esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 RL-Based Decision Making Solution
### RL-Based Multimedia Traffic Routing Algorithm  
The proposed routing algorithm is implemented to find the optimal path from the media provider to the customer in the infrastructure plane. Algorithm 1 takes the network topology, the QoS parameters, learning rate \(\alpha\), \(\epsilon\)-greedy parameter and the number of learning episodes as input. When video packets flow in the network from a given provider to the desired customer, the algorithm initialises the Q-values of the Q-table to zeros. This means that for a given video packet at the source OpenFlow switch \(y_{src}\), the first learning episode begins with initialising the state of a video packet at the source switch. At this point, this state starts selecting one action \(A\) from the current state \(S\) using \(\epsilon\)-greedy exploration-exploitation policy. Next, the algorithm uses the network QoS parameters and the state \(S\) to compute the reward based on Equation (9) related to the action \(A\) and discovers the new state \(S^{\prime}\). Following that, the Q-function is obtained using Equation (7) and set the next state \(S^{\prime}\) as the current state \(S\). The state transition iteration continues as this episode ends, and a new one starts till \(S\) is equivalent to the final state (i.e., the video packet arrives at the OpenFlow destination switch \(y_{dst}\)). Finally, the RL agent obtains the optimal path that achieves the lowest Q-values derived from the Q-table to forward video packets between the given provider-customer pairs. Once the final goal is reached, the flow installation module in the control plane receives the path and installs it in the routing table of the OpenFlow switches.  
```
Input: Network topology: \(G(V,E)\)  Network QoS parameters: \(B_{l}\),\(D_{l}\),\(J_{l}\),\(L_{l}\)  Learning rate: \(\alpha\)  Exploration-exploitation parameter: \(\epsilon\)  Learning episodes number: \(n\) Output: Optimal path from the media provider to the customer that produced the lowest Q-values derived from the Q-table foreachVideo transmission over \((y_{src},y_{dst})\in Y\)do  Initialise \(Q\): \(Q(S,A)=0\),\(\forall s\in S,\forall a\in A\) forepisode\(\gets 1\) to \(n\)do  Initialise state \(S=y_{src}\in\) S; while\(S^{\prime}\) is not \(y_{dst}\)do  Choose an action \(A\) from \(S\) using \(\epsilon\)-greedy policy, Equation (8) derived from \(Q\); \(R^{\prime}\gets R(S,A)\)// The RL agent obtains the reward from network QoS parameters using Equation (9); then it observes the new state \(S^{\prime}\); \(Q(S,A)=Q(S,A)+\alpha[R(S,A)+\min_{A}Q^{\prime}(S^{\prime},A^{\prime})-Q(S,A)]\) \(S\gets S^{\prime}\)// proceed with the next state;  end for  end for  end for
```  
**Algorithm 1** Q-Learning-based Multimedia Traffic Routing
###### Abstract  
The aim of the study was to evaluate the polyphenol profile of _Castanae sativa_ leaf methanolic extract and further evaluate its biological activities in vitro. After purification with an RP-18 resin, the extract was assessed for its polyphenol profile by UPLC-PDA-MS/MS, as well as for the antioxidant potential (ABTS, CUPRAC, ChA, ROS scavenging methods), anticancer, antibody, antidiabetic and antimicrobial potential. Eighteen polyphenols were identified and the dominant compounds were chestain followed by quercetin 3-O-glucoside. The total phenolic content of the extract showed a value of 1426.55 mg/100 g d.w. The obtained preparation showed the ability to scavenge O2*- (0.067 mg/mL) and OH* (0.207 mg/mL) radicals and had a stronger anti-obesity than anti-diabetic effect. Additionally, this extract exhibited a strong anticancer activity against the Caco-2 line (153.54 ug/mL), with anti-migratory and anti-proliferative activity. In turn, among the tested strains, the highest activity was demonstrated against _Staphylococcus aureus_. Moreover, the effects demonstrated were strongly dependent on the content of polyphenols. In conclusion, C. _sativa_ is a promising source of natural antioxidant, antibacterial, antiobesity, antidiabetic and chemoprer counting compounds for food-pharma industry; however, further experimental studies are needed to validate its pharmacological properties.  
