esa-sceva/satcom-chunk-collection huggingface dataset:
# A Method for Enhancing the Accuracy of Pet Breeds Identification Model in Complex Environments
## 1 Introduction
In this way, we have developed a dataset for breed classification of cats and dogs, comprising 5550 images uniformly distributed across 37 breed categories, named the CPI (Complex Pet Images) dataset (Figure 1). We utilize this dataset to fine-tune (Figure 2) three state-of-the-art image classification models, pre-trained on the ImageNet dataset [7]: Swin Transformer [16], DenseNet121 [17], and ResNet34 [18], testing their recognition performance in complex environments. Additionally, we present a lightweight cat and dog breeds identification model PBI-EdgeNeXt designed for edge devices (see Figure 3), which can accurately identify the breed of a cat or dog from images captured in various scenarios. The main contributions of this paper are given as follows:  
(1) We construct a complex image dataset derived from the Oxford IIIT Pet dataset. This dataset is utilized for fine-tuning the model to improve its classification accuracy in non-ideal environmental images. (2) We apply knowledge transfer to the PBI-EdgeNeXt model using the CPI dataset and subsequently develop a lightweight pet breeds identification model for edge devices. (3) Through rigorous experimentation and analysis, it is demonstrated that our CPI dataset significantly enhances the accuracy of pet breeds identification in complex environments. Furthermore, our PBI-EdgeNeXt model outperforms the other five compared models. The organization of this article is as follows: In the second part, we critically analyze existing research on pet classification, discussing its strengths and contributions while highlighting the shortcomings. The third part introduces the CPI dataset and outlines the general method for fine-tuning models using the dataset. Additionally, it presents a mobile-oriented pet cat and dog breeds identification model based on PBI-EdgeNeXt. The final chapter details our experimental work. The summary section provides a recap and synthesis of the research conducted in this work.
Generally, the photosynthetic rate and sugar accumulation increase linearly with photosynthetic photon flux density (PPFD), but at higher PPFDs, the formation of reactive oxygen species (ROS) is more abundant because of photo-oxidative stress, and consequently, the redox balance of plant tissues may be disturbed [33]. The most important ROS are hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)), superoxide radical (O\({}_{2}\)\({}^{\bullet-}\)), hydroxyl radical (HO\({}^{\bullet}\)), and singlet oxygen (\({}^{1}\)O\({}_{2}\)). ROS may be produced in almost every subcellular compartment, except for the chloroplast-specific \({}^{1}\)O\({}_{2}\), and they are important signalling molecules, but their excess induces oxidative stress [34; 35; 36; 37; 38; 39; 40]. The antioxidant system maintains an optimal level of ROS [22] and protects cells and cell compartments against oxidative damage under various environmental stresses [41; 42]. Besides the ascorbate-glutathione cycle, \(\alpha\)-tocopherol, carotenoids, flavonoids, thioredoxinus (TRXs), peroxidiredoxins (PRXs), superoxide dismutase (SOD), catalase (CAT), peroxidase (POD), and glutathione S-transferases (GSTs) are the most important antioxidants [43; 44; 45; 46; 47; 48; 49; 50]. Both ROS and antioxidants are involved in the light-dependent redox regulation of metabolism, which plays an important role in the adaptation of plants to changes in environmental conditions.
## 4 Enabling Technologies for FSO Communication
### Spatial Diversity
#### 4.6.1 Overview
Additionally, the performance degradation resulting from spatial correlation can be significant, highlighting the importance of efficient separation between apertures and precise co-alignment to realize the expecteddiversity benefits from multiple Txs and Rxs [263]. A key observation is that as the degree of diversity--whether spatial, temporal, or frequency--increases, performance improves significantly and rapidly [47; 254].
