esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Set-Up
### Subject Grouping Based on the Classification Accuracy of MI Responses  
To assess model robustness and quantify predictive uncertainty, we integrate MCD into the evaluated DL architectures referred to as (e.g., _MCD-EEGNet_) [23]. MCD enables uncertainty estimation by simulating multiple stochastic forward passes through the network during inference. This approach provides a more reliable evaluation of model behavior under variable conditions, such as the presence of defective channels during EEG acquisition, thereby enhancing the reliability of predictions. Specifically, we implement MCD by introducing a dropout layer prior to the first Conv2D layer. This layer applies a binary dropout mask to the multi-channel EEG input, randomly disabling channels over time to simulate structural variability and improve generalization.  
To interpret the elicited brain activity, we consider the influence of BCI illiteracy [78], which suggests that classification frameworks may fail to accurately decode brain responses from certain individuals, resulting in poor performance. To address this issue, we cluster subjects based on inter-subject variability in neural responses, grouping individuals with similar classification accuracies.  
The rationale behind this grouping is that classification performance may reflect an individual's ability to engage in MI tasks: the more accurately a subject distinguishes  
Figure 4: Detailed architectures of DL models evaluated for binary motor imagery (MI) classification. MCD is applied as the first layer to the input EEG data to enhance robustness, while LayerCAM activations are extracted from the Conv2D layer to provide spatially meaningful interpretability. All architectures use a Softmax activation function after the final dense layer for label prediction. Nodes represent individual layers, with arrows indicating their connections. Different colors are used to distinguish layer types, and outlines emphasize specific layers within the same category.  
between MI conditions, the more effectively their brain network may be functioning during the task. For evaluation, we order the individuals by accuracy, defining two groups:  
* _Group I_: Well-performing subjects with binary classification accuracy above 70%, as proposed in [32].
* _Group II_: Poor-performing subjects with accuracy below this threshold. As the performance measure, classifier accuracy \(a_{c}\in[0,1]\) is computed as \[a_{c}=\frac{T_{P}+T_{N}}{T_{P}+T_{N}+F_{P}+F_{N}},\] where \(T_{P}\), \(T_{N}\), \(F_{P}\), and \(F_{N}\) denote true positives, true negatives, false positives, and false negatives, respectively.  
To assess performance beyond random chance, the [PERSON]'s kappa coefficient \(\kappa\) is also calculated as follows:  
\[\kappa=\frac{a_{c}-p_{e}}{1-p_{e}},\]  
where \(p_{e}=0.5\) for binary classification problems.  
For validation, the training trial set is randomly partitioned using stratified 10-fold cross-validation. This procedure is repeated ten times, each time rotating the test and training subsets to ensure stability and generalizability of the reported performance metrics.
## 4 Experiments
### Cybersecurity Risk Assessment
GPS spoofing vulnerability  
An adversary has the capability to fabricate GPS signals with stronger signal strength in order to deceive the GPS system of autonomous driving vehicles that heavily rely on positioning information, posing substantial cybersecurity hazards. GPS attacks based on Software-Defined Radio tool are comparatively straightforward and have extensive coverage areas where they can be executed effectively. Consequently, the cybersecurity risks associated with such attacks are considerably high. Ultrasonic Radar jamming vulnerability  
When there is no obstacle behind the vehicle, the attacker successfully fabricates a false obstacle to deceive the sensor of the vehicle, leading it to mistakenly perceive an obstacle behind it. Although this attack is relatively simple to execute, it typically targets the parking assistance function and poses a minimal cybersecurity risk. Traffic sign recognition system spoofing vulnerability  
Under adversarial sample attacks, the vehicle fails to accurately recognize traffic signs on the road or even detect their presence. This attack significantly impacts functions such as automatic cruise control and advanced assisted driving in high-speed scenarios, posing substantial risks to personal safety and property. While constructing sophisticated adversarial samples is highly challenging, once these samples targeting specific vehicles are leaked, any attacker can launch an attack without physical access to the vehicle. Therefore, this vulnerability presents a high cybersecurity risk. PKE relay vulnerability  
Attackers can exploit relay attack devices to gain unauthorized access to vehicle doors and start the engine, facilitating vehicle theft. Despite the challenging development of such attack equipment, if assailants manage to acquire it through illicit means, they can effectively execute attacks and pilfer target vehicles without requiring specialized knowledge. This poses significant risks in terms of substantial property losses and heightened cybersecurity vulnerabilities.
