esa-sceva/satcom-chunk-collection huggingface dataset:
# Fresher Experience Plays a More Important Role in Prioritized Experience Replay
## 3 Methodology  
### Dueling DQN and DDPG  
In RL, the interaction process between the agent and environment is usually formulated as a Markov Decision Process (MDP). MDP is defined as a four-element tuple\((S,A,R,P)\), where \(S\)--state space, \(A\)--action space, \(R\)--reward function, and \(P\)--transition probability. The goal of RL is to learn the optimal policy \(\pi^{*}\) which defines the agent's behaviour and to maximize the discounted accumulative reward. The discounted accumulative reward function \(G_{t}\) is given as:  
\[G_{t}=R_{t}+\gamma R_{t+1}+\gamma^{2}R_{t+2}+\ldots=\sum_{k=0}^{\infty}\gamma^ {k}R_{t+k}, \tag{1}\]  
where \(\gamma\) is the discounted factor determining the priority of short-term reward, and \(t\) is the time step.  
The action-value function \(Q^{\pi}(s,a)\) represents the expected reward after taking an action \(a\) in state \(s\) and following policy \(\pi\):  
\[Q^{\pi}(s,a)=\mathbb{E}_{\pi}[G_{t}\mid S_{t}=s,A_{t}=a]=\mathbb{E}_{\pi} \Bigg{[}\sum_{k=0}^{\infty}\gamma^{k}R_{t+k}\mid S_{t}=s,A_{t}=a\Bigg{]}. \tag{2}\]According to the Bellman Equation [34], \(Q^{\pi}(s,a)\) can be expressed as an recursive formulation:  
\[Q^{\pi}(s,a)=\mathbb{E}_{\pi}[R_{t}+\gamma Q^{\pi}(S_{t+1},A_{t+1})\ |\ S_{t}=s,A_{t}=a]. \tag{3}\]  
The action-value function in Dueling DQN [20] is calculated by two parts: value function \(V(s)\) and advantage function \(A(s,a)\). Let \(\phi\) denote the parameters of the convolutional layers, \(\phi_{1}\) and \(\phi_{2}\) are the parameters of two streams of fully-connected layers, respectively. To address the identifiability problem that \(V\) and \(A\) can not be inferred from \(Q\), we have  
\[Q(s,a;\phi,\phi_{1},\phi_{2})=V(s;\phi,\phi_{2})+\Bigg{(}A(s,a;\phi,\phi_{1})- \frac{1}{|\mathcal{A}|}\sum_{a^{\prime}}A(s,a^{\prime};\phi,\phi_{1})\Bigg{)}. \tag{4}\]  
Herein, the action space is discrete, Dueling DQN can indirectly achieve the optimal policy \(\pi^{*}\) by searching all the action-value functions, which can be expressed as:  
\[\pi^{*}=\arg\max_{\pi}Q^{\pi}(s,a;\phi,\phi_{1},\phi_{2}). \tag{5}\]  
DDPG [25] cannot fit to the above approach due to the infinity of action space. It directly learns the optimal policy \(\pi^{*}\). It contains two deep neural networks: the action-value network \(Q(s,a,w)\) and the actor network \(\pi(s,v)\). This two networks are used to approximate the action-value function \(Q(s,a)\), as well as the actor function \(\pi(s)\), respectively where \(w\) and \(v\) are the network parameters. The action-value network is to minimize the loss function \(L(w)\), which is expressed as:  
\[L(w)=\big{(}R_{t}+\gamma Q^{\prime}\big{(}S_{t+1},A_{t+1},w^{\prime}\big{)}-Q( S_{t},A_{t},w)\big{)}^{2}, \tag{6}\]  
Note: \(Q^{\prime}(s,a,w^{\prime})\) is defined as the target network of \(Q(s,a,w)\), and the actor network is updated by following the chain rule from the start distribution \(J\).  
\[\
abla_{v}J\approx\mathbb{E}\Big{[}\
abla_{v}Q(s,a,w)|_{S_{t}=s_{j},A_{t}=\pi (s_{j},v)}\
abla_{v}\pi(s,v)|_{S_{t}=s_{j}}\Big{]}. \tag{7}\]
# Olfactory Capacity and Obesity: A Narrative Review of the Literature
## 3 Results
### Human Studies  
A study investigated sensitivity to a non-food odor (n-butanol) and a food-related odor (isoamyl acetate) in 24 healthy women with normal olfactory function. The tests were conducted when participants were hungry and when they were satiated. Additionally, participants rated their emotional condition, arousal, and alertness, as well as the intensity and pleasantness of both odors. No significant changes were found in detection thresholds for the non-food odor of n-butanol; however, a significant change was observed in the detection threshold for the food-related odor of isoamyl acetate. Specifically, the detection threshold for isoamyl acetate was significantly lower in the satiated state compared to the hungry state. Interestingly, greater sensitivity was observed during the satiated state, challenging the current hypothesis that food intake control is supported by decreased sensitivity to food odors. These findings support the idea that both odor thresholds and odor-related pleasure play important roles in regulating food intake [28].  