## 2 Numerical and Analytical Methods Description
### Analytical Method Description  
The analytical model used in this study is based on the well-established formulation by [PERSON], which describes the distribution of peel and shear stresses within the adhesive layer of a single lap joint. The peel stress and shear stress can be derived by considering the equilibrium of forces and moments in the adherends and the adhesive layer along with some simplifying kinematic assumptions. These stresses are typically expressed as functions of the applied load, adhesive thickness, and adherend stiffness, and are defined based on the bending moments induced by the eccentric loading of the joint, and the load transfer through the adhesive layer. The final expressions for the shear stress \(\tau\) and peel stress \(\sigma\) are given by the following:  
\[\tau=\frac{\beta_{\tau}(Ft_{\text{P}}+6M_{\text{k}})\cosh\beta_{\tau}x}{8t_{ \text{P}}\text{sinh}\beta_{\tau}c}+\frac{3(Ft_{\text{P}}-2M_{\text{k}})}{8t_{ \text{P}}c} \tag{1}\]  
\[\sigma=\frac{2E_{\text{A}}}{t_{\text{A}}}(B_{\sigma 1}\text{sinh}\beta_{ \sigma}x+B_{\sigma 4}\text{cosh}\beta_{\sigma}x\text{cos}\beta_{\sigma}x) \tag{2}\]  
where \(B_{\sigma 1}\) and \(B_{\sigma 4}\) are integration constants [31].  
\(\beta_{\tau}\) and \(\beta_{\sigma}\) are eigenvalues and are defined as \(\beta_{\tau}=\sqrt{\frac{8G_{\text{A}}}{E_{\text{P}}t_{\text{A}}\text{P}}}\), and \(\beta_{\sigma}=\frac{\sqrt{2}}{2}\sqrt[4]{\frac{24E_{\text{A}}}{E_{\text{P}}t_ {\text{A}}^{3}}}\)  
\[k=\frac{1}{1+2\sqrt{2}\text{tanh}\Big{(}\beta_{k}c/2\sqrt{2}\Big{)}} \tag{3}\]  
where \(F=\) axial force, \(t_{\text{P}}=\) thickness of the adherend, \(t_{\text{A}}=\) thickness of the adhesive, \(E_{\text{A}}=\) adhesive Young's modulus, \(E_{\text{P}}=\) adherent Young's modulus, \(G_{\text{A}}=\) adhesive shear modulus, \(c=\) half of the adhesive overlap, \(x=\) position along the overlap from the midpoint, and \(k=\) edge moment factor formula.  
Several key assumptions are made to simplify the analytical formulation of the single lap joint. First, the adherends are assumed to be linearly elastic, isotropic, and of uniform thickness, which allows for the use of classical beam theory in their analysis. The adhesive layer is also assumed to be linearly elastic and of uniform thickness, with no initial defects or voids. Furthermore, the model assumes that the adhesive deforms primarily in shear, with negligible normal strains. The overlap length of the joint is considered sufficient to develop the full strength of the adhesive, and the effects of edge stresses are neglected. While the assumption of elasticity will be violated in the FE simulations of this study at higher applied displacement levels, the FE simulations will be assessed for their consistency with this analytical model at lower applied displacements for which all stresses are below the elastic limit of the adhesive and adherends.
## 5 Deep Learning Models
### Temporal Convolutional Networks  
TCNs, one-dimensional CNN architectures, utilize a temporal convolutional layer that operates on the steps of each time series. In this context, \"temporal\" implies that the activations calculated for a specific time step are independent of future time steps. Multiple convolutional layers are often added together to establish context from previous time steps. The dilation factor of consecutive convolutional layers increases exponentially to achieve larger receptive field sizes. The general structure of a TCN consists of a series of residual blocks. Each block comprises two sets of dilated temporal convolutional layers with the same dilation factor. Following these layers, normalization, ReLU activation function, and spatial dropout layers are applied. The network aggregates the output of each block with the input of the block, ultimately obtaining the output by applying an activation function [78].  
To construct the AI models, subsets were allocated 80% for training, 10% for validation, and 10% for testing. During the validation phase, random search hyper-parameter optimization was performed over 100 iterations using a parameter space defined by Table 5. As in [79], a sequence-to-one approach was used for the surrogate model in this work. In constructing TCNs, key parameters included dilation factors for capturing long-range dependencies, filter sizes for temporal extent, number of filters for feature depth, dropout rates for preventing over-fitting, mini batch size for training efficiency, and an optimizer for parameter updates.