# A Presentation of Ionic Liquids as Lubricants: Some Critical Comments
## 5 Some Applications
### Steel Surfaces
#### 5.1.5 Magnetic Ionic Liquids  
Magnetic ionic liquids (MILs), ionic liquids, generally, with an organic cation and a magnetic anion (i.e., with a single electron organic free radical or metal ion complex), may be easily magnetized and used as lubricants of steel-steel sliding pairs. In contrast to traditional magnetic fluid lubricants (MFs), MILs have an excellent high temperature stability and a very low volatility, giving them important tribological properties [105]. For this reason, recently, [PERSON] et al. [106] synthesized a magnetic ionic liquid ([C\({}_{6}\)mim]\({}_{5}\)[Dy(SCN)\({}_{8}\)]) in which the cation is 1-hexyl-3-methylimidazolium and a dysprosium (Dy) is the metallic centre of the anion complex (Figure 12).  
The authors tested their MIL on rubbed steel surfaces, comparing it with a classical MF composed of DIOS (dioctyl sebacate) and Fe\({}_{3}\)O\({}_{4}\). The results of the coefficient of friction (COF) conducted at different temperatures showed a better and permanent friction reduction by the synthesized MIL at high temperatures and loads. Moreover, the wear volumes of a steel-steel sliding contact lubricated by the MIL and MF at variable temperatures and loads were measured, demonstrating the almost constant results of wear inhibition only by lubrication with [C\({}_{6}\)mim]\({}_{5}\)[Dy(SCN)\({}_{8}\)].
## 4 Applications of Fe\({}_{3}\)O\({}_{4}\) Nanoparticles
### Environmental Applications
In general, Fe\({}_{3}\)O\({}_{4}\) nanoparticles function as efficient magnetic carriers for absorbents or photocatalysts, which provide recyclability, reusability, nontoxicity, and inexpensive materials for large-scale wastewater treatments.
## 4 Test Results Using the IDHIL Simulator  
For the evaluation of the CED guidance and A-ECMS mentioned in Section 3, simulations were performed on a test bench consisting of the IDHIL simulator environment from Section 2. Only one driver participated to minimize driver variables. The driving path and traffic conditions were the same as in the scenario in Section 2.3. For comparison, the case with no CED guidance and the ECMS with a fixed EF value (normal PHEV) is also included. The simulation was performed four times each for both the connected PHEV and normal PHEV cases. The simulation results are shown below in Figure 15 for vehicle speed, Figure 16 for SOC, and Figure 17 for fuel consumption.  
Figure 14: Workflow of the A-ECMS with target SOC.  
Table 2 shows the results of the simulation and the fuel consumption calculated from it. Here, we can see the total driving range, the final SOC of the battery at the end of the simulation, and the fuel consumption. To calculate the PHEV's fuel economy, we calculate the equivalent fuel consumption \(V_{fuel,e}\) based on the battery usage as follows.  
\[V_{fuel,e}=\frac{\left(SOC_{init}-SOC_{final}\right)V_{bat}Q_{bat}}{\alpha} \tag{19}\]  
where \(\alpha\) (=8.9 kWh/L) is the conversion factor for converting 1 L of gasoline to electrical energy [24]. \(SOC_{init}\) and \(SOC_{final}\) are the initial and final SOCs. The numerator is the electrical energy consumption as the SOC changes. The total fuel consumption was calculated by adding the fuel consumption of the engine and \(V_{fuel,e}\). The average fuel consumption of the connected PHEV was 29.31 km/L, while the average fuel consumption of the normal PHEV is 29.31 km/L.  
Figure 16: SOC results: (**a**) connected PHEV; (**b**) normal PHEV.  
Figure 17: Total fuel consumption results: (**a**) connected PHEV; (**b**) normal PHEV.  
Figure 15: Vehicle speed results: (**a**) connected PHEV; (**b**) normal PHEV.  