C _Castanae sativa_; leaves; polyphenols; antioxidant; anticancer; antibacterial activities
# Estimation of Distribution Algorithms with Fuzzy Sampling for Stochastic Programming Problems
## 3 Estimation of Distribution Algorithms for Simulation-Based Optimization
### Sampling Techniques
#### 3.1.1 Variable Sampling Path  
The variable-sample (VS) method [30] is defined as a class of methods that uses the Monte Carlo simulation to solve the stochastic programming problem. This sampling technique invokes several simulations to estimate the objective function value at a single solution. Search methods can gain benefits from such sampling to convert the stochastic programming problem into a nonlinear programming one. Sampling Pure Random Search (SPRS) [30] is a random search algorithm that uses the VS process. The average of variable-size samples replaces the objective function value of the SPRS algorithm in each objective function evaluation call. The SPRS algorithm can converge, under certain conditions, to an optimal local solution. The formal SPRS algorithm is shown in Algorithm 5.  
```
1: Generate a point \(x_{0}\in X\) at random, set an initial sample size \(N_{0}\), and \(k:=0\).
2: Generate a point \(y\in X\) at random.
3: Generate a sample \(\omega_{1}^{k},\ldots,\omega_{N_{k}}^{k}\).
4: Compute \(\hat{f}(x_{k})\), \(\hat{f}(y)\) using the following formula: \(\hat{f}(x)=\frac{\widehat{g}(x_{\omega_{1}^{k}})+\ldots+\widehat{g}(x_{\omega _{N_{k}}^{k}})}{N_{k}}\).
5: If \(\hat{f}(y)<\hat{f}(x_{k})\), then set \(x_{k+1}:=y\). Otherwise, set \(x_{k+1}:=x_{k}\).
6: If the stopping criteria is satisfied, stop. Otherwise, update \(N_{k}\), set \(k:=k+1\) and go to Step 2.
```  
**Algorithm 5** Sampling Pure Random Search (SPRS) Algorithm
## 4 Simulation and Analysis
### Acceptable URA and \(P_{\text{sat}}\)
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**\(P_{\text{sat}}\)**} & \multirow{2}{*}{**Constellation**} & \multirow{2}{*}{**Coverage**} & \multicolumn{6}{c}{**Integrity Risk Requirement**} \\ \cline{6-9}  & & & \(\mathbf{2\times 10^{-7}}\) & \(\mathbf{10^{-7}}\) & \(\mathbf{9\times 10^{-8}}\) & \(\mathbf{4.5\times 10^{-8}}\) & \(\mathbf{2\times 10^{-8}}\) & \(\mathbf{10^{-8}}\) \\ \hline \multirow{6}{*}{_URA_ (m)} & \multirow{2}{*}{\(10^{-5}\)} & 24 GPS + 27 BDS & 99.5\% & 1.36 & 1.26 & 1.24 & 1.14 & 1.04 & 0.98 \\  & & 95.0\% & 1.84 & 1.70 & 1.68 & 1.58 & 1.46 & 1.38 \\  & & 23 GPS + 26 BDS & 99.5\% & 0.84 & 0.70 & 0.68 & 0.56 & / & / \\  & & 95.0\% & 1.32 & 1.20 & 1.18 & 1.08 & 0.96 & 0.88 \\ \cline{2-10}  & \multirow{2}{*}{\(24\) GPS + 27 BDS} & 99.5\% & 1.04 & 0.98 & 0.96 & 0.88 & 0.80 & 0.72 \\  & & 95.0\% & 1.46 & 1.38 & 1.36 & 1.28 & 1.20 & 1.14 \\  & & 23 GPS + 26 BDS & 99.5\% & / & / & / & / & / \\  & & 95.0\% & 0.96 & 0.88 & 0.86 & 0.78 & 0.68 & 0.60 \\ \cline{2-10}  & \multirow{2}{*}{\(10^{-3}\)} & 24 GPS + 27 BDS & 99.5\% & 0.80 & 0.72 & 0.72 & 0.66 & 0.56 & / \\  & & 95.0\% & 1.20 & 1.14 & 1.14 & 1.08 & 1.00 & 0.96 \\ \cline{2-10}  & \multirow{2}{*}{\(23\) GPS + 26 BDS} & 99.5\% & / & / & / & / & / & / \\ \cline{2-10}  & & 23 GPS + 26 BDS & 95.0\% & 0.68 & 0.60 & 0.58 & 0.52 & / & / \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Acceptable _URA_ with different \(P_{\text{sat}}\) and integrity risk requirement. From Table 4, the _URA_ requirement is tighter with smaller \(P_{\rm fa}\) and VAL because the probability of detection failure and position error exceeds the alarm limits, which become greater with either stricter \(P_{\rm fa}\) or smaller VAL. The selection of \(P_{\rm md}\) had little effect on acceptable _URA_ because the estimate of integrity risk could not be directly affected by \(P_{\rm md}\). If the global coverage is 99.5%, acceptable _URA_ should be better than 0.88 m when \(P_{\rm fa}\) is set as the continuity risk requirement of LPV-200, i.e., 8 \(\times\) 10\({}^{-6}\). When VAL was set to 20 m, which is required for APV-II, it was impractical for RAIM to meet the integrity risk requirement of 10\({}^{-7}\) by improving _URA_ because the observation error introduced by the other threat sources, such as tropospheric delay error, had not been mitigated. Monitoring threat sources with an auxiliary ground facility, such as a ground-based augmentation system (GBAS), can satisfy the higher integrity requirement.
