esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Numerical Simulation Methodology
### Fowcs Williams-Hawkings Acoustics Analogy  
In 1969, [PERSON] applied the generalized function method to extend [PERSON]'s result to the sound generation problem of arbitrary moving surfaces in an unbounded fluid. The FW-H equation is essentially an inhomogeneous wave equation that can be derived by manipulating the continuity equation and N-S equation [25; 26]. It can be written as:  
\[\begin{split}\frac{1}{a_{0}^{2}}\frac{\partial^{2}p^{\prime}}{ \partial t^{2}}-\
abla^{2}p^{\prime}&=\frac{\partial^{2}}{ \partial x_{1}\partial x_{j}}\big{\{}T_{ij}H(f)\big{\}}-\frac{\partial}{ \partial x_{i}}\big{\{}[P_{ij}n_{j}+\rho u_{i}(u_{n}-v_{n})]\delta(f)\big{\}} \\ &+\frac{\partial}{\partial t}\big{\{}[\rho_{0}u_{n}+\rho(u_{n}-v _{n})]\delta(f)\big{\}}\end{split} \tag{6}\]  
where \(u_{i}\) and \(v_{i}\) represent the flow velocity and surface velocity component in the \(x_{i}\) direction, \(u_{n}\) and \(v_{n}\) represent the flow velocity and surface velocity normal to the surface, \(\delta(f)\) is the Dirac delta function, and \(H(f)\) is the Heaviside function. In addition, \(p^{\prime}\) denotes the sound pressure at the far-field (\(p^{\prime}=p-p_{0}\)), \(a_{0}\) is the far-field sound speed, and \(T_{ij}\) is the Lighthill stress tensor, defined as:  
\[T_{ij}=\rho u_{i}u_{j}+P_{ij}-a_{0}^{2}(\rho-\rho_{0})\delta_{ij} \tag{7}\]  
where \(P_{ij}\) is the compressive stress tensor. Equation (6) contains the effects of all the sound sources in the flow field: The first term on the right reflects the quadrupole sound caused by the turbulent flow, the second term is the dipole sound caused by the solid surface force, and the third term is the monopole sound caused by the solid boundary motion.  
In the present study, the pantograph surfaces are set as integration surfaces and can be regarded as arbitrary rigid bodies; namely, the fluctuating volume quantity turns to zero. Therefore, there is no need to consider the monopole sources. Meanwhile, the Mach number (\(M\)) is set as 0.33 and the corresponding incoming flow velocity is 400 km/h. Moreover, the authors of [14] noted that the ratio of quadrupole source intensity to the dipole source intensity in flow field is proportional to \(M^{2}\). The noise intensity induced by quadrupole sources is significantly less than the dipole source, and thus the quadrupole sources can be ignored.
# Wintertime Local Wind Dynamics from Scanning Doppler Lidar and Air Quality in the Arve River Valley
## 4 Results
### Overview of the Wind Intensity over a Winter  
This section aims to provide a general overview of the wind distribution under stable conditions compared with more vertically mixed conditions. Figure 6 represents box plots of \(V_{los}\) extracted at 40 m AVL at two locations within the basin. These locations were chosen so that characteristics of the along- and cross-valley wind components could be inferred. Along (cross) valley components were extracted at azimuth 287\({}^{\circ}\) (350\({}^{\circ}\)) and 2600 m (700 m) from the lidar. Blue boxes represent the January-February database (_Jan-Feb_), which contains a large range of wintertime atmospheric conditions and associated wind patterns. Red boxes correspond to the stagnation stage of IOP1, described in Section 2.2 (_10-12 Feb_). The bottom and top of the box correspond to the 25 th and 75 th percentiles and the whiskers extend from the 1 st to the 99 th percentile, meaning that 98% of the database is between the two whiskers.  
For both along- and cross-valley winds, the extrema are significantly reduced for the _10-12 Feb_ dataset. This means that stable conditions limit the ventilation. To evaluate the distribution symmetry, the skewness (\(\gamma_{1}\)) was computed. For along-valley winds, the _Jan-Feb_ distribution is symmetric, whereas the _10-12 Feb_ is negatively skewed with \(\gamma_{1}=-0.27\). This asymmetry means that, over the period, there was a range of wind intensity, above 1.2 m\(\cdot\)s\({}^{-1}\), for up-valley winds that was not observed for down-valley winds. For cross-valley winds, the distribution ranges are less spread than for the along-valley component, which is consistent with the steep-sided basin shape. Both distributions are positively skewed meaning that, at least at the location considered (north of Site 2), the cross-valley winds blow more frequently from the south.  
