esa-sceva/satcom-chunk-collection huggingface dataset:
# Rebar Recognition Using Multi-Hyperbolic Attention in Faster R-CNN
###### Abstract  
Rebar constitutes a crucial element within tunnel lining structures, where its precise arrangement plays a pivotal role in determining both structural stability and load-bearing capacity. Due to the rebar's high dielectric constant approaching infinity, radar signal reflections are intensified, manifesting as distinct hyperbolic patterns within radar imagery. By performing convolutional operations, these hyperbolic features of rebar can be effectively extracted from radar images. Building upon the feature extraction capabilities of the ResNet50 model, this study introduces a Deformable Attention to Capture Salient Information (DAS) mechanism, employing deformable and separable convolutions to enhance rebar localization and concentrate on hyperbolic features resulting from multiple reflections. Before the Region Proposal Network (RPN) and region of interest (ROI) pooling stages in Faster R-CNN, this study integrates a hyperbolic attention (HAT) module. Through refined distance metrics, the hyperbolic attention mechanism enhances the network's Precision in identifying rebar hyperbolic features within feature maps. To ensure robustness across diverse conditions, this study utilizes a simulated public dataset for tunnel linings, alongside real data from the Husa Tunnel, to create a comprehensive ground-penetrating radar image dataset for tunnel linings. Experimental results indicate that the proposed model achieved an Average Precision of 94.93%, reflecting a 3.14% increase compared to the baseline model. Lastly, in a random selection of 50 radar images for testing, the model achieved a rebar detection accuracy of 93.46%, representing an enhancement of 0.94% over the baseline model.  
Keywords:faster R-CNN; tunnel lining; ground-penetrating radar (GPR); hyperbolic features; rebar recognition
# Intelligent TCP Congestion Control Policy Optimization
## 2 Related Work
### Deep Reinforcement Learning-Based Congestion Control  
The congestion control strategy framework based on deep reinforcement learning is shown in Figure 2. Deep reinforcement learning requires the construction of environments and agents. Taking the network environment as the environment, by collecting the real-time state of the network environment, the strategy function used by the agent is constructed, the agent responds after learning, and the strategy function is fitted by an artificial neural network. The agent makes the optimal control strategy according to the output of the policy function, controls the congestion window length, and changes the TCP sending policy. After the agent makes an action, deep reinforcement learning will judge the action according to the state, so as to output the reward value and depending on the reward value, modify the parameters of the artificial neural network so that the agent can maximize the reward.  
The QTCP algorithm is an algorithm based on the Q-learning framework. Its state space is continuous, and its state includes average RTT, average interval time between sending and receiving packets, and discrete action spaces including increasing by 10 bytes, decreasing by 1 byte, and remaining unchanged. Next, the system compares the size of the utility function of the current time period and the previous time period to determine whether it is a positive reward or a negative reward, where the utility function \(U=a\cdot\log(throughput)-b\cdot\log(RTT)\). QTCP algorithms perform better than traditional algorithms in a variety of network scenarios, but the effect is still linked to network scenarios and cannot completely avoid the shortcomings of traditional congestion control algorithms. The congestion control algorithm Indigo, based on imitation learning, sets the network scene information to expert knowledge, and the decision network is a single-layer LSTM network, to achieve congestion control in the current training scenario. However, the performance of the algorithm can only play a superior performance in the trained scenario, so the practical application is limited. In contrast, DRL-based congestion control algorithms only require a simple neural network and combine historical information based on multiple time slice states before the current moment to obtain performance beyond traditional algorithms, so we will take a closer look at DRL-based congestion control algorithms.
## 3 Materials and Methods
### Model Architecture  
The PHQ-V and GAD-V models consisted of a single response block, a multimodal block, and a multi-response block, as can be seen in Figure 3. These three blocks were established to address the problem of combining five different video questions over three different modalities to provide two scores per participant. The single response block was responsible for scoring a single question, the multimodal block for combining modalities, and the multi-response block for combining multiple questions. Section 4 compares the scores from a few experiments that used slightly different architectures; the differences were as follows:  
1. Single Question/Single Modality. A modality-specific input is passed to the single response block, and scores are returned directly from it.