The first study to associate olfactory ability with nutritional status was conducted by [PERSON] [42] and published in 1956. Using the Blast Injection Method, the study found that individuals with obesity exhibited lower olfactory sensitivity compared to non-obesesubjects. A second study, conducted by [PERSON] [43], showed that individuals with normal weight and those with obesity did not differ in their hedonic response to sucrose (taste) or benzaldehyde (odor). Similarly, in [PERSON]'s 1980 study, olfactory sensitivity was measured in 97 pairs of twins, and total body fat percentage was found to be a factor associated with olfactory sensitivity [44]. In a study by [PERSON] et al. [45], participants with a BMI \(\geq\) 28 kg/m\({}^{2}\) were evaluated. Among participants under 65 years old, lower scores were observed in odor detection, odor identification, and taste perception. In contrast, in participants over 65 years old, higher scores were observed in those with a higher BMI.  
Altered olfactory performance could affect food choice, which could promote over-consumption of food and, consequently, obesity in a modern food environment. Increased sensitivity to hypercaloric food odors has been observed in individuals with obesity, suggesting that the brain may amplify autonomic responses to highly rewarding foods [46; 47; 48; 49].  
On the other hand, a longitudinal study assessed olfaction and body composition in elderly people by means of the Brief Smell Identification Test. Participants with low olfactory ability had lower body weight and fat mass than those with high olfactory ability. According to longitudinal analyses, participants with poor olfaction lost more total weight and lean mass per year than those with good olfaction [50].
# Physics-Based Noise Modeling and Deep Learning for Denoising Permanently Shadowed Lunar Images
## 4 Results  
### Experimental Environment and Model Training  
The network is developed using the PyTorch 2.2.2 framework. For the comparative experiments, the Adam optimizer is employed for backpropagation, with training conducted for 200 epochs, a batch size of 16, and a learning rate of 0.0001, along with the use of automatic mixed precision. The training is performed on an NVIDIA RTX 3090 GPU with 24 GB of memory. For the ablation experiments, the setup is almost identical to that of the comparative experiments, with the only difference being a reduction of 55 epochs in the training duration.  
This paper conducts both quantitative and qualitative experiments on simulated and real-world datasets to compare and analyze the proposed method with five recent state-of-the-art image denoising approaches. These include: U-Net (2019) [49,50], which utilizes an encoder-decoder architecture with skip connections to fuse multi-scale features for end-to-end noise suppression; UNet3 Plus (2020) [51], which aggregates deep semantic and shallow details through a full-scale skip connection mechanism to enhance texture restoration capability; DRANet (2024) [18], which incorporates a dual residual attention subnetwork for collaborative optimization along with a global feature fusion strategy to improve noise modeling robustness; UFormer (2022) [19], which integrates window-based multi-head self-attention modules and local enhancement feed-forward networks for collaborative modeling of local-global feature interactions; and AST (2024) [20], which constructs a lightweight adaptive Transformer framework through structural sparsification and attention feature optimization components to balance computational complexity with denoising accuracy.
# A Lightweight File System Design for Unikernel
## 5 Experiment Results
* In most scenarios, ULFS delivers the best performance regardless of workload characteristics and I/O size, while other file systems give and take amongst each other under specific workload conditions. * ULFS's superior performance can be attributed to its streamlined file system design and efficient file I/O operations via mmap. In contrast, other file systems suffer from significant overhead in software stacks (e.g., API calls) rather than real I/O processing. * In all file systems, the latency required to access a given amount of data decreases as the I/O size increases. However, for in-memory file systems, which allocate memory in 4 KB units, this improvement becomes noticeable only for I/O sizes larger than 4 KB. * In-memory filesystems (i.e., Ramfs-u, Ramfs-q, Rumpvfs) generally perform well, as they bypass storage access during file system operations. However, their performance significantly drops with frequent memory allocations, such as those required for small-sized read-after-write operations. * Hcfs redirects API calls to the host for file system operations, leading to substantial overhead. Consequently, Hcfs performs well only when API calls are infrequent. * 9 pfs tends to show relatively lower performance since it is a host-based file system requiring a hypervisor, resulting in API call overhead. However, it excels in lseek operations where requests are managed directly by the guest and not transferred to the host, highlighting specific operational efficiencies. Figure 14: Performance comparison of Unikernel file systems for SQLite Insert/Select.