These results show the difference between a connected PHEV and a normal PHEV. It shows the difference in driving speed depending on whether CED guidance is used or not. We can also see the difference in fuel and battery usage tendencies with and without the A-ECMS. This is mentioned in more detail in Sections 4.1 and 4.2
## 4 Discussion
This suggestion is highly acceptable since it is known that the mucus layer of the reproductive tract has a surface negative charge, whereas the GnRH-ChNPs developed in our study had a positive charge (+8.0 mV). This difference in charge might increase the adhesion between the GnRH-ChNPs and the mucosal layer of the reproductive tract, allowing sustained GnRH release [11; 29]. The sustained release of GnRH for a longer time may extend the ovulation time, leading to asynchrony of ova shed time. The variation in ovulation time might have resulted in the occurrence of fertilization for a longer period, and consequently a variation in embryo developmental stages. In context, it is worthy to note that GnRH is synthesized by preimplantation embryos in many species including rabbits [30]. Furthermore, GnRH has a pivotal role in the pre-implantation division and implantation of the embryo. [PERSON] et al. [31] found that pre-implantation embryonic development could be enhanced by incubation with increasing concentration of the GnRH agonist, while GnRH antagonist had a deleterious effect. Thus, intravaginal deposition of GnRH may facilitate a GnRH binding with its receptors on preimplantation embryos, affecting their developmental stage. Analysis of progesterone (P\({}_{4}\)) and estradiol (E\({}_{2}\)) throughout pregnancy revealed a clear reduction in P\({}_{4}\) concentrations in group HV compared to groups C and HM. In rabbits, follicular and placental E\({}_{2}\) is the main luteotropic factor supporting CL maintenance [15; 32]. This explains the obvious increase in serum P\({}_{4}\) concentration in group HM, as E\({}_{2}\) concentration was the highest among groups. On the other hand, the concentrations of E\({}_{2}\) showed a similar trend in groups C and HV, which indicates the implication of other factors in the maintenance of CL and P\({}_{4}\) concentration. In rabbits, the pre-implantation embryo can synthesize chorionic gonadotrophins, which play a role in the maternal embryo interface [33]. The failure of this process leads to luteolysis and early embryonic loss [34]. In the present study, a proportion of the embryos, about 25%, of group HV had advanced developmental stages, while the rest of the embryos were at 48 h at the typical developmental stage. The lack of synchrony between advanced developed embryos and normally developed embryos might drive to a disturbance in the maternal-embryonic cross talking, leading to luteolysis, P\({}_{4}\) reduction, and increased implantation failure and embryonic loss. Such hormonal and embryonic development disturbances may explain the lowest conception and parturition rates and litter size of group HV.
# A Survey: Network Feature Measurement Based on Machine Learning
## 3 Network Measurement Based on ML Method
### Throughput  
Throughput, which is a key metric of network performance, is the quantity of data successfully transmitted per unit of time for networks, devices, ports, and other facilities. The internal and external network interface hardware of the network equipment, as well as the effectiveness of the program algorithm, especially the program algorithm, are the key determinants of throughput. The best throughput method is one which, for a certain TCP transmission size, can be accomplished via a particular path between two end hosts. Creating a solution that accurately predicts TCP performance for arbitrary and potentially highly dynamic end-to-end links is the fundamental difficulty [42].  
[PERSON] et al. [42] generated throughput estimations by using SVR's ability to take various inputs. The actual throughput of the target TCP stream was measured on-site in comparison to the predicted throughput produced by numerous instances of our SVR-based algorithms that were trained using various combinations of path parameters. Throughput prediction can increase by up to three times when path attributes are taken into account using SVR-based tools in comparison to history-based predictors. [PERSON] et al. [68] provided a paradigm for deep neural network-based traffic-based throughput classification. Using t-distribution random neighbor embedding, it presents a real dataset of 252 million traffic records amassed in a single week (t-SNE). Instead of dividing the expected bit rate into elephant and mouse flows, their objective was to divide it into three categories. The number of nodes, number of layers, and learning rate were three super parameters that were optimized. The results of the trial revealed that during a continuous period of one week, the prediction's accuracy averaged 82%. As a benchmark, [PERSON] et al. [24] measured the throughput of each network slice's cellular links. He then utilized the DTs model to better understand how root cause analysis decisions are made. The chosen method for processing tabular datasets, particularly an MLP, is an ANN. To forecast the available throughput in a non-independent 5G network based on network slices, a non-network intrusive machine learning approach is suggested. To predict the link throughput using a real traffic dataset, [PERSON] et al. [69] evaluated the effectiveness of the LSTM network and ARIMA model. For the test split, they assessed the duration of four epochs and three iterations of each model. In each instance, the LSTM network's average error was much lower than the ARIMA model's average error. Currently, the majority of studies using machine learning to estimate throughput only analyze the artificial network path, only taking into account the particular network path, and have not been tested in a big flow and scientific traffic environment.