2. Single Question/All Combined. All three modality inputs are passed to the multimodal block, the scores are returned directly.
3. Multiple Question/Single Modality. A modality specific input is passed to the single response block, and output embeddings are then used as the input to the multi-response block (one per question). Scores are returned from the multi-response block.
4. Multiple Questions/All Combined. All three modalities are passed to the multimodal block and the output embeddings are then used as input to the multi-response block (one per question). Scores are returned from the multi-response block.  
The use of a transformer block is referenced; in all cases where this is mentioned it refers to the encoder half of the transformer as described previously [9] and shown in Figure 4. In general, pre-trained transformers were utilized for the first module in this research because they have been shown to be highly effective for transfer learning [11], and indeed they returned strong results for this dataset. The downstream transformer encoder blocks were  
Figure 2: Distribution of participants by (**a**) gender, (**b**) ethnicity, and (**c**) age.  
not pre-trained and were found to be more effective than concatenating class embeddings, especially where full questions were omitted. This is likely due to the attention mechanism's ability to mask certain tokens. Also shown in Figure 4 is the prediction head block, which is a simple feed-forward network that takes the final class embeddings to predict the PHQ-9 and GAD-7 scores. Below, the three main blocks are discussed in further detail.
# A Study of Network Intrusion Detection Systems Using Artificial Intelligence/Machine Learning
## 3 IDS Performance Evaluation
### F-Measure  
Corresponds to the harmonic mean of the Precision and Recall. It is used to provide a better evaluation of the system by showing the gap between the two metrics to see if the solution is balanced. This metric is also known as the F-Score or the F1-Score.  
\[\text{F}-\text{Measure}=2\times\frac{(\text{Precision }*\text{ Recall})}{(\text{Precision }+\text{Recall})}\]  
Since the attack detection rate and false alarm rate are often opposed to each other, evaluation of IDSs is also performed using Receiver Operating Characteristics (ROC) analysis. A ROC curve, as shown in Figure 1, represents the trade-off between attack detection rate and false alarm rate. The closer the ROC curve is to the top left corner the more effective the IDS is [4]. As shown in Figure 1, ROC Curves can also be used to compare different IDS using the same dataset.  
IDSs can also be evaluated according to their time performance. The time performance corresponds to the total time that the IDS needs to detect an intrusion. This time is composed of the processing time and the propagation time. The processing time is the  
Figure 1: ROC Curves for different IDSs.  
time needed by the IDS to process the information to detect an attack. The processing speed of the IDS needs to be as fast as possible, if not, real-time processing of intrusion is not feasible. The propagation time is the time needed to propagate the information to the security analyst or the Security Operation Centre (SOC). Both times, processing time and propagation time need to be as short as possible to allow security analysts to have enough time to react to an attack in real-time [4].
## 2 The SASW Method
### The Field Arrangement of SASW Method  
The SASW system is composed of several hammers of different sizes, two signal receivers, and a dynamic signal analyzer, as shown in Figure 1. It is primarily used to measure the Rayleigh wave between two receivers at given distances on the ground. The small hammer, serving as the impact source, strikes the ground to generate Rayleigh wave signals within a range of frequencies. The two receivers are vertically placed on either side of the measurement location for receiving and recording the Rayleigh wave signals. The dynamic signal analyzer has a preliminary filtering and fast Fourier transform process for pre-processing the received signals. In this study, the sampling rate is set at 20 microseconds, the total number of data points per sampling is set at 1024, the total duration of time-domain signals is set at 7168 microseconds, and the highest frequency of the signal can reach up to 7 kHz.  