# Silk Bionanocomposites for Organic Dye Absorption and Degradation
## 2 Materials and Methods
### Adsorption Isotherms  
The adsorption capacity of SF sponges and swollen hydrogels were evaluated by immersing the samples in 15 mL of MB solution (10-200 mg L\({}^{-1}\)) under stirring at RT for 48 h to reach the equilibrium concentration (C\({}_{e}\); MB concentration in the solution at the equilibrium state). After incubation, the absorbance at 664 nm of the solution was measured. \(C_{e}\) and the adsorption capacity (\(q_{e}\); mg g\({}^{-1}\); mass of adsorbed MB (mg) per mass of adsorbent (g)) parameters were calculated for each point. Results were fitted with the Langmuir, Freundlich and Temkin adsorption models as these models are widely used inthe literature for similar systems [10; 15; 41; 42; 43; 44]. The Langmuir model can be described by Equation (1).  
\[q_{e}=\frac{q_{m}K_{L}C_{e}}{1+K_{L}C_{e}} \tag{1}\]  
This equation can be linearized as follows:  
\[\frac{C_{e}}{q_{e}}=\frac{1}{q_{m}K_{L}}+\frac{1}{q_{m}}\times C_{e} \tag{2}\]  
The Freundlich experimental model can be described by the following equation:  
\[q_{e}=K_{F}C_{e}^{1/n} \tag{3}\]  
It can be linearized as:  
\[\ln(q_{e})=\ln(K_{F})+\frac{1}{n}\ln(C_{e}) \tag{4}\]  
The [PERSON] model can be described by Equation (5).  
\[q_{e}=\frac{RT}{b_{T}}\ln(K_{T}C_{e}) \tag{5}\]  
This equation can be linearized as follows:  
\[q_{e}=\frac{RT}{b_{T}}\ln(K_{T})+\frac{RT}{b_{T}}\ln(C_{e}) \tag{6}\]  
where \(q_{e}\) is the adsorption capacity (mg MB g adsorbent\({}^{-1}\)); \(q_{m}\) the maximum adsorption capacity (mg g\({}^{-1}\)); \(C_{e}\) the MB concentration at the equilibrium state (mg L\({}^{-1}\)); n represents the heterogeneity of the adsorbent; \(K_{L}\), \(K_{F}\) and \(K_{T}\) are the adsorption constants (L mg\({}^{-1}\)) of the Langmuir, Freundlich and Temkin models, respectively. \(T\) is the absolute temperature in Kelvin (\(K\)), \(R\) is the universal gas constant, 8.314 J mol\({}^{-1}\) K\({}^{-1}\), \(b_{T}\) is the constant related to the heat of adsorption indicating the variation of adsorption energy (J mol\({}^{-1}\)).
# Valuing Resilience Benefits of Microgrids for an Interconnected Island Distribution System
## 1 Introduction
Recent efforts by [11] as well as [12] have proposed a hybrid approach that combines several of the concepts overviewed by [10], and offers a clear path forward to calibrating and validating these models. Case studies that directly compare these various approaches and describe the findings using language and concepts that are readily accessible to electric utility regulators were not found in the literature. This research work presents a real case study of two islands within a multi-island power system operated by a utility that serves about 1.5 million metered premises, providing electricity to nearly 3.2 M residents. A valuation is presented of the economic impact of avoiding both day-to-day outages (e.g., reliability events) and more extreme hurricanes (e.g., resilience events) on the islands' residents. This valuation serves to establish a resilience investment business case for guiding infrastructure investment to modernize the electric grid. A microgrid sizing exercise is presented, consisting of aggregations of DERs leading up to a nested architecture to leverage renewable generation. Renewables can be integrated not only through utility-scale, utility-owned resources, but also through virtual power plants through renewable procurement processes that are currently open for bids. Improving the economic resilience of island communities by investing in the electric grid is one way to approach the power system and its design, where power system infrastructure, along with control and communication architectures, are designed concurrently to integrate DERs and microgrids and ensure the delivery of power to critical loads, even under extreme events [13]. The proposed concept and resilience valuation approach represents a unique opportunity to develop a system according to these tenets. The paper is organized into five sections. This introduction has introduced the need for methodologies for the quantification of the resiliency value of grid investments such as microgrids and the application of such methods to practical problems. Section 2 presents the proposed valuation methodology for resiliency-enhancing grid investments. Section 3 describes the Vieques Culebra case study and the microgrid DER sizing methodology and costs. Section 4 presents the conceptual design for the microgrid, considering the microgridwould be composed of utility-owned and customer owned assets. The authors present conclusions in Section 5.
