esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
The extremely hydrophilic nature and specific surface structure make GO able to purify waste water as an adsorbent. For example, [PERSON] et al. [18] systematically investigated the adsorption of trivalent antimony (Sb(III)) on GO. It was found that GO had a strong adsorption ability for Sb(III) within a wide pH range of 2.0 to 10.0. [PERSON] et al. [19] revealed that GO could efficiently remove cationic dyes. In recent decades, a great deal of work has focused on the adsorption process of aromatic compounds. Among them, the concept of \(\pi\)-\(\pi\) stacking was widely accepted to describe the face-to-face stacking of aromatic systems involved in noncovalent interactions [20; 21; 22]. It should be noted that graphene is more suitable for the adsorption of aromatic compounds with a benzene ring. Numerous experimental studies indicated that graphene had relatively higher sorption capacities and a stronger sorption affinity for aromatic compounds than nonaromatics [13; 23]. Most of the research only explored the phenomenon description, rather than the exact sorption mechanism. On the basis of several theoretical calculation studies, it was believed that the \(\pi\)-\(\pi\) interactions between aromatic adsorbates and graphene played the most important role, which depended on the electron density of the aromatic system and the adsorbate structure [24; 25; 26]. However, these theoretical studies were only limited to a small portion of the benzene-ring units of graphene. In addition, very few experimental studies systematically investigated the interactions between \(\pi\)-acceptor or donor aromatics and graphene. Therefore, it was necessary to combine detailed experimental and theoretical calculation studies for the description of the adsorption mechanism. The goal of the current research was to reveal the adsorption characteristics of aromatics for graphene. To this end, five kinds of phenolic compounds were selected and the adsorption characteristics of them on reduced graphene oxide (RGO) and GO with a different \(\pi\)-electron density as adsorbents were studied. The effect of the molecular structure of adsorbates on the adsorptive interaction was also investigated. In order to further verify the research viewpoints, the electron distribution of phenols was also investigated by theoretical calculation. We anticipate that this study could fill the knowledge gap of the underlying sorption mechanism of aromatics on RGO and GO.
## 2 Method
### Evaluation Metrics  
This study uses the following metrics to evaluate the generated results.  
**Validity**. A generated molecule is considered valid as long as it can be parsed by RDKit.  
\[validity=\frac{\textit{the number of valid molecules}}{\textit{the number of all generated molecules}} \tag{5}\]  
**Uniqueness**. Uniqueness refers to the only molecule in the valid molecule set. If a molecule is unique, it means that there is no generated molecule that is the same as this molecule.  
\[uniqueness=\frac{\textit{the number of unique molecules}}{\textit{the number of valid molecules}} \tag{6}\]  
**Novelty.** Novelty is the proportion of generated molecules that are not present in the training set.  
\[novelty=\frac{\textit{the number of valid molecules that are not in the dataset}}{\textit{the number of valid molecules}} \tag{7}\]  
**KL Divergence.** KL divergence is an asymmetric measure of the difference between two probability distributions. Let \(P(x)\) and \(Q(x)\) be two probability distributions on a random variable \(x\); then, in the case of discrete and continuous random variables, KL divergence is defined as follows:  
\[KL(P,Q)=\sum P(x)log\frac{P(x)}{Q(x)} \tag{8}\]  
\[KL(P,Q)=\int P(x)log\frac{P(x)}{Q(x)}dx \tag{9}\]  
**Frechet ChemNet Distance (FCD).** The FCD is calculated using the features of the generated molecules and those from the dataset. Lower FCD values indicate better alignment with the data distribution. Mathematically, the FCD between a generated distribution G and a training data distribution D is defined as follows:  
\[FCD(G,D)=||\mu_{G}-\mu_{D}||^{2}+\textit{Tr}\bigg{(}\sum_{G}\ +\sum_{D}\ -2 \bigg{(}\sum_{D}\sum_{G}\ \bigg{)}^{\frac{1}{2}}\bigg{)} \tag{10}\]