During the measurement procedure, the distance between the two receivers should be set equal to the distance between the first receiver and the impact source. At each measurement location, the SASW method requires several repeat experiments by gradually increasing the distance between the receivers. This is because the receiver distance is also related to the measured wavelength of the Rayleigh wave: the longer the distance, the longer the wavelength of the Rayleigh wave that can be collected. Thus, this measurement procedure makes it possible for the acquisition of signals covering different wavelength (frequency) ranges, thereby obtaining the properties of deeper layers.
# Multi-formalism Models for Performance Engineering
## 3 Queueing Networks Techniques
### MapReduce with Class Switch
#### 3.3.3 Model Description  
A simple MapReduce application is presented in Figure 10. In particular, we consider \(N\) users who, every \(Z=100\)s (modeled by delay node _Users_), submit a map-reduce job to the system. There are two classes in the system, representing, respectively, the map and reduce stages of the application. All requests starts in the map phase, and are characterized by the corresponding class. The Map_fork node splits the job into M_Map tasks, which are then rejoined in node Map_join. The class switch node CS_M->R changes the class of the job to the reduce stage, which is immediately split into M_Red reduce tasks in node Reduce_Fork. The job is finished when all the reduce tasks terminate: this is modeled by the join operation performed by node Reduce_join, immediately followed by class switch node CS_R->M that restores requests to the starting map class. All tasks are served by the \(K\) server queueing node VMs modeling the cloud environment running the application. The service time of the all the VMs is assumed to be exponential, with a different duration D_Map and D_Red, respectively, for the map and reduce stages. In the next experiments, we assume M_Map = 64, M_Red = 32, D_Map = 1s, and D_Red = 2s.
## 1 Introduction
Presenting a satisfactory and efficient training algorithm has always been a challenging subject. The training phase may consist of several epochs. A popular approach used in the training phase is back-propagation learning; however, researchers have pointed out that some commonly used learning algorithms have disadvantages [10, 11]. Several heuristic algorithms, including genetic algorithm (GA) [12], particle swarm optimization (PSO) [13] and ant colony optimization (ACO) [14] have been proposed for the purpose of training neural networks. Recently, two meta-heuristic algorithms inspired by the lifestyle of cuckoo birds were developed for solving optimization problems. Through some benchmarking studies, these two algorithms have been proven to be powerful meta-heuristic ones. Cuckoo Search (CS), proposed by [PERSON] and [PERSON] [15, 16], was inspired by the particular egg laying and breeding characteristics of the cuckoo bird. Since then, CS has been used in solving various problems, and is considered to outperform other algorithms such as PSO and GA [16, 17, 18, 19]. The other algorithm is the Cuckoo Optimization Algorithm (COA), developed by [PERSON] [20]. The comparison of the COA with standard versions of PSO and GA in some problems showed that the COA has superiority in fast convergence and global optimal achievement [20, 21]. The purpose of this study is to use CS and COA for training neural networks in predicting students' academic performance. Taking into account the available literature, there is still room for improvement of ANN and the prediction model. This leads to the new approach proposed in this work for accomplishing a successful prediction of a student academic performance. The merit of the CS and COA algorithm and the success of ANN in the prediction of student academic performance have encouraged us to combine ANN and these algorithms. In this study, we propose an approach based on the multilayer feed-forward network improved by the CS and COA algorithms for predicting academic performance, which makes use of the optimizing ability of the two algorithms. We also compare the prediction performances of neural networks trained by these two algorithms. In general, the scientific contribution provided by the current research is the new approach applied herein. To the best of our knowledge, this combination of the two artificial intelligence techniques is applied for the first time in this research. The paper is organized into seven sections. After the introduction in Section 1, the CS algorithm is presented in Section 2. Section 3 describes the COA algorithm. Section 4 is dedicated to describing the process of neural network training. The research design is provided in Section 5 and the results are in Section 6. Finally, Section 7 presents the conclusion.
