.
## 2 Theory  
### The Cause of Branch Ambiguity  
The Nicolson-Ross-Weir (NRW) method is a well-established procedure first introduced to characterize electromagnetic materials [7] and later employed in the MM field [31]. Using the NRW method, the effective complex refractive index \(N_{eff}(\omega)\) of an MM slab with effective thickness \(d_{eff}\) is recovered via the inversion of the following relation (the \(e^{-i\omega t}\) time convention is assumed and suppressed):  
\[e^{iN_{eff}(\omega)k_{0}d_{eff}}=\frac{S_{21}(\omega)}{1-S_{11}(\omega)R_{01} (\omega)} \tag{1}\]  
in which \(k_{0}\), \(S_{11}(\omega)\), \(S_{21}(\omega)\), and \(R_{01}(\omega)\) are, respectively, the free-space propagation constant, scattering parameters, and the reflection coefficient belonging to the effective medium slab related to the MM at hand [30]. Routinely, Equation (1) is solved by using the complex logarithm \(LOG(\cdot)=log(\cdot)+2p\pi i\), where \(log(\cdot)\) is the principal logarithm and \(p\in\mathbb{Z}\) is the _branch index_[32], from which we obtain, for \(\text{Re}[N_{eff}(\omega)]=\kappa_{eff}(\omega)\) and \(\text{Im}[N_{eff}(\omega)]=n_{eff}(\omega)\), the following expressions.  
\[n_{eff}(\omega) =\frac{1}{k_{0}d_{eff}}\mathsf{Im}\bigg{[}log\bigg{(}\frac{S_{21}( \omega)}{1-S_{11}(\omega)R_{01}(\omega)}\bigg{)}\bigg{]}+2p\pi \tag{2}\] \[\kappa_{eff}(\omega) =-\frac{1}{k_{0}d_{eff}}\mathsf{Re}\bigg{[}log\bigg{(}\frac{S_{21} (\omega)}{1-S_{11}(\omega)R_{01}(\omega)}\bigg{)}\bigg{]} \tag{3}\]  
Because of the presence of term \(p\), which ranges in the set of integer numbers, refractive index \(n_{eff}(\omega)\) turns out to be not uniquely determined, giving rise to the so-called _branch ambiguity issue_[18; 19].
## 4 Methodology  
This section is divided into two main segments: feature selection and feature ranking. In feature selection, the goal is to remove irrelevant or unhelpful inputs while maintaining the model's predictive accuracy. This process is particularly useful when the objective is to spot the fewest features yielding strong performance, thus improving efficiency and interpretability. Discarding unnecessary features makes the model less prone to complexity and potential overfitting.  
In feature ranking, the emphasis is on identifying the influence of each feature in predicting the target variable. Ranking methods help clarify the relative importance of different inputs, offering insights into why the model behaves a certain way. This level of explanation is especially beneficial in avoiding training pitfalls such as overfitting or bias since it reveals which features drive decisions. These two approaches facilitate the feature set while ensuring the most informative predictors remain, ultimately leading to more robust and understandable models.  
Both the PV output data and meteorological variables were collected at 15 min intervals over 2 years (August 2022-August 2024). To preserve the temporal integrity of the time series, we used a chronological split: the first 90% of the dataset was used for training, and the remaining 10% was reserved as unseen test data. This block-based split prevents data leakage and better reflects real-world deployment, where future forecasts are made based on past observations.