# Monitoring Small Water Bodies Using High Spatial and Temporal Resolution Analysis Ready Datasets
## 2 Methods
### Validation Scheme  
To validate the surface area classification using the Otsu thresholding approach, we downloaded five orthorectified and multispectral SkySat images [17] (Blue: 0.450-0.515 \(\upmu\)m, Green: 0.515-0.595 \(\upmu\)m, Red: 0.605-0.695 \(\upmu\)m, and NIR: 0.740-0.900 \(\upmu\)m) at sub-meter (0.66-0.73 m) spatial resolution (Table 3). For each image, we overlaid the OFR geometry and manually delineated the OFR surface area, which resulted in 144 validation surface areas from 71 different OFRs for multiple observations in time. Then, we conducted a pairwise comparison of the validation surface area with the surface area obtained from PlanetScope, Basemap, and Planet Fusion. When the PlanetScope surface area date did not correspond exactly to the SkySat dates, we used the closest observation in time, which had a maximum difference of three days before or after the SkySat date. In addition, we assessed the uncertainties of PlanetScope, Basemap, and Planet Fusion for different surface area size classes: 0.1-5 ha (\(n=50\)), 5-10 ha (\(n=46\)), and 10-50 ha (\(n=48\)).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**OFR id** & **OFR Size (ha)** \\ \hline A & 13.62 \\ \hline B & 22.60 \\ \hline C & 9.83 \\ \hline D & 12.73 \\ \hline E & 13.82 \\ \hline F & 29.72 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Selected OFRs to illustrate PlanetScope, Basemap, and Planet Fusion surface area time series and their size according to the OFR dataset.
# Multiport Energy Management System Design for a 150 kW Range-Extended Towing Vessel
## 4 Simulation
### Simulation of ESS
#### 4.2.3 Case 3: ESS with an Unsatisfied Initial Battery Condition (15% SoC)  
In this case, the initial SoC of the battery was set as 15%. According to the power demand of the PMSM, the output power of the lithium battery, supercapacitors, and diesel generator system is shown in Figure 21a. The power data for several typical operation points are listed in Table 7. The following analysis was conducted:Figure 19: Battery discharge current for two ESS systems: (**a**) simulation period, 3.995–4 s; and (**b**) simulation period, 6.837–6.841 s.  
Figure 20: SoC curves for two ESS systems.  
Figure 21: (a) EMS power curves for case 3; (b) SoC curves of the battery and supercapacitors for case 1.  
1. During the initial stage (0-4 s), the supercapacitors and the diesel generator system supplied the PMSM. The battery was charged as its SoC dropped below 20%. The charging power diminished from 200 kW to 80 kW due to the continuous rise of the PMSM power demand.
2. In the before-cruise stage, the diesel generator system provided the entire power demand, simultaneously providing consistent charging to the lithium battery during 4-6 s. The charging power to the lithium battery was constant at 80 kW. The diesel generator's output power was 200 kW.
3. During the cruise stage (6-10 s), the diesel generator system and supercapacitors supported the RETV until its speed peaks. After that, the diesel generator system contributed to the peak power demand (150 kW). The battery's charging rate declined when the power was around 50 kW.
4. As the power demand decreased during the towing stage (10-14 s), the charging power of the lithium battery increased to 100 kW. The diesel generator system provided a total of 200 kW, of which 100 kW went to the PMSM.
5. In the endowing stage (after 15 s), the lithium battery received a small amount of charging power, 13.3 kW, from the PMSM until the end of the operation cycle at 20 s.
6. As shown in Table 7, the power demand of the PMSM in one operation cycle was 0.3742 kWh, while the diesel generator system and ESS output power was 0.4418 kWh. The comprehensive energy efficiency of the EMS was approximately 85%.  
Figure 21b shows the SoC curves of the battery and supercapacitors during an operation cycle. As shown, because the lithium battery SoC continued to be below the limit (20%), it remained charged for 0-20 s. At 0-4 s, it had the highest charging power due to the lower PMSM power demand. During the working stages, with constant speed, the diesel generator system provided uniform charging power, which can be explained by the stable increase in the SoC in 6-10 s. The reduced charging power after 15 s was provided by the reverse charging of the PMSM. The performance of the supercapacitors in this case was consistent with the performance in case 1.