Finally, this figure shows that the wind intensities are significantly weaker in the Passy basin bottom during stable conditions than in the full two months database. The observed asymmetry on both the cross and along valley wind distributions may be of major importance for pollutant dispersion. In the following, the analysis focuses on the dynamics during the persistent temperature inversion episode.
# RNN-Based Sequence to Sequence Decoder for Run-Length Limited Codes in Visible Light Communication
## 3 Learning to RLL Decode
### Dataset and Training Details  
(1) _Data set_: To train the decoder, by transmitting the RLL-coded frames over the VLC model in Section 2.1 on different SNRs, we can generate a sufficient number of samples of received signals \(\mathbf{y}\) and corresponding transmitted data (labels) \(\mathbf{x}\) as the training and testing datasets. Further, the proposed seq2 seq model can be trained over the whole codebook of RLL codes under various SNRs, since the codebook only contains up to dozens of codewords. This makes it easier to train a superior decoder.  
(2) _Training SNR_: Note that the SNR is unknown and time-varying during the actual decoding phrase (testing phrase); hence, we trained an RLL decoder with generalization capability. That is, we trained the decoder at a particular SNR, and tested it on different SNRs. Naturally, the performance of the seq2 seq decoder depends heavily on the SNR of the training samples. Next, section discusses the optimal training SNR for the seq2 seq decoder. Our work employed the proposed method in [8] to obtain the optimal training SNR, and we define a new performance criterion, which is called the mean error to MAP (MEMAP) as follows:  
\[\text{MEMAP}(s_{train})=\frac{1}{T}\sum_{s_{\text{train}}\in\mathbb{T}}|\text {BER}_{\text{82s}}(s_{train},s_{test})-\text{BER}_{\text{MAP}}(s_{test})|, \tag{7}\]  
where \(s_{train}\) and \(s_{test}\) represent the SNRs (defined by \(E_{b}/N_{0}\)) of training and testing sets, respectively. \(\text{BER}_{\text{82s}}(s_{train},s_{test})\) is the BER achieved by the seq2 seq decoder trained at SNR \(s_{train}\) and tested at SNR \(s_{test}\). \(\text{BER}_{\text{MAP}}(s_{test})\) is the BER of MAP decoding of RLL codes on SNR \(s_{test}\) and \(T\) denotes a set of different SNRs. The proposed criterion is able to evaluate how good a seq2 seq decoder is and help us search the optimal training SNR, the details of which will be presented in the next section. Obviously, with \(\text{MEMAP}=0\), the decoder achieves MAP decoding.  
(3) _Network configuration_: Two RLL look-up tables are allowed to be employed simultaneously in the VLC standard to enhance the system throughput, and it is worth noting that our proposed seq2 seq decoder is also adaptable to this task. If considering frame-by-frame transmission, i.e., the 4B6B codewords are transmitted and decoded one by one, the \(N\) of  
Figure 3: Offline training process and online decoding process of the proposed seq2 seq-based decoding scheme.  
the decoder is set to 6 and \(K\) is set to 4. If we consider multi-frame transmission in one time slot, the \(N\) is set to a multiple of 6 and \(K\) is set to a multiple of 4, e.g., \(N=12,18\), and \(K=8,12\). For the setting of other hyperparameters in seq2 seq decoder, we performed a lot of trials and determined relatively reasonable parameters that are summarized in Table 2. The performance of the decoder with this set parameters will be presented in the next section.
## 4 Experiment and Results
### Comparative Experiment of Different Target Detection Models  
We have carried out comparative experiments with the same advanced target detection models, including FCOS, YOLOv4, and YOLOv3, as shown in Table 4. The accuracy of our improved model is higher than that of other baselines. Similarly, the detection speed in 2080 Ti is 83 FPS/s, which is increased by 10 and 14 FPS/s, respectively, compared with YOLOv4 and YOLOv3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **AP/\%** & **Speed/ms** & **Recall/\%** \\ \hline YOLOv5s & 91.03 & 270 & 87.13 \\ YOLOv5s + CBAM & 93.42 & 310 & 88.21 \\ YOLOv5s + CBAM + AFFF & 93.86 & 320 & 88.91 \\ YOLOv5s + CBAM + AFFF + Purning & 93.32 & 180 & 88.78 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model Ablation Experiment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Model Size/MB** & **AP/\%** \\ \hline Original & 33 & 93.86 \\ First & 27 & 93.55 \\ Second & 21 & 93.32 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model results of twice pruning.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Dataset. A (AP/\%)** & **Dataset. B (AP/\%)** \\ \hline Original & 95.44 & 87.86 \\ Ours & 96.01 & 90.41 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of citrus fruit data sets with different occlusion degrees.  