## 4 Description of the System
### Incorporation of New Components  
In addition to the components described in the previous section, which are commonly available in highway public transit infrastructure, the system employs two new components. The following components are added to the vehicle equipment.
## 3 Results and Discussion  
### Synthesis and Gel Permeation Chromatography  
While the pure PS and PETE appeared colorless, the materials synthesized through in situ polymerization with MWNT/PS and MWNT/PETE exhibited a black coloration, indicating the successful incorporation of the MWNTs. There was no significant difference in the reaction time between the synthesis of the pure PS and PETE and that of the MWNT-incorporated materials. These results indicate that the presence of MWNTs did not interfere with the polymerization kinetics. The comparable reaction times are likely due to the uniform dispersion of the MWNTs within the reaction system at relatively low concentrations (1 wt% and 2 wt%). To evaluate the molecular weights of the PS and PETE, a GPC analysis was conducted (Table 1). The pure PS exhibited an Mn of 6600 g/mol, an Mw of 16,900 g/mol, and a dispersity (D) of 2.56. In the case of the pure PETE, the Mn and Mw values were 2640 g/mol and 4250 g/mol, respectively, with a D of 1.61. This relatively low dispersity suggests that the PETE formed polymer chains with a more uniform molecular weight distribution. This difference can be attributed to the distinct polymerization mechanisms of the two systems. PS is synthesized via a radical initiation process, in which polymer chains with a wide range of molecular weights are rapidly and randomly formed. In contrast, PETE is formed through a gradual step-growth polymerization between monomers, which tends to produce more uniform polymer chain lengths [27; 28].
## 3 Results and Discussion
### CO Diffusion Law
#### 3.2.3 Effect of \(\theta\) on the CO Diffusion
This phenomenon indicates a general trend that the smaller the \(\theta\) value, the earlier the pollutant front reaches the open end. The simulation results are in agreement with the experimental results. As for the phenomenon of low peaks at large \(\theta\) values, this is due to the relatively lower concentration as the diffusion distance becomes larger. Therefore, based on the combined results of experiments and numerical simulations, it can be concluded that the distribution of CO is in an inverted trapezoidal shape. The larger the \(\theta\) value, the larger the contamination range becomes, while causing the CO concentration at specific points to decrease. Meanwhile, at smaller slopes, there is a general tendency for CO to be transported to the open end earlier.
## 3 Case Study
### New Hanger Installation Process  
The installation process of the new #10 hanger was calculated in five stages with the same step size based on the equations in Section 2.3, that is, the pocket hanging force and unloading force of each stage were 20% of the initial internal force of the pocket hanging hanger, whose calculated results are shown in Table 2 and Figure 11. The internal force was controlled by a hydraulic jack in the process of hanger tension and unloading force.  
It can be seen from Table 2 that the internal force increase of the new hanger was basically the same as the internal force decrease of the pocket hanging hanger after two rounds of tensioning and unloading, while the accumulative displacement showed an alternating trend of rise and fall, and the rise and fall were basically the same when the new hanger was installed by means of equal step length tensioning and unloading. The maximum accumulative displacement was 3.12 mm, which meets the requirements.
