esa-sceva/satcom-chunk-collection huggingface dataset:
# Personalized Tour Itinerary Recommendation Algorithm Based on Tourist Comprehensive Satisfaction
## 4 Method  
This section describes our proposed algorithm. Essentially, the tour itinerary recommendation problem can be boiled down to solving two key issues: recommending which scenic spots to visit (determining the node combination of the tour itinerary) and how to plan the itinerary (including routes and timing). These two factors collectively influence the quality of the tour itinerary recommendation results. In the objective function, node combination directly determines \(ISAS\) and \(R_{div}\) and sets upper limits for \(TUS\) and \(FS\), while itinerary planning directly determines \(TUS\) and \(FS\). Therefore, the main idea of the recommendation algorithm is to explore different node combinations and optimize the objective function of the itinerary composed of these node combinations through itinerary planning, aiming to return the optimal result among all attempts. During each iteration, the selection of candidate attractions is influenced by their performance in participating in the itinerary. We implemented this main process based on a variant of the MCTS algorithm.  
As a crucial step in the recommendation algorithm, tour itinerary planning aims to find the optimal itinerary for the given set of nodes by maximizing \(TUS*FS\). We define \(TUS*FS\) as the tour itinerary planning satisfaction score (\(TPSS\)). The tour itinerary planning problem also involves two key issues: route planning (represented as the order of node visits in the algorithm) and time planning. Both factors collectively affect the quality of the itinerary planning results. The order of node visits determines the upper limit of the \(TPSS\), while the time scheduling on this node visit order determines the specific \(TPSS\). Therefore, our main approach to itinerary planning is to heuristically explore different node visit sequences and optimize the TPSS under the current sequence through timing planning, aiming to return the optimal itinerary arrangement among all attempts. Itinerary planning is modeled based on the Traveling Salesman Problem, which was implemented using the LKH algorithm.  
As a key step in itinerary planning, timing planning comprehensively considers the time costs of food, lodging, transportation, and sightseeing for the determined node combination and visit sequence. It primarily maximizes the \(TPSS\) through the selection of node time windows and the arrangement of food and lodging. We implemented this using a local optimal algorithm. Figure 3 illustrates the hierarchical relationship of the recommendation algorithm, itinerary planning algorithm, and timing planning algorithm, as well as the main processes of the algorithms. Next, we will provide detailed descriptions of the timing planning, itinerary planning modules, and overall algorithm implementation.  
Figure 3: Framework and workflow of the tour itinerary recommendation algorithm.
## 8 Discussion: Flow Classifier in SDN  
With the learned multi-layer LSTM model, it is necessary to suggest ways to achieve the essential purpose of this paper. In this section, therefore, we describe how to classify traffic flow using the learned model in the flow classifier of the proposed SDN-based network architecture (see Figure 1).  
The OpenFlow protocol should be extended to feed the payload information to the traffic classification model. When a packet arrives at an Open vSwitch, the switch looks up its flow table and executes an action such as dropping or forwarding to the packet. If there is no action rule in the flow table, the packet-in message is sent to the controller. The packet-in message is expanded such that it contains the beginning of the packet's payload. That is, the pre-defined 36, 64, 256, or 1024 pixels from the front of the packet payload are contained in the packet-in message (one pixel indicates the four bits of the payload data). To use our LSTM model, consecutive \(N\) packet payloads per flow are needed (\(N\) = 30, 60, or 100). Therefore, the controller continually sends to the Open vSwitch the packet-out message containing 'null action' rule until receiving \(N\) packet-in messages for the packet arrivals in the same flow. The 'null action' indicates that the Open vSwitch does not create any action rule for the packet. When the controller receives the \(N\)-th packet-in message including packet information generated in the same flow, the sequence of \(N\) packet's payload information is fed into the learned multi-layer LSTM model and the flow classification is performed. At this point, the controller sends to the Open vSwitch the packet-out message containing rules, according to the scheduling or QoS policy based on the classified flow.