Figure 9 visualizes the effect of our citrus detection model. We showed the detection effect of citrus fruits with mild occlusion and severe occlusion, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **AP/\%** & **FPS (In 2080 ti)/s** \\ \hline FCOS & 90.76 & 47 \\ YOLOv3 & 91.21 & 69 \\ YOLOv4 & 91.97 & 73 \\ Ours & 93.32 & 83 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Model comparison experiment.  
Figure 9: We visualized the detection effect of citrus fruits under different occlusion levels, In the figure, the words above all detected citrus fruits are ”orange” and their confidence.
## 1 Introduction
Classification algorithms train machine learning (ML) models, such as decision trees and k-nearest neighbors, using labeled data and testing them on new data. However, labeling data can be a complex and expensive process. An example of recent work in this area is that by [PERSON] in 2022 [17], who proposed a new classifier called AdaBoost, K-nearest neighbors, and logistic regression-based stacking ensemble (AKL-SE) to classify faults in WT monitoring systems, obtaining promising results. The aim of reconstruction models, in contrast, is to understand the time series and reconstruct the variables to detect anomalies, using the reconstruction error as a measure to identify outliers. A notable example of this approach is the research by [PERSON] et al. [18], which proposes a method for WT fault detection. This method, which combines a convolutional neural network (CNN) with a long- and short-term memory (LSTM) network based on an attention mechanism (AM), is designed to alert on generator and gearbox faults, making it highly relevant to the field of WT systems. In addition, deep learning (DL) networks have also been used in this area. [PERSON] et al. [19] propose a new Deep Residual Network (DRN) for WT fault detection. In their method, raw data from the SCADA system are directly used as inputs to the DRN, which incorporates a Convolutional Residual Building Block (CRBB) with convolutional layers and compression and excitation units. This approach performs fault recognition and classification only when faults occur or are imminent, although, in practice, it is preferable to provide early warnings before a fault occurs. A notable limitation is that most studies focus on detecting failures in mechanical or electrical components [20; 21; 22], while only some address failures in electronic components using SCADA data [23; 24]. In this context, one of the studies exploring converters is that by [PERSON] et al. [25], who propose an improved structure called attention octave convolution (AOctConv), applied to the ResNet50 backbone (called AOC-ResNet50) for detecting faults in WT converters using SCADA data with 10 min intervals. In line with this research, we study this component for its recurrent failures, as demonstrated in Section 2.3. Meanwhile, hybrid models are more robust than their base models, as evidenced in recent studies [26; 27]. However, a limitation of these approaches is that they tend to detect alarms and do not attempt to predict failures. An emerging approach in this context is the use of attention mechanisms, as presented in the study by [PERSON] et al. [28]. The authors propose a novel method using an integrated AM with a multivariate query pattern for anomaly detection and underlying cause identification. The proposed anomaly detection model comprises multiple cascaded encoders and decoders with a multihead self-attention mechanism to extract correlations between variables. Inspired by this approach, we propose integrating a similar mechanism into our architecture.
## 2 Materials and Methods
### Genome Assembly and Annotation  
High-quality DNA samples were extracted from two inbred lines, N531_R and its recurrent parent, N531, in 2-week-old seedlings to perform whole-genome sequencing. The de novo genome assembly process involved the three following steps: primary assembly, Hi-C scaffolding, and polishing. Genome assembly was performed through a sequencing library constructed for PacBio sequencing, Illumina sequencing, and BioNano optical mapping. For the generation of Hi-C data, genomic DNA was sequenced on an Illumina HiSeq 4000 platform and subsequently purified to obtain qualified DNA products suitable for library construction. Repeat regions in the N531_R and N531 genomes were identified through de novo predictions and homology-based annotation using Repeat-Modeler v2.0.1 [27]. Gene annotation combined ab initio training with evidence-based predictions for homolog evidence-based predictions, along with RepeatMasker v4.1.1 [28] and HelitronScanner v1.0 [29] for identifying repeat regions. For homolog evidence-based predictions, protein sequences from B73, Mo17, _Arabidopsis thaliana_, _Oryza sativa_, _Setaria italica_, and _Sorghum bicolor_ were aligned with the N531_R and N531 genomes. For de novo predictions, we used Augustus v3.2.3 and Fgenesh v8.0.0a for training on the repeat masked N531_R and N531 genomes. For function annotation of the protein-coding genes, protein domains were identified using PfamScan v34.0 [30], and Gene Ontology (GO) term annotation was calculated by Blast2 GO v6.0 [31] with default parameters.