## 1 Introduction
For example, a set of processed dialogues (corpus) of realized CSE attacks can be tagged in sentence-level to discriminate the malicious sentences from the benign ones. A ML/NLP algorithm trained with this corpus will be able to efficiently recognize a malicious sentence in a future dialogue. The efficiency of the algorithm is related to the level of consistency between the content of the dataset and the researcher's domain of interest, i.e., if one asks how to detect phishing emails, then she needs a corpus of emails that gives insight into the question asked. To the best of our knowledge, there is a lack of corpus composed of realized CSE attacks, in contrast with other types of attacks like network intrusions, because CSE dialogues are rarely going public. Furthermore, a CSE corpus should be of sufficient quantity so the algorithms can be trained well and be efficient. In case the collected dialogues are not of sufficient quantity, researchers apply resampling techniques [9; 10] to enlarge the corpus, or they use annotation [11] to add useful in-context information as metadata to every dialogue. The metadata information augments the algorithms' effectiveness by providing pointers to what is important in a corpus. The ML/NLP algorithms extrapolate rules from the metadata provided in order to apply those rules later to unannotated text dialogues. An annotation task frequently employs a custom XML representation scheme depending on the context used. While there are some well-known annotation schemes, there is no universal standard for annotating dialogues. Furthermore, there are multiple ways for this metadata information to be stored, either as inline annotation (metadata are stored in same file as the dialogue), or stand-off annotation (metadata are stored in a separate file). This work is targeting to answer specific research questions by investigating:  
* The feasibility of collecting and building a CSE attack corpus to address the lack of training data composed of realized CSE attacks. * The feasibility of annotating the linguistic characteristics of social engineers' language in order to constitute an adjuvant factor for the ML algorithms' training. * The usage of the produced CSE corpus towards an early-stage automated CSE attack recognition by training a NER system that is able to identify critical in-context terms. The paper is structured as follows. The current trends and work of utilizing ML/NLP algorithms used for social engineering attack recognition are described in Section 2. Section 3 presents the proposed approach comprised of a conceptual framework and the methodology followed to build and annotate a specific-purpose CSE corpus. The application of the methodology and the presentation of the results is presented in Section 4. Section 5 presents the evaluation of the annotated CSE corpus on training a Named Entity Recognition (NER) system based on a bi-LSTM neural network. The paper concludes in Section 6.
###### Abstract  
The advancement of the sensing capabilities of end devices drives a variety of data-intensive insights, yielding valuable information for modelling intelligent industrial applications. To apply intelligent models in 5G and beyond, edge intelligence integrates edge computing systems and deep learning solutions, which enables distributed model training and inference. Edge federated learning (EFL) offers collaborative edge intelligence learning with distributed aggregation capabilities, promoting resource efficiency, participant inclusivity, and privacy preservation. However, the quality of service (QoS) faces challenges due to congestion problems that arise from the diverse models and data in practical architectures. In this paper, we develop a modified long short-term memory (LSTM)-based congestion-aware EFL (MLSTM-CEFL) approach that aims to enhance QoS in the final model convergence between end devices, edge aggregators, and the global server. Given the diversity of service types, MLSTM-CEFL proactively detects the congestion rates, adequately schedules the edge aggregations, and effectively prioritizes high mission-critical serving resources. The proposed system is formulated to handle time series analysis from local/edge model parameter loading, weighing the configuration of resource pooling properties at specific congestion intervals. The MLSTM-CEFL policy orchestrates the establishment of long-term paths for participant-aggregator scheduling and follows the expected QoS metrics after final averaging in multiple industrial application classes.  
congestion-aware collaboration learning; edge federated learning; industrial applications; quality of service; long short-term memory +
Footnote †: journal: Electronics  
Article
## 4 Proposed Protocol: Probing of Devices  
Probes are short messages sent across devices in the network to exchange information between them. To select a relay, probing messages are exchanged between a source, a base station, other devices in proximity, and a destination. The design of our communication protocol for relay selection in D2D communications is shown in Figure 2. It consists of three phases: Initiation phase, Probing phase and Data transmission phase.  
The initiation phase begins when a source wants to have a relay for D2D communication. The source sends a probe (D2D request) to the BS consisting of source and destination identity. As a response, the BS sends a message to the source containing a list of idle devices that are located within certain search radius (\(R\)) along with corresponding battery level and maximum allowable transmission power (\(P_{max}\)) that can be used for D2D communications as implemented in Algorithm 1. The tasks performed by BS in this proposed scheme is similar to that of MRS-ST in Reference [10]. However, the difference in this paper is that the BS sends additional battery level information of devices to the source.  
Upon receiving message from the BS, the source determines the socially trusted devices in the list using its contact list and initiates probing of those devices sequentially [7].  
In the probing phase, a source probes the nearby socially trusted devices to learn about link conditions for relaying data from the source to the destination as implemented in Algorithm 2 for MRS-ST-BL and Algorithm 3 for MRS-SD-BL. The probe message contains source identity, destination identity, probed device identity and maximum allowable transmission power. The message is also received by the destination. As a reply to a probe, each of the devices which are available for relaying data, individually responds to the source with SNR of the probe signal they have received and also forwards the probe message to the destination. The destination calculates SNR for each of the received probing packets and sends the SNR values to the source. The source calculates estimated data rate offered by the probed devices based upon link conditions and transmission power, and selects a device which offers maximum rate.  