# Interactive Graph Stream Analytics in Arkouda
## 7 Experiments
### Experimental Results
#### 7.3.2 Performance
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Graph Name** & **Factor** & **Stream Time (s)** & **Exact Time (s)** & **Speedup** \\ \hline delaunay\_n12 & 4 & 2.04689 & 2.72048 & 1.33 \\ \hline delaunay\_n12 & 8 & 1.8969 & 2.72048 & 1.43 \\ \hline delaunay\_n12 & 16 & 1.77604 & 2.72048 & 1.53 \\ \hline delaunay\_n13 & 4 & 2.67814 & 4.70596 & 1.76 \\ \hline delaunay\_n13 & 8 & 2.27257 & 4.70596 & 2.07 \\ \hline delaunay\_n13 & 16 & 2.0258 & 4.70596 & 2.32 \\ \hline delaunay\_n14 & 4 & 3.94324 & 8.91793 & 2.26 \\ \hline delaunay\_n14 & 8 & 3.04275 & 8.91793 & 2.93 \\ \hline delaunay\_n14 & 16 & 2.60059 & 8.91793 & 3.43 \\ \hline delaunay\_n15 & 4 & 6.34795 & 17.4302 & 2.75 \\ \hline delaunay\_n15 & 8 & 4.44783 & 17.4302 & 3.92 \\ \hline delaunay\_n15 & 16 & 3.6619 & 17.4302 & 4.76 \\ \hline delaunay\_n16 & 4 & 11.2135 & 34.2433 & 3.05 \\ \hline delaunay\_n16 & 8 & 7.29339 & 34.2433 & 4.70 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Speedup of Stream graph processing. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Filename** & **Head Local Access Ratio** & **Mid Local Access Ratio** & **Tail Local Access Ratio** \\ \hline delaunay\_n10 & 0.736364 & 0.75 & 0.642045 \\ \hline delaunay\_n11 & 0.578635 & 0.789894 & 0.707775 \\ \hline delaunay\_n12 & 0.737226 & 0.906832 & 0.878136 \\ \hline delaunay\_n13 & 0.710651 & 0.902913 & 0.91839 \\ \hline delaunay\_n14 & 0.661084 & 0.914058 & 0.941802 \\ \hline delaunay\_n15 & 0.699193 & 0.963969 & 0.947161 \\ \hline delaunay\_n16 & 0.685931 & 0.968672 & 0.953034 \\ \hline delaunay\_n17 & 0.692844 & 0.972879 & 0.963517 \\ \hline delaunay\_n18 & 0.693935 & 0.983028 & 0.975103 \\ \hline ca-HepTh.txt & 0.687343 & 0.645873 & 0.650115 \\ \hline ca-CondMat.txt & 0.630154 & 0.663701 & 0.6764 \\ \hline ca-AstroPh.txt & 0.61735 & 0.615687 & 0.615749 \\ \hline email-Enron.txt & 0.376773 & 0.632443 & 0.785357 \\ \hline ca-GrQc.txt & 0.721421 & 0.671429 & 0.687907 \\ \hline ca-HepPh.txt & 0.643376 & 0.648718 & 0.593803 \\ \hline loc-brightkite\_edges.txt & 0.71338 & 0.603389 & 0.721972 \\ \hline amazon & 0.731695 & 0.723097 & 0.738042 \\ \hline dblp & 0.605114 & 0.711748 & 0.830386 \\ \hline youtube & 0.669736 & 0.671671 & 0.9342 \\ \hline jj & 0.570621 & 0.728383 & 0.993926 \\ \hline orkut & 0.556868 & 0.569822 & 0.861902 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Local Access Ratio of different stream graphs.
## 2 Experimental Procedure and Analysis
### Judd-Ofelt Analysis  
In order to study the effect of the Tm\({}^{3+}\) ion concentration on the radiative lifetimes for the \({}^{3}\)F\({}_{4}\) and \({}^{3}\)H\({}_{6}\) levels, Judd-Ofelt (J-O) theory was employed. According to the theory, the effect of the electric-dipole transition from the ground state \(SLJ\) to an exited state \(S^{\prime}L^{\prime}\) on the integrated absorption coefficient \(\left(\Sigma_{\mu}\right)_{calc}\) of the rare earth ions can be calculated by using the following equation [30; 31],  
\[\left(\Sigma_{\mu}\right)_{calc}=\frac{8\pi^{3}e^{2}}{3 ch}\frac{\left(n^{2}+2 \right)^{2}}{9n}\frac{\overline{\lambda}}{(2J+1)}N_{o}x\sum_{t=2,4,6}\Omega_{t }\Big{|}\Big{\langle}SLJ\|U^{(t)}\|S^{\prime}L^{\prime}J^{\prime}\Big{\rangle} \Big{|}^{2} \tag{1}\]  
Here, \(N_{o}\) is the ion concentration in the gain medium, \(e\) is the electron charge, \(c\) is the speed of light, \(h\) is [PERSON]'s constant, \(n\) is the refractive index, \(\overline{\lambda}\) is the average wavelength of the absorption band, \(J\) is the total angular momentum quantum number, the \(\Omega_{t}\)'s are the J-O intensity parameters, \(SLJ\) and \(S^{\prime}L^{\prime}\)' are the ground state and excited state of the dipole transition, respectively, and \(U^{(t)}\) is the doubly reduced matrix element of the unit tensor operator. The \(\Omega_{t}\)'s can be estimated by using the measured integrated absorption coefficient \(\left(\Sigma_{\mu}\right)_{\text{exp}}\) and the matrix elements in [32]. \(\left(\Sigma_{\mu}\right)_{\text{exp}}\) can be obtained by calculating the integral under the absorption spectrum for each transition Then, \(\Omega_{t}\)'s can be determined by fitting the experimental integrated absorption coefficient to theoretically calculated value. Then, the spontaneous emission probability (\(A(JJ^{\prime})\)) of the dipole transition from \(SLJ\) to \(S^{\prime}L^{\prime}\) can be calculated by using the equation,  
\[A(J,J^{\prime})=\frac{64\pi^{4}e^{2}}{3h(2J+1)}\frac{n(n^{2}+2)^{2}\overline{v }^{3}}{9}x\sum_{t=2,4,6}\Omega_{t}\Big{|}\Big{\langle}SLJ\|U^{(t)}\|S^{\prime }L^{\prime}J^{\prime}\Big{\rangle}\Big{|}^{2} \tag{2}\]  