## 3 Results and Discussion
### Application to Outdoor Sound Level Modeling  
Because the clusters may represent different acoustic environments, they can help identify which locations should be targeted for future acoustic data collection. Figure 6 shows the distribution of clusters in the training data (a) and the CONUS (b). We see that these distributions are quite different, with Cluster 7 (especially Subcluster 7a) being the most overrepresented and Cluster 6 being the most underrepresented.  
Recall that Cluster 7 is characterized by developed land cover and high road density and is therefore likely one of the most complex acoustic environments. Additionally, ambient noise is likely large within Cluster 7, making accurate predictions of sound levels within this cluster of greater importance for public health. Therefore, it is reasonable to sample this cluster more heavily than many of the others for human-focused applications.  
Cluster 6, on the other hand, which is generally characterized by cultivated (crop) land, is the most common cluster in the CONUS and the least common cluster (after combining data from both subclusters) in the training data. This indicates that we should target future data collection efforts in Cluster 6 first to improve overall model performance in the CONUS.
## 4 Results and Discussion
### Observations about the Experiment  
By employing a fusion of DCGAN data, the Inception V3 learning model, and XGradient boost for feature mapping, it is possible to enhance both the recognition rate and quality of chest X-ray data.  
Figure 13 illustrates the ROC curve of the suggested approach, which demonstrates the superior performance of our DCGAN, DCGAN + Inception V3, and DCGAN + Inception V3 + XGradient Boost models compared to previous research findings.  
Table 3 shows a comparison between the proposed approach and various techniques of data augmentation, such as the DCGAN. The improved suggested method has given a promising improvement to the results.  
The proposed methodology has demonstrated enhancements in accuracy, precision, recall, and F1 score ranging from 1.1% to 2.1% in a standard experimental setting. The observed enhancement can be attributed to the utilization of a hybrid approach involving the combination of the DCGAN and inception V3, together with the incorporation of XGradient Boost. If a one-to-one comparison is conducted, certain accomplishments may exhibit a 4.6% enhancement.
## 2 Related Works
### Q-Learning Network
#### 2.2.1 Deep Q-Network  
Deep Q-network (DQN) [8] overcomes the limitations of traditional Q-learning in handling large or continuous state spaces by integrating neural networks into the Q-learning framework. While conventional Q-learning relies on a Q-table to iteratively update action-state values, this tabular method becomes computationally impractical for complex environments due to storage and scalability constraints. DQN addresses this by replacing the Q-table with a parameterized neural network that approximates the action value function, where \(\theta\) denotes the network weights. The target Q-value is calculated using a separate update rule, and the discrepancy between the network's predicted Q-values and these target values is quantified through a loss function \(L(\theta)\). This relationship between the estimated Q-values and the target function is formalized in Equation (7), with the true action value defined by Equation (8), enabling efficient learning through gradient-based optimization rather than explicit table maintenance.  
\[L(\theta)=E[\sum_{t=1}^{N}(y_{t}-Q(s,a;\theta))^{2}] \tag{7}\]  
\[y_{t}=r_{t}+\gamma\text{maxmax}_{a^{\prime}}Q(s^{\prime},a^{\prime};\theta^{ \prime}) \tag{8}\]  
where \(s^{\prime}\) and a\({}^{\prime}\) denote the subsequent state and action after executing action a in state s. The Q-network approximates the action value function \(Q(s,a;\theta)\), where \(\theta\) and \(\theta^{\prime}\) in Equations (7) and (8) correspond to the dual parameter sets in the DQN architecture. The primary network, parameterized by \(\theta\), determines the current policy by estimating Q-values for action selection, while the target network, with parameters \(\theta^{\prime}\), computes the target Q-value. This target is derived from the immediate reward \(r_{t}\) and discounted future reward component \(\gamma\text{maxmax}_{a^{\prime}}Q(s^{\prime},a^{\prime};\theta^{\prime})\), reflecting the trade-off between present and anticipated returns. The interplay between these networks stabilizes training by decoupling value estimation and target generation.