# Towards Efficient Sink Mobility in Underwater Wireless Sensor Networks
## 4 Proposed Scheme
### Network Model  
Initialization phase starts with the logical division of network field into two equal parts vertically. The divided parts are further logically divided into five regions of equal size horizontally. Five regions on the left side of the mid-point of the network field are named \"left-5 regions\" and the regions on the right side of the mid-point of the networks field are named \"right-5 regions\". This logical division is done to have efficient movement of mobile sinks in the proposed scheme. All sensor nodes are randomly deployed with same energy level. It should be noted that node deployment strategies are out of the scope of this work. Three sinks are used: one sink is static and the other two are mobile. The static sink is placed at the top middle of the network field. Mobile sink one (MS1) and Mobile sink two (MS2) are utilized to gather data from the network field. The network Model is shown in Figure 1.
## 4 System Model
### Trajectory Design  
We introduce a technique named the Elliptical Shaped Efficient Data Gathering (ESEDG) for maximum data gathering and to balance energy utilization and reduce network delay. With the aid of GNs, the ESEDG can gather a large amount of data and decrease the network's end-to-end latency.
## 4 Experiments  
(1) Training Settings: This study implements the network model using the PyTorch 1.7 framework and conducts experiments on an NVIDIA RTX A3000 GPU with 12 GB of memory. As momentum-based stochastic gradient descent (SGD) has proven more effective for model optimization [36], the optimizer is configured with a momentum value of 0.9 and weight decay of \(1\times 10^{-3}\). The initial learning rate is set to 0.01, the batch size is fixed at 8, and the training process spans a maximum of 50 epochs.  
(2) Loss Function: Owing to the class imbalance present in the dataset, the training could be adversely affected if a suitable loss function is not chosen. To address this issue,  
Figure 4: The process of the spatial attention mechanism.  
this study adopts a combined loss function that integrates the Dice loss and cross-entropy loss. The joint formulation can be summarized as  
\[L_{ce}=-\frac{1}{N}\sum_{n=1}^{N}\sum_{k=1}^{K}y_{k}^{(n)}\text{log}\,\hat{y}_{k }^{(n)} \tag{6}\]  
\[L_{dice}=1-\frac{2}{N}\sum_{n=1}^{N}\sum_{k=1}^{K}\frac{\hat{y}_{k}^{(n)}y_{k}^ {(n)}}{\hat{y}_{k}^{(n)}+y_{k}^{(n)}} \tag{7}\]  
\[L=L_{ce}+L_{dice} \tag{8}\]  
where \(N\) denotes the number of samples, and \(K\) denotes the number of classes. The terms \(y^{(n)}\) and \(\hat{y}^{(n)}\) represent the ground-truth label and the predicted outcome, respectively.  
(3) Evaluation Metrics: In this experiment, we employ three metrics commonly used in remote sensing segmentation tasks--accuracy (\(Acc\)), the intersection over union (\(IoU\)), and the \(F1\)-score--to evaluate the performance of our model. Each of these metrics is derived from the confusion matrix, which consists of four key components: true positives (\(TP\)), false positives (\(FP\)), true negatives (\(TN\)), and false negatives (\(FN\)).  