In Equation (2), \(\overline{v}\) is the average wave number of the corresponding dipole transition. By using the calculated \(A(JJ^{\prime})\) from the \(f\)th excited state to all possible \(J^{\prime}\) states, the radiative lifetime \(\tau_{R}\) for the \(f\)th excited state can be calculated from  
\[\tau_{R}=\frac{1}{\sum\limits_{j}A(J,J^{\prime})} \tag{3}\]
## 2 Material and Methods
### Parallel Bayesian Optimization  
The concept has been introduced by [PERSON] (2018) and later developed by [PERSON] et al. (2018) before being popularized by [PERSON] et al. (2018). The general idea of BO is to fit a probabilistic predictive model of the black-box objective function \(f\)--the simulator--in order to guide the optimization process. Indeed, \(f\) being time-consuming, we cannot afford to query a large number of simulations. Therefore, the surrogate model is used to evaluate the utility of a candidate point before it is evaluated with the costly simulator. This utility measure is often called AF, but also Infill Criteria (IC) or figure of merit. It uses the predicted value of the surrogate as well as the prediction variance provided by probabilistic models such as Gaussian Processes (GP). One famous example of AF is the Expected Improvement (EI) used in Efficient Global Optimization (EGO) [29]. BO operates in a loop composed of (i) fitting a metamodel, (ii) searching for the most valuable candidate(s) to simulate, (iii) simulation of the candidate(s). The three steps are referred to as a cycle.  
BO involves two key elements: the definition of a surrogate model \(\mathcal{M}\) that provides a prediction \(\hat{y}_{cand}\) for any candidate point \(x_{cand}\) as well as a measure of uncertainty \(\sigma(x_{cand})\), and an AP that proposes a (batch of) valuable point(s) for evaluation.  
Many AFs have been proposed over the past decades. [PERSON] et al. [6] propose a classification of the different AFs: (i) optimistic strategy--using best case scenario regarding the uncertainty such as Gaussian Process Upper Confidence Bound [30]; (ii) Improvement Based--using the improvement defined as \(I(\mathbf{x})=max(f_{min}-f(\mathbf{x}),0)\), such as Probability of Improvement [27], Expected Improvement [29; 31] (EI), Scaled EI [32]; and Information Based strategy--focusing on the information rather than the improvement, such as Thompson Sampling, Entropy Search [33], Predictive Entropy Search [34] or Max-Value Entropy Search [35].  
Exploiting parallel computing requires being able to provide a batch of candidate points for batch parallel evaluation. In addition to intrinsic multi-point criteria such as \(q\)-EI, various strategies are adopted to achieve this goal. One may choose to rely on a single point criterion either using it several times [13; 36], or trying to localize distinct local optimal values of the AFs [37; 38]. It is also possible to rely on multiple single points criteria hoping that they yield distinct candidates [11; 39] or even optimize them in a multi-objective way to find a compromise between ICs [40; 41]. Five different APs are investigated in this paper and presented in the following.
## 4 Performance Evaluation
### Result Evaluation
#### 4.3.1 Throughputs of the Network \(\rho_{g}\)
WAKE-up communicating packets and ACK communicating packets make up the majority of the energy that the inter-cluster communicating nodes receive. To be clear, the energy received by the central node in the cluster consists of both the energy that occurs from the communicating data packets and the energy from the active communicating data packets. To control both space and time efficiently and avoid conflicts and retransmissions, the chosen-MAC protocol includes an ordered cluster structure that improves network performance by using a time synchronization plan and energy-efficiency technique. The chosen MAC protocol performs better than other protocols, including TLPC, MACA-PC, and CAPC, under comparable traffic load situations [55; 56]. The transmission energy for communicating data packets is constantly altered by the suggested protocol's two iterations, one utilizing the algorithm known as greedy and another utilizing the greedy technique in response to the interference conditions within the area. Adaptive distribution of the energy reduces packet losses. However, in an environment of numerous interferences, the TLPC, MACA-PC, and CAPC protocols are unable to allocate sufficient energy for reliable DATA transmission, which results in greater packet losses. When packets are lost, there is an increase in delay for successful packet delivery, which lowers network performance.