# Compact Ultra-Wideband Series-Feed Microstrip Antenna Arrays for IoT Communications
## 2 Materials and Methods
### Design of the Antenna Element  
The radiating element chosen is a rectangular microstrip patch for its simplicity of design, and because its resonant frequency is easily controllable by varying its length, L. In this work, in fact, it is a particular case of the rectangular patch, which is a square structure, i.e., \(W=L\), as shown in Figure 1. Another advantage is that it allows the use of inset feeding technique [31], allowing the radiating element to be easily matched to any input impedance, changing the point (\(y_{n}\)) where the microstrip line (\(w_{line}\)) connects the patch, which will be a huge benefit in this work.  
The different parameters that influence the operation of the microstrip patch are identified in Figure 1. They were determined based on theoretical concepts [30], optimized through simulation, and will be detailed later. The characteristic impedance \(Z_{0}=90\ \Omega\) for the feeding line (of width \(w_{line}\)) was chosen.
# Whole-Genome Alignment: Methods, Challenges, and Future Directions
## 5 Comprehensive Analysis of Genomic Comparison Tools: Human and Diverse Genomes
### Analysis of C. elegans and Baker's Yeast Genomes  
The study was then extended to a comparison of _C. elegans_ and baker's yeast genomes to evaluate the tools under significantly different genomic conditions:  
* In stark contrast to its performance with human genomes, MUMmer4 yielded no output for the _C. elegans_ and baker's yeast genomes. This indicated MUMmer4's limitations in aligning genomes that are not closely related, thereby suggesting its niche application in genomic studies;
* Sibeliaz revealed only 12% coverage in conserved regions between _C. elegans_ and baker's yeast, identifying 21,816 distinct blocks. This low coverage indicated a substantial evolutionary distance between these species. Sibeliaz's strength lies in its ability to analyze and compare genomes with significant structural and evolutionary variations, making it a versatile tool for comparative genomics across diverse species;
* Like its performance with human genomes, Dialign2 was unsuccessful in processing the data from _C. elegans_ and baker's yeast, further highlighting its limitations in handling diverse genomic data.
* Unlike its effective mapping of human genomes, Minimap2 failed to map the _C. elegans_ and baker's yeast genomes, indicating challenges in aligning significantly divergent genomes.
## 3 Challenges in Immersive Video Quality Assessment
### Corresponding Pixel Shift Error  
The corresponding pixel shift error (CPSE) is caused by the quantization of depth maps (usually represented with 8 or 10 bits per value), imperfect camera parameters, and inaccuracies in the virtual view rendering process. All mentioned causes are unavoidable during the rendering and induce minor changes (shifts) in the position of rendered objects. This type of error is unnoticeable for viewers; however, it downgrades the \"quality\" measured by most typical objective quality metrics and makes the results irrelevant.  
An example of a rendering-introduced object shift is shown in Figure 2. The figures show an original image (Figure 2A) and a synthesized image (Figure 2B) with the dancer being slightly shifted (by less than 2 pixels). This change in object position is irrelevant to the experience of a viewer; however, it significantly degrades the SSIM metric value (0.9954\(\rightarrow\)0.9893). The proposed IV-SSIM metric is immune to CPSE artifacts (the IV-SSIM metric value is the same for both examples), exposing a better correlation with subjective quality perception. The example shown in Figure 2C presents the object shift exceeding the range expected from reprojection error. In this case, both metrics indicate quality degradation.