# Computational Investigations and Control of Shock Interference +
## 1 Introduction
### Technical Approach
#### 1.2.2 Turbulence Closure Models  
As mentioned earlier in this paper, there are two computational approaches to solve the turbulent flow field, called LES and RANS. These methods provide different fidelity in resolving the energetics of turbulence, with RANS requiring modeling of energy transport and dissipation at all turbulence scales, whereas LES requires modeling of turbulent dissipation occurring near Kolmogorov scales. However, it has become increasingly known that RANS turbulence closure models demonstrate significant variability in terms of predictions of shock physics.  
While many turbulence closure models are available for RANS, a two-equation k-omega model called Wilcox 1998 [23] was utilized for turbulence closure in this work. In this model, turbulence closure is achieved via the formulation of two equations for turbulent kinetic energy, k, and specific dissipation rate, omega. This model has been found to predict the behavior of attached compressible boundary layers subjected to adverse pressure gradients more accurately than traditional turbulence kinetic energy dissipation rate models.  
For LES, the Smagorinsky model [24] has been utilized to close the anisotropic residual-stress tensor, which is represented as a product of fluid velocity gradient and a modeling term known as eddy viscosity (sometimes referred to as turbulent viscosity). Eddy viscosity is calculated by assuming that it is proportional to the product of the square of the mesh size and the magnitude of the velocity gradient. The proportionality constant is typically between 0.1 and 0.2, depending on the flow evolution. This is the simplest model and has been proven to perform reasonably well. The Smagorinsky model assumes that there is equilibrium between the energy production and dissipation at the small scales. There are a few shortcomings with the Smagorinsky model, one of which is that there is no provision to account for energy transfer from the smallest scales to the larger scales. However, there is no evidence that such a transfer is occurring in the problem addressed by the current work. Another shortcoming is that there is no clear guidance on the value of the proportionality constant utilized in eddy viscosity calculations, which casts a degree of modeling uncertainty. Despite this issue, the Smagorinsky model is still a simple and reliable model for LES, and therefore, it has been utilized in this work.
###### Abstract  
Frost mulberry leaves possess significant medicinal and nutritional values and feature extensive resource availability and convenient acquisition. The study investigated the physicochemical structure and functional properties of frost mulberry leaf superfine powder (FMLSP) and the effects of FMLSP on the hypoglycemic activity and gut microbiota of type 2 diabetes mellitus (T2 DM) mice. The results indicated that the total flavonoid content of FMLSP reached 91.30 mg/g, with significant inhibitory effects on both \(\alpha\)-glucosidase and \(\alpha\)-amylase activities. Animal experimental data showed that FMLSP could significantly reduce insulin content, improve insulin resistance, and protect liver and pancreatic tissues in T2 DM mice. Meanwhile, FMLSP showed significant effects on lipid metabolism, especially the low-density lipoprotein cholesterol (LDL-C) content in T2 DM mice was significantly reduced by 76.22%. In addition, FMLSP has excellent antioxidant effects, which greatly alleviated the oxidative stress phenomenon in T2 DM mice, especially the malondialdehyde (MDA) content was significantly reduced by 72.17%. FMLSP also restored the diversity and structure of the gut microbiota, significantly increasing the abundance of beneficial bacteria such as _Akkermansia_, _Lachospiraceae_NKA136_group_, _Aloprecotella_, and _Lactobacillus_ in T2 DM mice and significantly decreasing the abundance of abundance of harmful bacteria such as _Rikenellaceae_RC9_gut_group_, _Enterorthabdus_. These results indicate that FMLSP may serve as a potential dietary intervention for the prevention and treatment of T2 DM.  
Keywords:frosted mulberry leaf; superfine grinding; hypoglycemic; type 2 diabetes mice; gut microbiota +
Footnote †: journal: Applied Sciences
# Electronic Nose Analysis to Detect Milk Obtained from Pasture-Raised Goats
## 3 Results and Discussion  
### Electronic Nose Analysis  
Figure 1 shows the LDA plot obtained from the data vectors extrapolated from E-nose measurements in the time interval 55-59 s, when the MOS sensors were stable [28].  