# Support Vector Machine-Based Transmit Antenna Allocation for Multiuser Communication Systems
## 3 SVM-Based Antenna Allocation  
In order to reduce the computational complexity of the exhaustive search for antenna selection, we consider SVM-based antenna selection to solve Equation (4). Specifically, we employ a multiclass SVM algorithm to classify channel gain samples into \(L\) classes, each of which corresponds to an available antenna allocation scheme. With a sufficient number of channel gain samples, i.e., training data, we can  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Notation** & **Description** \\ \hline \(N_{t}\) & number of antennas at the transmitter \\ \(U\) & number of users \\ \(\mathbf{h}_{i}\) & channel coefficient vector to device \(i\) \\ \(\mathbf{H}\) & overall channel coefficient matrix \\ \(\mathbf{G}\) & overall channel gain matrix \\ \(\mathbf{s}_{l}\) & index vector of the allocated antenna with label \(l\) \\ \(\mathcal{L}\) & set of labels for all the available antennas allocated \\ \(L\) & number of labels in \(\mathcal{L}\) \\ \(P_{\text{TX}}\) & transmit power per antenna \\ \(R_{\text{sum}}\) & sum rate of the system in \(\text{bps}/\text{Hz}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notations used to describe communication systems.  
design a classification model, which can be used to predict the class of a test channel gain matrix, i.e., the best antenna allocation scheme for a new channel realization in a test, i.e., actual communications.  
Generally speaking, in a machine learning system, a learning model is first trained by the input training set and the corresponding labels [51; 52]. Then, this learning model can be used to predict the class labels for a new test dataset. The overall machine learning framework of our antenna allocation system is illustrated in Figure 2. It is worth noting that there are two types of tasks in this framework, an offline task and an online task. The online task includes channel estimation and channel allocation through learning-based prediction. The offline task consists of three tasks: i) training sample set design, ii) learning systems design, and iii) parameter adjustment. Distinguishing the online and offline tasks is crucial in communication systems. This is because the offline task can be performed with more powerful computing resources and relaxed computational complexity requirements, while the online task typically has stringent latency and computing constraints. In the following three subsections, the three offline tasks will be described in detail.
## 3 Results and Discussion
### Thermodynamic Stability and Dispersibility Studies of the Optimized ETB-NE  
Thermodynamic stability and good physical stability are specific advantages of nanoemulsions [32; 33]. The thermodynamic stability of the ETB-NE was confirmed as it passed the heating-cooling cycle, centrifugation, and freeze-thaw cycle test. No phase separation, creaming, or cracking was observed with ETB-NE. Further, ETB-NE was clear after dispersibility testing, and thus it passed the dispersibility test too.
## 3 Related Work
### Data Privacy
In the context of providers of big data, the large volume of data from a vast number of users means that, from an economic point of view, it is not feasible to apply deletion methods that destroy the data carriers. To this end, there are nondestructive approaches for both hard disk drives and solid-state drives. While overwriting-based approaches are effective for hard disk drives, where the sectors containing the data in question are overwritten several times with other data, erasure-based approaches for solid-state drives ensure that the flash block that provides access to the flash page containing the data is erased. As a result, the data can no longer be accessed and is finally dumped by the garbage collection [191]. So, while in theory, there are approaches to enable secure deletion for those physical data carriers, these approaches have limitations in terms of logical data storage. For instance, in the case of databases, such an approach is not possible as abstraction layers prevent a direct mapping of data to sectors or flash pages [192]. Blockchain-based data stores represent another example where this type of secure erasure is unsuccessful, as here the data are immutable, i.e., a deletion renders the blockchain invalid [193]. Cloud-based data stores also require other approaches, since only the cloud provider has control over the physical storages and the knowledge of how many replicas are held on which nodes [194]. In these cases, encryption-based approaches can be used. Here, the data are stored fully encrypted. This way, it is sufficient to delete the decryption key, which renders the encrypted data unreadable in the process. Hierarchical key management enables fine-grained deletion of individual data items, clusters of related data, or all data of a data subject at once [195]. Provenance analyses can be used to identify all data products that have been created based on a specific data item [196]. These data products must also be erased if a data subject requests to have their base data (or any part of it) deleted. It is evident that such a provenance analysis also generates a lot of additional data that potentially disclose private information [197]. To this end, when answering this kind of provenance queries, it is therefore necessary to rely on special privacy-aware approaches, such as intensional provenance answers [198].