## 5 Performance Evaluation  
### Internet of Things Virtual Network Functions Requirements  
The aforementioned IoT scenarios involve the use of different types of VNFs. Table 2 reports the main features of the proposed architecture, in relation to some example VNFs.  
The load balancer is a common middlebox to be softwarized in order to improve scalability and reduce management costs. NetBricks provides optimized packet processing and forwarding functions for developing load balancers with low latency, resulting in reduced memory overhead due to Rust runtime safety checks, which can be eliminated statically in some cases (e.g., where bounds can be placed on the index statically) [38]. Moreover, the solution of Kata Containers, accelerated by the Firecracker VMM, produces fast and high density deployment, suitable to satisfy the load balancer requirements for replicability and granularity.  
IoV requires services related to cooperation and communication between traffic participants, in order to optimize driving behavior for both automated systems and human drivers. To this end, wireless communication systems, such as C-V2X, can be used to exchange information about speed, position, intended trajectories/maneuvers, geolocation and other helpful data between vehicles through messages which can be periodical (CAM) or event-based (DENM) [23]. The server orchestrating these messages should be able to scale quickly when there is a high volume of traffic. To this aim, Firecracker VMM entails a rapid startup time (125 ms [37]). The stronger isolation introduced by Kata Containers provides a safer environment for the management of information messages in a security-sensitive context like IoV. Also, the extra latency introduced by the Kata nested virtualization may preclude the usage of this architecture in uRLLC services. However, there are C-V2X services, such as an alerting one [43], with timescales in the order of seconds. They are compliant with the performance of the proposed architecture in terms of both startup time of new instances and service time for data plane traffic.  
Video streaming and multimedia content delivery are also commonly present in the IoV scenario [23], for both remote driving and entertainment services. The required data rate is around 2-20 Mbps [44], which can be sustained by the high processing rate of the NetBricks optimized environment [38].  
Since, in IoT scenarios, security is critical, a common service is firewalling. The proposed architecture comes with a special focus on security and isolation. In addition to the isolation boundaries introduced by Kata Containers, the Firecracker minimal VMM reduces the potential attack surface. Furthermore, as mentioned above, the NetBricks framework makes use of zero copy soft isolation and Rust to provide memory and packet isolation [38].
# Overall Design of Satellite Networks for Internet Services with QoS Support
## 2 Proposed Design of Satellite Networks
### Design for the QoS Support  
The proposed design for the QoS support is as shown in Figure 3. To provide Internet services with the QoS support, the QoS class should be defined for them. The priority and characteristic for services should be then reflected in the scheduling and the resource allocation in the link and the network layers. For example, the resource for delay-sensitive services such as VoIP and video streaming services should be allocated by the CRA and the RBDC to minimize the delay [23; 29; 31]. Generally, the definition of the QoS class is different with each layer. The QoS mapping is thus needed between the network and link layers [1; 23]. In the proposed design, the NMS manages the QoS class and the QoS policy such as the service priority, the maximum data rate, the QoS mapping, etc. This information is forwarded to the NCC and UTs. In the NCC and UTs for the QoS support, this information is used in the DAMA controller, the DAMA agent, and schedulers [1; 23; 29; 31; 29; 30]. The data rate in the UT is limited by the policer in UTs to prevent the traffic congestion because the maximum data rate can be changed according to the type of terminal. For example, the maximum data rate can be changed because the maximum equivalent isotropically-radiated power (EIRP) is changed by the antenna size in the satellite network. In the log-on process for the QoS support, the NMS conducts the admission control of UTs by rejecting the log-on request message based on the total number of UTs and the sum of the maximum data rate for UTs. To design the QoS policy, the user requirement for each application is reflected by the QoS policy, as well as the goodput, and satisfaction for the user experience should be considered according to various environments for the application characteristics, the traffic pattern, and the protocol characteristics. Thus, in Section 3, we practically define the satisfaction ratio and the goodput for the user experience. We also measure the performance for various environments by varying each factor such as the application service type, the traffic load, the burstiness of the traffic, the number of users, and the resource allocation method in the link layer.