Electronic nose provided good discrimination of the two milk labels. P milk exhibited greater variability than S milk. Table 3 shows the confusion matrix with classification of milk samples obtained by LDA analysis.  
Figure 1: LDA plot of the resulting 10-dimensional patterns (data vectors) obtained from electronic nose analysis of 45 Pasture (green) and 45 Stall (red) individual milk samples. Data processed with Winnmuster v.1.6 software (Airsense Analytics GmbH, Schwerin, Germany).  
The correct classification of milk samples according to diet and milking period was 80%. On the contrary, considering only the animal diet (pasture vs. stall diet), the correct classification of the samples increased to 88%. By E-nose equipped with 10 MOS sensors, [PERSON] et al. [27] discriminated the cow's milk of the summer period according to the type of grazing vegetation. The two groups of cows were fed on two different Alpine vegetation (Trifolium albumin and Festuca nigrescens as dominant species of the two groups) and received only a supplement of minerals with no integration feeding. The authors reported over 90% correct classification through statistical analysis of the E-nose data, similar to our findings when the milking period effect was not taken into account.  
From the ninety E-nose acquisitions, P milk was incorrectly classified as S milk for a total of seven times, while S only three times. Generally, S milk samples obtained a better classification during the investigated periods attributable to a more standard and less variable diet compared to P milk from grazing goats. The more variable responses obtained for P milk could be linked to volatile organic compounds, the presence of which may vary drastically both qualitatively and quantitatively as the grazing period varies and in relation to the type of fresh forage [3]. In fact, [PERSON] et al. [33] reported both sensory (odour) and chemical differences (VOCs) in goat's milk according to grazing season (March to July). For example, milk of the summer season was characterised by terpene compounds while the ketones increased from winter to spring. These differences could be those that determine a different response of the E-nose sensors. The lactation stage also influences the variation of the composition of milk and, hence, the response of E-nose sensors. [PERSON] et al. [34] reported that the lactation stage (days in milk) is one of the most important individual factors contributing to the variation in the flavour profile of milk.
# Pasteurization of Foods with Ultrasound: The Present and the Future
## 1 Introduction  
Ultrasound is a novel technology that has been under active research in the last two decades. Although the first research was focused on microbial inactivation using only model systems, the positive results encouraged scientists to study the effect of ultrasound in real and complex food systems. The main effect of ultrasound in microbial cells is because of cavitation. This physical phenomenon occurs when sound waves enter contact with a liquid medium [1, 2]. The passing of sound waves through the liquid generates tiny bubbles or cavities. During the formation of bubbles, there are micro implosions and explosions inside the medium. These explosions increase temperature and pressure, damaging the cellular membrane [3, 4]. However, the intelligent combination of processing conditions can use these physical effects to promote the microbial growth of some beneficial microorganisms without damaging cellular structures. This specific feature of ultrasound can accelerate fermentation processes [3]. Ultrasound has also been studied for enzyme inactivation with promising results in stabilizing pasteurized products. Cavitation can modify the protein structure and affect enzyme functionality [5]. The combination of ultrasound and mild thermal treatment often achieves the required 5-log reduction as pasteurization standard with minimal effects on the quality product. Furthermore, the study of the sonicated products has shown the cavitation effect in breaking the cells of vegetable tissue and the release of some bioactive compounds [6, 7, 8], making these nutrients more available for quantification and absorption in the digestive system. The positive effects of ultrasound for pasteurizing liquid foods have been widely reported and documented, not only in terms ofmicrobial inactivation but also in the generation of specific characteristics in the sonicated product [9].  
This review presents an update on the current research on low-frequency or power ultrasound focused on pasteurized products. The different microorganisms and enzymes studied under this novel technology are discussed. Furthermore, a comprehensive list of bioactive compounds in sonicated products is presented and discussed. The overall quality of several sonicated products is included. At the end of the manuscript, some of the current research needs of ultrasound are discussed to evaluate its potential future as a novel technology in food processing.