_Applied_  
_sciences_  
_Article_  
**Optimizing HARQ and Relay Strategies in Limited Feedback Communication Systems \({}^{\dagger}\)**  
**[PERSON] 1,*\({}^{\copyright}\), [PERSON] 1 and [PERSON] 2\({}^{\copyright}\)**  
Received: 24 September 2020; Accepted: 5 November 2020; Published: 8 November 2020  
**Abstract:** One of the key challenges for future communication systems is to deal with fast changing channels due to the mobility of users. Having a robust protocol capable of handling transmission failures in unfavorable channel conditions is crucial, but the feedback capacity may be greatly limited due to strict latency requirements. This paper studies the hybrid automatic repeat request (HARQ) techniques involved in re-transmissions when decoding failures occur at the receiver and proposes a scheme that relies on codeword bundling and adaptive incremental redundancy (IR) to maximize the overall throughput in a limited feedback system. In addition to the traditional codeword extension IR bits, this paper introduces a new type of IR, bundle parity bits, obtained from an erasure code across all the codewords in a bundle. The type and number of IR bits to be sent as a response to a decoding failure is optimized through a Markov Decision Process. In addition to the single link analysis, the paper studies how the same techniques generalize to relay and multi-user broadcast systems. Simulation results show that the proposed schemes can provide a significant increase in throughput over traditional HARQ techniques.  
**Keywords:** hybrid automatic repeat request (HARQ); communication systems; relays; broadcast channels; optimization methods; error correction coding
# A Method Improves Speech Recognition with Contrastive Learning in Low-Resource Languages
## 2 Related Work
### Contrastive Predictive Coding  
[PERSON] et al. proposed a general unsupervised comparative learning framework. Its purpose is to extract useful representation information from high-dimensional sequence data, compare the context representation information extracted from the data with the representation information of samples at future time, and obtain the key representation information that best predicts the future, which is called Contrastive Predictive Coding (CPC). The model first cuts the audio sequence into suitable sequence fragments \(X=x_{1},...,x_{T}\), and the fragments are mapped into the hidden space \(Z\) using a nonlinear encoder \(g_{enc}\) with \(z_{t}=g_{enc}(x_{t})\). The autoregressive model \(g_{ar}\) is then used to fuse the historical temporal information \(z\) to obtain the context representation \(C\), \(c_{t}=g_{ar}(z_{\leq t})\). The model architecture of Contrastive Predictive Coding using speech as input is shown in Figure 1.  
The encoder and autoregressive models are optimized using InfoNCE loss. At each time \(t\) there is given a set \(X=x_{1},...,x_{N}\) of \(N\) random samples containing one positive sample from \(p(x_{t+k}|c_{t})\) and \(N-1\) negative samples from the proposal distribution \(p(x_{t+k})\), with the following loss function  
\[\mathcal{L}_{N}=-\mathbb{E}\Bigg{[}log\frac{exp(z_{t+k}^{T}h_{k}(c_{t}))}{\sum _{x_{j}\in X}exp(z_{j}^{T}h_{k}(c_{t}))}\Bigg{]} \tag{1}\]  
where \(h_{k}(\cdot)\) is a linear transformation for every step \(k\).
# QUIC Network Traffic Classification Using Ensemble Machine Learning Techniques
## 4 Methodology
### Ensemble Techniques  
In ensemble ML techniques, the classification or regression decision is made by a group of models rather than a single model. These ensemble models work on three methods: bagging, stacking, and boosting [21; 22]. Bagging is based on random sampling with replacement, whereas boosting is based on sequential modeling. In other words, in the bagging method, a subset of training samples is selected from the original samples with replacement, and the final decision is based on majority voting [23]. In boosting technique, the model converts a weak learner into a strong learner by sequentially creating models so that the final model has the highest accuracy [24]. Stacked, or stacked generalization, is also a common ensemble method where a combined trainable learner is formed from individual learners. Individual learners are called first-level learners and the combined ones are called meta-learners. Meta-learners can be used to identify which classifiers are reliable and which are not. It is possible to use trainable combiners to identify which classifiers are most likely to succeed in which region of the feature space and combine them accordingly [22]. To visualize the three different methods, an illustration is given in Figure 3.
# Malware API Calls Detection Using Hybrid Logistic Regression and RNN Model
## 4 Results and Discussion
### Balance Dataset  
Following the application of an undersampling technique to balance the data, a method commonly used to tackle imbalanced datasets in classification tasks, the number of majority classes (malware) was adjusted to align with that of the minority classes (not malware). This adjustment helped avoid potential bias in the model towards the majority class. As a result, the number of records for both malware and nonmalicious software reached 1079 each, culminating in a balanced dataset consisting of 2158 records, although somewhat limited in size. Employing the undersampling technique yields advantages in terms of computational efficiency and reduces false alarms in the minority class, which might otherwise occur during training on an imbalanced dataset.  
The significance of creating a balanced dataset is not to be underestimated, as it ensures that the model is capable of accurately distinguishing between malware and nonmalicious software. By effectively managing class imbalance, the overall performance and reliability of a classification model are enhanced. This is particularly crucial in cybersecurity applications, where false alarms and misclassifications can have severe consequences. In this regard, the undersampling technique is a valuable tool for improving a model's generalizability and robustness in handling diverse and real-world datasets.  