Figure 2: Probing for relay selection.  
The source takes into account time wasted while probing devices. The more candidate devices to be probed, the more time is wasted for real communications. The source estimates the throughput offered by that selected device and compares it with the direct communication. Data transmission phase starts after the source decides whether to use the selected device as a relay or not. If a relay is used for D2D communications, the source sends the data to the relay, which is then forwarded to the destination.
###### Abstract  
Recently, a multi-agent based network automation architecture has been proposed. The architecture is named multi-agent based network automation of the network management system (MANA-NMS). The architectural framework introduced atomized network functions (ANFs). ANFs should be autonomous, atomic, and intelligent agents. Such agents should be implemented as an independent decision element, using machine/deep learning (ML/DL) as an internal cognitive and reasoning part. Using these atomic and intelligent agents as a building block, a MANA-NMS can be composed using the appropriate functions. As a continuation toward implementation of the architecture MANA-NMS, this paper presents a network traffic prediction agent (NTPA) and a network traffic classification agent (NTCA) for a network traffic management system. First, an NTPA is designed and implemented using DL algorithms, i.e., long short-term memory (LSTM), gated recurrent unit (GRU), multilayer perceptrons (MLPs), and convolutional neural network (CNN) algorithms as a reasoning and cognitive part of the agent. Similarly, an NITCA is designed using decision tree (DT), K-nearest neighbors (K-NN), support vector machine (SVM), and naive Bayes (NB) as a cognitive component in the agent design. We then measure the NTPA prediction accuracy, training latency, prediction latency, and computational resource consumption. The results indicate that the LSTM-based NTPA outperforms compared to GRU, MLP, and CNN-based NTPA in terms of prediction accuracy, and prediction latency. We also evaluate the accuracy of the classifier, training latency, classification latency, and computational resource consumption of NITCA using the ML models. The performance evaluation shows that the DT-based NITCA performs the best.  
network management automation; multi-agent system; network traffic classifier; network traffic predictor; machine learning; deep learning; 5G/6G; future network architecture; service-oriented architecture
## 3 Experimental Facilities
### Bubbly Flow in Vertical Pipe  
An air-water two-phase bubbly flow system in a vertical tube was built to get the local flow parameter by both four-sensor probe and visual techniques. The test facility and flow rate were chosen for obtaining a simple and steady two-phase flow pattern. As shown in Figure 6, a transparent glass tube with the length of 0.5 m and internal diameter of 8 mm was used as the test section, and water was filled up to a height of 0.4 m during the experiment. Air produced by air compressor and regulated by surge tank and control valve was injected from the bottom of the tube. The air flow rate was maintained at 0.1 L/min during the test. As a result, it was found that a steady series of cap bubbles was produced inside the tube.  
The top of the transparent tube was open to the atmosphere, and the four-sensor probe was vertically mounted in a one-dimensional sliding module with its tip pointing downside, so the probe could move horizontally to measure parameters across the tube diameter transversely. Only the 4.5 mm in the middle part of the 8-mm-diameter tube was accessible for measurement, resulting in a range from \(-2.5\) to 2 mm with the interval of 0.5 mm.  
The DC power supply of the probe was from a 9-V battery to avoid voltage fluctuation characterizing AC power supplier. The probe was connected to a data acquisition card which then transferred the collected data to a laptop. The data collection system was able to collect and transform the analog electrical signal to digital form at frequency of 10 kHz for each of the four sensors. For every transverse position of the probe in the tube, the data collecting persisted for 80 s and thus 800,000 data points were obtained for each sensor.  
A high-speed camera was employed to record the images of bubble in a system without a probe. Images of 1262-pixel vertically and 710-pixel horizontally with a frequency of 50 fps were shot for 80 s. Through the image processing and analysis, the local flow parameters of the two-phase flow could be obtained, which is typical for the so-called visual measurement.