# Multiscale YOLOv5-AFAM-Based Infrared Dim-Small-Target Detection
## 1 Introduction
Therefore, we propose a new dataset that includes more small targets and covers a wider range of background conditions. When the target detection algorithm based on deep learning is applied to infrared dim-small-target detection, appropriate adjustments and improvements should be made. For this demand, this paper proposed a real-time detection algorithm for infrared dim-small-target detection based on YOLOv5, which can improve the average detection accuracy while maintaining the detection speed and model parameters. The main contributions in this paper are constructed as follows: Section 2 provides an introduction to the original model of YOLOv5, as well as a detailed description of the multiscale YOLOv5-AFAM network constructed in this paper to address difficulties encountered in the dim and small-target detection process. In Section 3, we propose a new dataset that includes more small targets and covers a wider range of background conditions. To demonstrate the effectiveness of our model, the impact of each improvement on the detection effect is verified from multiple perspectives by conducting rigorous ablation experiments on the target detection dataset constructed in this paper for infrared dim vehicles, combined with evaluation metrics. Section 4 concludes this study and provides outlooks. Therefore, the main contributions of this study lie in four aspects:  
* We propose a new dataset that overcomes the limitations of previous infrared datasets by covering a wider range of background conditions. Furthermore, this dataset includes more small targets, which are crucial for accurately training models to detect small targets. * To address the problems of the low signal-to-noise ratio of weak infrared targets and susceptibility to background interference, this study proposes the Adaptive Fusion Attention Module (AFAM), which makes the neural network feature extraction focus on the target region, as can be seen from the results in Section 2, compared with the original YOLOv5 model, [EMAIL_ADDRESS], [EMAIL_ADDRESS]:0.95, and recall are improved. * To solve the problems of the low contrast of weak targets and the large-scale variance of targets, this study optimizes the multiscale fusion structure, which enables the network to adapt to the detection of small targets with diverse scales and accurately extract the features of each class of targets. * Given the low resolution and blurred contours of infrared images, this study improves the downsampling method by incorporating Maxpool in convolutional downsampling, which reduces the computational effort while preserving texture information.
## 2 Theoretical Background
\begin{split} n_{TE}(x)&=n_{b}+\Delta n_{TE}u(x)\\ n_{TM}(x)&=n_{b}+\Delta n_{TM}u(x)\\ n_{0}(x)&=n_{b}+\Delta n_{0}u(x)\end{split}  \tag{7}\]  
In the above equations, \(n_{b}\) means the refractive index of the glass (without ion exchange), \(\Delta n_{i}\) is the increase in the refractive index at the glass surface (for \(i=\text{TE}\), TM, 0), \(u(x)\) is a function that describes the normalized concentration of the admixture ions introduced into the glass [14]. These dependencies are shown in Figure 2.  
Figure 1: Stress directions in a planar waveguide. Figure 2: Refractive index profiles in a planar waveguide showing birefringence. The refractive index profile \(n_{0}(x)\), in the absence of stresses, can be expressed on the basis of (5) in two ways:  
\[ \begin{array}{l}n_{0}(x)=n_{TM}(x)-\frac{2C_{2}}{C_{2}-C_{1}}[n_{TM}(x)-n_{TE}( x)]\\ n_{0}(x)=n_{TE}(x)-\frac{C_{2}+C_{1}}{C_{2}-C_{1}}[n_{TM}(x)-n_{TE}(x)]\end{array}  \tag{8}\]  
Taking into account dependencies (7) in the Equation (6) we get:  
\[\sigma(x)=\frac{n_{TM}(0)-n_{TE}(0)}{C_{2}-C_{1}}\times u(x)=\frac{n_{TM}(0)-n_ {TE}(0)}{C_{2}-C_{1}}\times\frac{n_{0}(x)-n_{b}}{\Delta n_{0}}=\sigma(0)\times \frac{n_{0}(x)-n_{b}}{\Delta n_{0}} \tag{9}\]  
The obtained dependence (9) links the function of stresses \(\sigma(x)\) with the refractive index profile \(n_{0}(x)\). This compound plays an important role in modeling stresses. The idea of modeling the refractive index profiles in the real time of the ion exchange process [10,14] can be applied here in relation to \(\sigma(x)\) function, which is shown in Figure 3. The \(\sigma(x)\) function calculations included stress relaxation at the glass surface according to the equation [6,18]:  
\[\sigma(0,t)=\sigma(0,0)exp\Big{[}-(t/\tau)^{\beta}\Big{]} \tag{10}\]  