## 4 Results and Discussion  
In this section, the techniques performed for the indoor position estimation with the proposed hardware, algorithm, and methods are presented along with the experimental data gathered in the experiments in a real indoor environment. Among these, the estimated indoor position, the satellites seen by each directional outdoor GPS antenna during the experiments, estimated distances from each repeater to the receiver with receiver clock bias, the CNR of each satellite signal within the angle of view of the corresponding repeater, and the 50% CEP from the estimated position are graphically visualized and presented in this section. The MATLAB(r) routine with LSNAV algorithm, presented in Section 2, has been utilized for the position estimation of each sample collected in the experiments.  
The satellites, in the angle of view of each repeater in Experiments 1 and 2, are presented in Figure 12. It should be noted that for this approach to work properly, it is  
Figure 11: Distance between the receiver and each repeater, directional GPS antenna azimuth (Az) and elevation (El), and the attenuator value in each repeater in Experiment 2.  
important to know which repeater transfers the signal, coming from a particular satellite, into the building. This is in fact one of the reasons for the decision to utilize directional GPS antennas.  
The association of the repeater, used for the signal of a particular GPS satellite, is accomplished by using the position, the azimuth, and the elevation of the directional GPS antenna and considering the GPS satellites that are in the targeted region of that particular antenna. If a satellite falls into the targeted region of multiple GPS antennas, we do not use any measurement based on that satellite, since we cannot be sure which repeater transferred the signal of that satellite into the building. In these experiments, therefore, the satellite labeled as G19 in Figure 12 (red point) has been ignored, while the satellites represented with green points have been utilized in position estimation for both experiments. Additionally, although the directional antenna beam widths are 60 degrees, the experiment results have shown that the GPS antennas can receive signals from a 90-degree angle in azimuth (Figure 12).  
Figure 12: Satellites in each repeater’s angle of view in Experiments (**a**) 1 and (**b**) 2.
# Leveraging IoT Devices for Atrial Fibrillation Detection: A Comprehensive Study of AI Techniques
## 2 Materials and Methods
### Features Extraction
#### 2.2.2 Wavelet Spectrogram-Based Features  
The wavelet spectrogram is generated from preprocessed photoplethysmography (PPG) signals, providing a time-frequency representation that facilitates the extraction of significant features. This spectrogram is obtained by applying the Morlet wavelet transform within the frequency range of 0.1 to 5.5 Hz, allowing for detailed analysis of the PPG signals. The resulting wavelet spectrogram serves as a robust basis for feature extraction, enabling the identification of critical patterns relevant to atrial fibrillation (AF) detection.  
From the spectrogram matrix, a total of 1808 features are derived using various statistical and energy functions, as indicated in prior studies [2, 11]. These features encompass a comprehensive overview of the spectrogram's energy distribution, shape, and spectral content, providing essential insights into both the spectral characteristics and temporal fluctuations of the PPG signals. This rich feature set is instrumental in enhancing the accuracy of AF detection, as detailed in Table 2.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline
**Parameter** & **Description** \\ \hline Average Heart Rate & Indicates the overall heart rate level. \\ \hline Heart Rate Standard Deviation & Captures heart rate variability over time. \\ \hline Heart Rate Median & Represents the middle heart rate value, robust to outliers. \\ \hline NN Interval Ratio & Measures irregularity in heart rate patterns, calculating the intervals greater than a threshold. \\ \hline Root Mean Squared Successive Difference (RMSSD) & Reflects short-term heart rate variability, computing the RMSSD between consecutive NN intervals. \\ \hline Low-Frequency to High-Frequency Power Ratio & Relates to autonomic nervous system activity based on the signal frequency spectrum. \\ \hline Inflection Point Ratio (TPR) & Provides insights into waveform morphology. \\ \hline Crest Time & Calculates time between the first peak and trough in the PPG signal. \\ \hline Combined Peak-Rise and Fall Height & Captures pulse waveform shape. \\ \hline Waveform Width & Measures the temporal extent of the pulse waveform. \\ \hline Cross-correlation Coefficient & Evaluates similarity between consecutive pulse segments, providing information about consistency. \\ \hline Adaptive Organization Index (AOI) & Quantifies adaptive organization in the PPG signal by measuring the changes in successive pulse segment differences. \\ \hline Variance of Slope of Phase Difference & Characterizes variation in phase difference slope. \\ \hline Spectral Entropy & Measures frequency spectrum complexity capturing the power distribution across different frequency bands. \\ \hline \end{tabular}
\end{table}
Table 1: Heart Parameters as Signal-Based Features.