# A Bayesian Nonparametric Learning Approach to Ensemble Models Using the Proper Bayesian Bootstrap
## 6 Conclusions  
In this paper a new approach in ensemble tree modelling using informative Bayesian bootstrap is proposed. In ensemble methods based on bagging, each data resample is generated through [PERSON]'s bootstrap. This procedure is equivalent to consider the multinomial distribution as the prior distribution for the data generating process which assigns to each observation equal probability of being sampled. Under the Bayesian framework the natural extension is the bootstrap technique proposed by [PERSON] which considers the Dirichlet distribution as a prior distribution for the data generating process. It is well known that [PERSON]'s and [PERSON]'s bootstraps are strongly dependent on the observed values and do not take into consideration any prior opinions.  
In this paper the Proper Bayesian Bootstrap procedure is proposed in ensemble tree modelling. This procedure allows to introduce expert opinions through the definition of the prior parameter, thus overcoming the main drawbacks of the classical ensemble models which only consider the data without any prior opinion. However, if the prior distribution is not properly chosen, giving high weight to the prior could introduce noise in the data, thus loosing in model performance.  
Obtained results suggest that, with the introduction of pseudo-samples in the data and a proper choice of prior weight, the final model can gain in terms of stability without loosing in model performance. These results are highlighted both in simulated and real datasets.  
Data curation, M.G.; Methodology, C.B.; Software, C.B.; Supervision, S.F. and P.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 6: Nonparametric confidence intervals on obtained results in real dataset.
## 5 Numerical Results and Discussion
### Network Settings  
#### 5.1.1 Single-Slot Network Settings  
For the single-slot ISAL networks, we present four networks with different topologies in the area of \(200~{}\text{m}\times 200~{}\text{m}\), as shown in Figure 7. The solid red circles represent the anchors, the solid green triangles represent the radars, and the solid black pentagram represents the target. These four networks all contain two anchors, two radars, and one target.
# A Deep Learning Approach for MIMO-NOMA Downlink Signal Detection
## 3 Deep-Learning Scheme Based on the MIMO-NOMA System
### Feasibility Analysis of DL in a MIMO-NOMA System
For UE1, the MMSE detection process can be expressed as:  
\[l_{1}^{1}=\underset{i}{\arg\min}\ \|\mathbf{Y}^{1}\mathbf{\bar{H}}^{1}-\sqrt{P_{1}}X_{i }\|, \tag{20}\]  
\[\mathbf{\bar{H}}^{1}=\left(\mathbf{H}^{1}\right)^{H}\left(\mathbf{H}^{1}\left(\mathbf{H}^{1} \right)^{H}+\sigma_{1}^{2}\mathbf{I}\right)^{-1}. \tag{21}\]For UE2, the information of UE1 should be extracted first, and the process is similar to (20):  
\[l_{1}^{2}=\arg\min_{i}~{}\|\mathbf{Y}^{2}\mathbf{\hat{H}}^{2}-\sqrt{P_{1}}X_{i}\|, \tag{22}\]  
\[\mathbf{\tilde{H}}^{2}=\left(\mathbf{H}^{2}\right)^{H}\left(\mathbf{H}^{2}\left(\mathbf{H}^{2} \right)^{H}+\sigma_{2}^{2}\mathbf{I}\right)^{-1}. \tag{23}\]  
A reconstructed signal can be denoted as:  
\[S_{l_{1}^{2}}=A\exp{(j\omega t+\varphi_{l_{1}^{2}})}. \tag{24}\]  
Then, the reconstructed signal is subtracted from the received signal:  
\[\mathbf{\hat{Y}}^{1}=\mathbf{Y}^{2}, \tag{25}\]  
\[\mathbf{\hat{Y}}^{2}=\mathbf{\hat{Y}}^{1}-\sqrt{P_{1}}S_{l_{1}^{2}}\mathbf{H}^{2}. \tag{26}\]  
Decoding the UE2 signal:  
\[l_{2}^{2}=\arg\min_{i}~{}\|\mathbf{\hat{Y}}^{2}\mathbf{\hat{H}}^{2}-\sqrt{P_{2}}X_{i}\|. \tag{27}\]  
As the process mentioned above, for the \(K\)-th UE, the detection can be shown as:  
\[\mathbf{\hat{Y}}^{K}=\mathbf{\hat{Y}}^{K-1}-\sqrt{P_{K-1}}S_{l_{K-1}^{K}}\mathbf{H}^{K}, \tag{28}\]  
\[l_{K}^{K}=\arg\min_{i}~{}\|\left(\mathbf{\hat{Y}}^{K}\right)\mathbf{\tilde{H}}^{K}- \sqrt{P_{2}}X_{i}\|. \tag{29}\]  
From Equations (20), (27), (29), it can be seen seen that the final classification result can be expressed in the form:  
\[\mathbf{\bar{H}}^{1}=\left(\mathbf{H}^{1}\right)^{H}\left(\mathbf{H}^{1}\left(\mathbf{H}^{1} \right)^{H}+\sigma_{1}^{2}\mathbf{I}\right)^{-1}. \tag{21}\]0  
where \(\mathbf{Y}_{k}\) is a constant matrix, \(\mathbf{b}_{k}\) is a constant vector, and \(f_{i}\) represents some form of nonlinear function. Comparing with (10), it can be found that the DNN detector is capable of replacing the traditional SIC method in a MIMO-NOMA system and is even more powerful due to its ability to find the optimal solution with the data-driven mode.