# Imaginary Speech Recognition Using a Convolutional Network with Long-Short Memory
## 3 Materials and Methods  
### Preparing Database  
The Kara One Database was developed following a partnership between the University of Toronto and Toronto Rehabilitation Institute [9]. It contains signals acquired from eight male and four female participants (a total of twelve subjects), all right-handed, with no visual, hearing, or motor problems. All signals were collected using a standard protocol consisting of a (1) rest state, (2) stimulus state, in which the prompt with the stimulus appeared, (3) a five-second of stimulus imagined speaking state, and (4) an actual articulation of the stimulus. An important aspect of this database consisted of randomly presenting the stimulus to the subject at each epoch, avoiding the accidental creation of temporal correlations in EEG signals, as presented in [16]. The prompts contained seven phonemes: /iy/, /uw/, /piy/, /tiy/, /diy/, /m/, /n/ and four words chosen from the Kent's list of phonetically similar pairs: \"pat\", \"pot\", \"knew\", and \"gnaw\". The stimulus was chosen to cover a wide range of pronunciation mechanisms, such as nasal: /m/, /n/, \"knew\", and \"gnaw\", bilabial: /piy/, \"pat\", \"pot\", and /m/, closed vocal tract: /piy/, /tiy/, /diy/, vocals: /iy/, /uw/, and consonants: /m/, /n/. Further, in the study, we only used the signals corresponding to the imaginary speech.  
All signals from the database were visually analyzed by a specialist. This process led to elimination of the epochs containing high noises. Additionally, during the visual analysis of the signal, four of the twelve subjects were eliminated from the study, due to the very high noise signals or the presence of unattached ground wires. The number of signals that passed on in the study were of 993 for all subjects and all prompts.
# AA-HMM: An Anti-Adversarial Hidden Markov Model for Network-Based Intrusion Detection
## 4 Methodology
### Anti-Adversarial (AA) Mechanism: TH and THC
Furthermore, if the TH consecutively triggers the request to enlarge the DW for F times due to the normal pattern change (necessary feedback operation), instead of directly adjusting the DW to the intended WW, the DW would be adjusted to the average of the current upper bound plus the average of the sum of the intended WWs during the past F consecutive windows. Correspondingly, the upper bound would be \"broken\" and increased by the average of the sum of the intended WWs during the past F consecutive windows. The lower bound would be increased by the same step as the upper bound. The similar operations would also be applied to the DW recall procedure. As a result, this series of operations further enhance the model's adaptive abilities toward normal and intensive pattern fluctuations. Once the attackers successfully misleading the model, the next two things they would do are to stop sending the malicious training traffic and launch the real attacks that cannot be detected by the misled model. Notably, the pattern of malicious training traffic would be different from that of the actual attacks. Most importantly, even if the AA-HMM is misled, it can automatically recover itself to the normal state using the recall operation--the extended sections of upper and lower bounds  
Figure 6: Overview logic chain of threshold. would be recalled once the malicious training samples have not been consecutively received G times. The values of F and G can be flexibly set based on the defensive strategy and the desired security level. Particularly, repeatedly enlarging or reducing the DW is not a wise strategy due to the high PE or PV, even if the MD and DT trigger the DW adjustment because the DW cannot be rapidly decreased or increased to common values once this extremely fluctuated pattern passes. Accordingly, to tackle this kind of extreme case, another feedback variable called Threshold Controller (THC) was introduced to control the difficulty of breaking the bounds of TH. THC is responsible for counting the times of breaking bounds (Figure 6). The more breaking operations are accomplished, the more difficult to break the current bounds again. For instance, if the model increases the upper bound two times (THC = 2), the current upper bound would not be increased again unless the TH consecutively receives F + THC times requests for increasing TH, which is triggered by the DW increasing operation.