## 2 Materials and Methods
### Structure of the CER-YOLOv5s Model
#### 2.2.4 Soft-DIoU-NMS Algorithm  
In images of power transmission lines, there is a phenomenon where engineering-vehicle targets occlude each other. The YOLOv5s model uses the traditional NMS algorithm to eliminate overlapping detection boxes, but this method has the limitation of mistakenly deleting boundary boxes. The Soft-NMS algorithm [16] suppresses the competition  
Figure 10: ERM module.  
between overlapping boxes by gradually decreasing the confidence scores of the target boxes, effectively reducing the problem of missed detections. However, it still uses IoU (intersection over union) to determine the overlap between two bounding boxes, ignoring the measurement of the distance between bounding boxes. Therefore, this paper proposes a Soft-DIoU-NMS algorithm based on DIoU [17]. The formula is shown in Equation (5):  
\[\left\{\begin{array}{l}S_{i}=\left\{\begin{array}{l}S_{i},\quad IoU-R_{ DIoU}(M,b_{i})<N_{t}\\ S_{i}(1-IoU(M,b_{i})),IoU-R_{DIoU}(M,b_{i})\geq N_{t}\\ R_{DIoU}=\frac{\rho^{2}\left(b,b^{st}\right)}{c^{2}}\end{array}\right.\end{array}\right. \tag{5}\]  
where \(S_{i}\) represents the score of the \(i\)th bounding box; \(M\) represents the bounding box with the highest current score; \(b_{i}\) represents the \(i\)th bounding box; \(N_{t}\) represents the set threshold; \(R_{DIoU}\) is the penalty term of the DIoU function. The schematic diagram is shown in Figure 11. \(\rho^{2}\left(b,b^{st}\right)\) represents the Euclidean distance between the center points of the predicted box and the ground truth box and \(c\) represents the diagonal length of the smallest enclosing rectangle that covers both the predicted box and the ground truth box.  
The Soft-DIoU-NMS algorithm employs a score penalization mechanism. When the DIoU value between the highest-scoring bounding box and another bounding box falls below a set threshold, the score of the bounding box remains unchanged. Otherwise, the score of the bounding box is linearly reduced, and it participates in the next iteration until all retained detection boxes are identified. The Soft-DIoU-NMS algorithm not only considers the overlapping area of the bounding boxes but also uses DIoU to minimize the distance between the target bounding boxes. This leads to faster model convergence, higher recall rates for occluded targets, and more reasonable detection results.
## 3 Proposed Methodology
### Evaluation Matrices  
The evaluation metrics used to assess the performance of the RNN model are Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the Coefficient of Determination (\(R^{2}\)). Each metric provides insight into the model's accuracy, consistency, and overall fit to the data [26]. MAE calculates the average of the absolute differences between the true values \(y_{i}\) and the predicted values \(\hat{y}_{i}\). It indicates the average error magnitude without considering its direction (positive or negative).  
\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}\lvert y_{i}-\hat{y}_{i}\rvert \tag{19}\]  
In time-series forecasting, MAE provides a clear interpretation of the average forecast error. It is particularly useful when each error has an equal impact on the evaluation. RMSE measures the square root of the average squared differences between the actual and predicted values. By squaring the errors before averaging, RMSE penalizes larger errors more heavily than smaller ones, making it sensitive to outliers.  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{\frac{n}{n}}(y_{i}-\hat{y}_{i})^{2}} \tag{20}\]  
\(R^{2}\) represents the proportion of variance in the actual values that is predictable from the model's predictions. It measures how well the model's predictions approximate the actual data points.  
\[R^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{n}(y_{i}-\bar {y})^{2}} \tag{21}\]  
where \(\bar{y}\) is the mean of the actual values.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline No. of units & 128.64 \\ Dropout rate & 0.35 \\ Loss function & MSE \\ Optimizer & Adam \\ Learning rate & 0.001–0.00001 \\ Window size & 7 \\ Epochs & 300 \\ Early stop patience & 25 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Proposed model hyperparameters.