## 3 Results and Discussion
### Video Spectral Comparator (VSC) Studies
As can be seen in Figure 5, samples of subgroup I and subgroup II possessed no IRL under the first filter with a bandwidth of 400-485 nm, while possessing a strong IRL across the five following filters and an IRL decay beginning from the seventh filter to the tenth. Figure 6 shows the IRL images of the solvated ink samples of subgroups III and IV under white light and different preset optical excitation filters. The solvated ink samples of subgroup III exhibited similar behavior to that observed in the case of subgroups I and II. On the other hand, solvated ink samples of subgroup IV exhibited unique IRL characteristics. The sample SA1a possessed enhanced solubility, while ZR1a and TO3 samples exhibited poor solubility in ethanol. Consequently, those solvated ink samples displayed variable appearance. The sample SA1a appeared black with white edges along the stroke, while the samples ZR1a and TO3 appeared essentially neutral under the first filter. Nevertheless, they began to exhibit IRL starting from the second excitation filter to the sixth filter. Afterward, they seemed to absorb light and appeared black from the seventh to the tenth filter.
## 5 Architectural Design of the PORTAL-DOORS System
### New Multilevel Metadata Design
**Entity metadata:** All metadata pertaining to the entity itself including tags, labels, locations and description of the entity as well as references to the owner and contact for the entity; corresponds to PDS schema element **EntityMetadata** and considered primary or _Level 1_ metadata about the entity itself. 2. **Record metadata:** All metadata pertaining to the stored records about the entity and the process of registering and managing the records including timestamps for creating and updating the records, references to the governing registries and directories, as well as references to the registrant and agents for the records; note that the registrant and agent for the records may be different from the owner and contact for the entity; corresponds to PDS schema element **RecordMetadata** and considered secondary or _Level 2_ metadata about the _Level 1_ metadata. 3. **Infoset metadata:** All metadata pertaining to the dynamic infoset about the entity assembled from the distributed stored records including status, validation timestamps if validated, and any entailments if inferred by a reasoning engine; corresponds to PDS schema element **InfosetMetadata** and considered tertiary or _Level 3_ metadata about the _Level 1_ and _Level 2_ metadata. 4. **Representation metadata:** Current design limited to use with only an identifier as an attribute on a wrapper element collating the three elements **EntityMetadata**, **RecordMetadata**, and **InfosetMetadata** respectively for the primary, secondary, and tertiary metadata; corresponds to PDS schema type **ResourceRepresentation** with element instances **PORTAL**, **DOORS**, and **NEXUS**. 5. **Message metadata:** All metadata pertaining to the messaging envelope and the process of exchanging messages throughout the PORTAL-DOORS System; design based on using an analogy with the IRIS-DNS System; corresponds to PDS schema element **PDS** as the root element for all PDS messages. As noted in the list above, implementation of the current design concepts for metadata in the PORTAL-DOORS System (see also Section 6) explicitly uses the term _metadata_ in the names for the corresponding PDS schema elements for the entity, record, and infoset objects (but not for the representation and message objects) to emphasize the distinctions made for the first three hierarchical levels of metadata for any given resource (see Figure 4).