Table 2 displays a range of performance metrics for each model, such as the accuracy, optimal loss values, confusion matrix, and the time required for executing each model. With respect to the accuracy, the models did not exhibit a substantial disparity, as they all led within the 0.83 to 0.84 range, which was deemed satisfactory for the limited number of records in this dataset. Nonetheless, it is crucial to take the loss value into account when assessing a model's efficacy. As evident from the table, the proposed model significantly outperformed the Xavier model in achieving the lowest loss value. The proposed model attained a loss value of 0.40, compared to the Xavier model's 3.47. This outcome suggests that the proposed model was more efficient in reaching the lowest possible loss values, while requiring a comparable or even shorter amount of time to train than the other models. A confusion matrix is included to highlight the similarities in values between both classes in the balanced dataset and to illustrate how these values differ in the upcoming section discussing the imbalanced dataset.  
Figure 3 depicts the changes in loss values for the neural network's balanced dataset throughout the training procedure. It is clear that the proposed model in Figure 3a started with a relatively low loss value and, by the conclusion of the training process, achieved a lower loss value than the Xavier model in Figure 3b. This observation implies that there could be opportunities to optimize the training process of neural networks of this nature, leading to enhanced computational efficiency and superior performance.
# Panoptic Segmentation Method Based on Feature Fusion and Edge Guidance
###### Abstract  
Panoptic segmentation, a pivotal research direction in computer vision, unifies pixel-level object and background recognition within a scene, crucial for applications like autonomous driving. However, existing methods, including State-of-the-Art models like Mask2 Former, often exhibit limitations such as inadequate adaptation in multi-scale feature fusion and ambiguous boundary segmentation, particularly for small objects in complex scenes. To address these specific challenges, we propose a novel network: PSM-FFEG (Panoptic Segmentation Model with Feature Fusion and Edge Guidance). PSM-FFEG introduces three key components: (1) A dynamic multi-scale feature fusion module enhancing contextual modeling via cascaded convolutions and adaptive attention. (2) An explicit edge guidance module refining boundary features with dedicated supervision. (3) A dual-path Transformer decoder optimizing cross-path feature interaction between pixels and queries. Extensive experiments on the Cityscapes and MS COCO datasets demonstrate that, using a ResNet50 backbone, PSM-FFEG achieves 2.6% and 2.4% absolute improvements in panoptic quality (PQ) over the Mask2 Former baseline, respectively. Notably, PSM-FFEG shows significant gains for small objects on Cityscapes, with PQ increasing by 4.3% for traffic lights and 6.8% for motorcycles. These results validate the effectiveness of the proposed modules. To foster further research, our implementation code will be made publicly available.  
deep learning; image segmentation; panoptic segmentation; feature fusion; edge guidance 2020
## 3 Result  
### Vegetation Cover Changes from 2001 to 2020  
In this study, using the land use data for the Loess Plateau in 2001 and 2020, it can be seen from Figure 2 that the land cover types of the Loess Plateau show subtle spatial differences. The whole region shows a general trend of overgrowth of forest to grassland areas from south to north, with forest areas being concentrated in the valley plains of the Guanzhong Basin, Fen River Valley, and Luoyang Basin (Figure 2), accounting for 12.07% of the total area of the Loess Plateau in 2001 and increasing to 14.28% in 2020 (Table 2). Grassland is the most abundant type of land cover category, mainly distributed in the desert areas in the north (Figure 2) and increasing from 49.12 to 50.24% in 2001. The arable land is mainly distributed in the loess areas of the Longdong Plateau and the plateau areas on both sides of the Weihe River in the south of the Loess Plateau (Figure 2). Since the project of \"returning crop to forest and grass areas\" started, the area of arable land has been reduced from 31.73 to 28.83% (Table 2). The area covered by shrub is the smallest, which has been stable at less than 1% for many years and shows a decreasing trend (Table 2).  
Although the areas of these four land cover types remained relatively stable from 2001 to 2020 (Table 2), the NDVI values showed an increasing trend (Figure 3a), and the vegetation cover of forest and scrub areas showed significant advantages compared with the other two classes (Figure 3c). Spatially, the NDVI values showed a significant increasing trend in most areas of the Loess Plateau, especially in the central-eastern part of the plateau (Figure 3b), where grassland is the main growth area. After counting the NDVI trends for different land cover types, it can be found that grassland has the fastest growth rate, followed by crop, while the slowest growth rate is for scrub (Figure 3d).