where: \(\sigma(0,0)\) is the surface compressive stress at the beginning of the diffusion process, \(\tau\) is the relaxation time, \(\beta\) is the stretching exponent. According to the scheme shown in Figure 3, the diffusion equation is integrated here in the time domain with the \(\Delta t\) time step [14,15]. Solving this equation is carried out in parallel with the  
Figure 3: The principle of monitoring stresses generated in glass in real time based on temperature measurements. implementation of the diffusion process. The temperature in the crucible is measured at specific moments of time \(t_{p}\), which are the total multiplicity of the time step \(\Delta t\). The diffusion coefficients occurring in this equation are calculated in moments of time \(t_{p}\) based on the knowledge of their temperature dependencies. The refractive index profile \(n_{0}(x,t_{p})\) without stresses is calculated here at discrete time points \(t_{p}\). On its basis and at the same time points, the function \(\sigma(x,t_{p})\) given by the following equation is calculated:  
\[\sigma\Big{(}x,t_{p}\Big{)}=\sigma(0,0)exp\left[-\left(t_{p}/\tau\right)^{ \beta}\right]\times\frac{n_{0}\Big{(}x,t_{p}\Big{)}-n_{b}}{\Delta t_{0}} \tag{11}\]
## 4 Experiment and Discussion
### Experimental Results
#### 4.4.2 Performance Validation with Noise  
In order to verify that the proposed method has adaptive anti-noise abilities in harsh production environments, white noise is added to the experiment to simulate the influence of noise on the prediction ability of the model. White noise is uniformly distributed noise and is defined as:  
\[\mathrm{p(z)=}\begin{cases}1/2&\quad\text{if }-1\leq\mathrm{z}\leq 1\\ 0&\quad\text{otherwise}\end{cases} \tag{19}\]  
z is between \(-1\) and \(1\).  
The signal-to-noise ratio (SNR) of the original signal and the noise is calculated by the following equation:  
\[\mathrm{SNR}=10\log_{10}\left[\frac{\mathrm{A_{signal}}}{\mathrm{A_{noise} }}\right]^{2}(\mathrm{dB}) \tag{20}\]  
A\({}_{\mathrm{signal}}\) is the signal amplitude, \(\mathrm{A_{noise}}\) is the noise amplitude, and the experiment design is 5 to 1, so the SNR is about 7 dB, indicating that the added white noise is a strong interference signal. Taking the \(X\)-axis vibration signal as an example, adding white noise results in Figure 18, showing that the original signal has been disturbed by noise, which slightly damages the original signal waveform.  
The confusion matrices of the results of adding white noise are shown in Figures 19 and 20, and the bar graph in Figure 21 shows the test accuracy of all comparison methods with added white noise. After ten experiments, the average of each method is calculated to create the bar graph. Figures 19 and 21 show that ADCNN had four errors when predicting a normal machining tool. This indicates that there is a certain degree of adaptive anti-noise ability towards white noise, which can suppress the interference of the noise and reduce the decline of the model prediction accuracy. Figures 20 and 21 show that the prediction accuracy of other comparative methods after adding noise had a larger decline than the method proposed in this paper, while CNN-LSTM had a smaller decline but lower accuracy. The results show that the proposed method is robust in a high-noise environment.  
Figure 17: Bar graph of test accuracy for all compared models.  
Figure 19: ADCNN prediction confusion matrix with noise added.  
Figure 18: \(X\)-axis vibration signal with added white noise: (**a**) original signal; (**b**) white noise; (**c**) original signal plus white noise signal.  
Figure 21: Bar graph of test accuracy for all models with added noise.  
Figure 20: Predicted confusion matrix of other comparison methods with added white noise: (a) 1 DCNN; (b) LSTM; (c) CNN-LSTM; (d) ConvLSTM.
# STFEformer: Spatial-Temporal Fusion Embedding Transformer for Traffic Flow Prediction
## 2 Materials and Methods
### Spatial-Temporal Attention Layer
#### 2.4.3 Spatial-Temporal Attention Fusion  
In order to diminish the computational complexity of the model, the heterogeneous spatial-temporal self-attention outcomes are combined after defining two types of attention modules. We concatenate the outcomes of these attention modules and then project them to yield the outputs, enabling the model to simultaneously incorporate information from spatial and temporal dimensions. Formally, the output can be obtained as follows:  
\[STA=(Z_{MaskSSA}\parallel Z_{TSA})W_{f} \tag{18}\]  
where \(W_{f}\in\mathbb{R}^{d\times d}\) represents a learnable projection matrix and \(d^{\prime}=d/(h_{tsa}+h_{tsa})\) is set in this paper.  