The formula for the calculation of Acc is expressed as follows:  
\[Acc=\frac{TP+TN}{TP+FP+TN+FN} \tag{9}\]  
The formula for the calculation of the \(IoU\) is expressed as follows:  
\[IoU=\frac{TP}{TP+FP+TN} \tag{10}\]  
The formula for the calculation of \(F1\) is expressed as follows:  
\[Precision=\frac{TP}{TP+FP} \tag{11}\]  
\[Recall=\frac{TP}{TP+FN} \tag{12}\]  
\[F1=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{13}\]
# Deep Reinforcement Learning for Resource Management on Network Slicing: A Survey
## 1 Introduction
Though there are various surveys involving DRL and resource management [14; 25; 26; 27; 28; 29], this survey is purposefully different. Reference [25] reviews DRL techniques without focusing on the networking domain. Unlike [14], which presents a comprehensive survey on ML for networking, and [27; 28; 29], which introduces a complete revision on DRL techniques for networking, communication networks, and HetNets, our work focuses on approaches using RL and DRL for realizing resource management in NSL. Reference [26] presents, as we do, a survey on network slicing resource management. However, it does not discuss resource management from the perspective of its constitutive phases (admission, allocation, scheduling, and orchestration), includes only a constrained research directions section, and is outdated (published in 2018). To sum up, in contrast to the existing surveys, this paper addresses the following research questions: (i) What are the phases of network slicing resource management and which RL/DRL-based approaches are useful in each phase? (ii) What are the research directions on RL/DRL-based network slicing resource management?. The contributions of this paper are:  
* A comprehensive view of RL/DRL-based resource management in NSL. The literature published in peer-reviewed venues over the past four years that have a high impact and have been well received by peers is explored and analyzed from the perspective of the main resource management phase in which each proposed approach operates. In addition, the elements used per the RL/DRL technique are detailed for each approach. * Identification of challenges and research directions in network slice resource management. The presented discussion on RL/DRL-based resource management in NSL uncovers fundamental research challenges to achieve cognitive and autonomous 5G, 6G, and beyond networks. The discussion motivates performing future work to push the boundaries of cognitive networking. The rest of this paper is organized as follows. Section 2 presents the methodology used to compile our survey and the fundamental concepts needed to understand it. Sections 3-6 describe works that use RL and DRL to perform admission control, resource allocation, resource scheduling, and resource orchestration, respectively. Section 7 raises challengesand future research directions on RL/DRL-based resource management in NSL. Section 8 concludes this survey. For the sake of readability, Abbreviations provides the list of acronyms and definitions used in this survey.
# Antioxidant and Antimicrobial Properties of Selected Phytogenics for Sustainable Poultry Production
## 4 Selected Plant Sources of Antimicrobial and Antioxidant Compounds
### Aloe vera  
_Aloe vera_ is one of over 420 species in the genus _Aloe_, which has been variously categorized as belonging to the _Asphodelaceae_, _Lilliaceae_, or _Aloaceae_ families [116]. It is a succulent, perennial xerophyte with thick, fleshy, pointed leaves that cluster at the stem. _Aloe vera_ is ubiquitous in components with biological activity, such as polysaccharides, phenolic compounds, minerals, water- and lipid-soluble vitamins, organic acids, and lipids [117]. It is made up of 96% water and 4% dry matter, containing protein, fat, dietary fiber, and 75 other biologically active compounds [118,119]. Anthraquinones, saccharides, vitamins, enzymes, and low-molecular-weight compounds are the main components of _A. vera_ and are responsible for its anti-inflammatory, immunomodulatory, wound-healing, antibacterial, antiviral, antifungal, anti-tumor, anti-diabetic, and antioxidant properties [120].  
In addition, other phytochemicals, such as alkaloids, anthraquinones, anthrones, chromosomes, coumarins, flavonoids, lignin, naphthalene, saponins, sterols, pyrans, and pyrones, have been isolated from the plants of the genus [121]. In poultry nutrition, the incorporation (1.5, 2, 2.5%) of _A. vera_ gel in broiler feed reduced the _Escherichia coli_ count [122]. Similarly, [PERSON] et al. [123] found that _A. vera_ products increased _Lactobacillus spp._ and _Bifidobacteria_ while decreasing the amount of _E. coli_. In another study, [PERSON] and [PERSON] [124] reported improved feed utilization efficiency upon adding 1.5%, 2%, and 2.5% of _A. vera_ gel to the diet of broilers with coccidiosis. The authors stated that this could be attributed to the potential of _A. vera_ to enhance the intestinal health and immune system response in the birds.
# An Eccentricity Based Data Routing Protocol with Uniform Node Distribution in 3D WSN
###### Abstract  
Due to nonuniform node distribution, the energy consumption of nodes are imbalanced in clustering-based wireless sensor networks (WSNs). It might have more impact when nodes are deployed in a three-dimensional (3D) environment. In this regard, we propose the eccentricity based data routing (EDR) protocol in a 3D WSN with uniform node distribution. It includes network partitions called 3D subspaces/clusters of equal member nodes, an energy-efficient routing centroid (RC) nodes election and data routing algorithm. The RC nodes election conducts in a quasi-static nature until a certain period unlike the periodic cluster heads election of typical clustering-based routing. It not only reduces the energy consumption of nodes during the election phase, but also in intra-communication. At the same time, the routing algorithm selects a forwarding node in such a way that balances the energy consumption among RC nodes and reduces the number of hops towards the sink. The simulation results validate and ensure the performance supremacy of the EDR protocol compared to existing protocols in terms of various metrics such as steady state and network lifetime in particular. Meanwhile, the results show the EDR is more robust in uniform node distribution compared to nonuniform.  
Keywords:wireless sensor network; three-dimensional space; data routing; network lifetime +
Footnote †: journal: Sensors