Additionally, we further apply a position-wise fully connected feed-forward network. This enables the model to delve deeper into the extraction of complex and abstract characteristics in traffic data by applying two linear transformations. Formally, this network can be defined as follows:  
\[X_{FNN}=GELU((STA)W_{1}+b_{1})W_{2}+b_{2} \tag{19}\]  
where \(X_{FNN}\in\mathbb{R}^{T\times N\times d}\) is the output of the network; \(W_{1}\), \(b_{1}\), \(W_{2}\), and \(b_{2}\) represent learnable parameters; and GeLU is the activation function. We also introduce residual connection and layer normalization here, as in the original transformer [38].
# Welding Image Data Augmentation Method Based on LRGAN Model
## 2 LRGAN Model
### The Discriminator Part of the LRGAN Model  
Considering the balance between network capacity and overfitting, a discriminator with too few layers (such as one to three layers) may lead to insufficient model capacity, making it difficult to capture complex image patterns. On the other hand, too many layers (such as nine or more) may enhance expressive power but also risk overfitting the training  
Figure 1: LRGAN model diagram.  
data. Therefore, in this paper, a five-layer spectral normalization neural network was designed as the discriminator using a least squares loss function. The five-layer architecture is widely used in many image generation tasks (such as DCGAN and SRGAN) and is an empirically validated choice for a good trade-off. Furthermore, the LRGAN generator in this study uses a nine-layer residual module. To match the feature learning capability during the adversarial process and align with the generator's layer count, the discriminator should not be too shallow, and five layers provide an appropriate level of complexity.
# A Novel Hybrid Method for Urban Green Space Segmentation from High-Resolution Remote Sensing Images
## 2 Materials and Methods
### Decoder with Decoder Block and Bilateral Feature Fusion Module  
During the decoding stage, many networks perform direct upsampling to match the original image size, which can result in information loss. Some networks address this by using a single convolutional layer for decoding, preserving important local features but lacking long-range feature connections. These networks are not as effective in recovering the edges of the UGS areas. Therefore, we built a novel decoder module designed to gradually recover essential information from deep features. Figure 3 shows the structure of our proposed decoder module.  
In the upsampling process, high-level semantic and spatial information often results in a rough final segmentation boundary, especially when dealing with arbitrary and irregular UGS sizes and shapes, making UGS boundary segmentation challenging. Some existing methods segment the boundaries very coarsely and lack detail. As shown in Figure 3a, this paper uses two layers of \(3\times 3\) convolution, normalization, and activation functions to form a DE module to enhance the ability to obtain information when extracting features. As shown in Figure 3b, this article uses DWConv convolution [27] to  
Figure 3: (**a**) A new decoder block; (**b**) a bilateral feature fusion module.  
Figure 2: Resnet50 residual structure.  
can learn richer detailed features while reducing the amount of calculation parameters. During the training phase, multiple DWConv convolution combinations can enhance the representation of UGS information features. Maxpool is used to extract feature information. We used three maxpool layers of 3 \(\times\) 3, 5 \(\times\) 5, 7 \(\times\) 7, respectively, to achieve UGS feature information extraction in parallel. To a certain extent, it can extract texture and edge information in the image, reduce interference from non-green space areas in the HRS image, and more effectively identify scattered multiscale objects, thereby reducing the risk of missed segmentation and false segmentation. At the same time, we keep an input channel in the side to preserve the integrity of the input information. The new decoder module has the potential to promote MAFANet model segmentation of UGS.
## 3 Tracking Framework for Lobe Fissure Based on Modified ACO Algorithm
### Optimal Path Detection
These metric functions are defined:  
\[precision=\frac{TLF}{TLF+FRLF} \tag{12}\]  
\[recall=\frac{TLF}{TLF+FLF} \tag{13}\]  
\[F\_measure=\frac{2\times(precision\times recall)}{precision+recall} \tag{14}\]  
The relationship of three parameters \(FLF\), \(TLF\), and \(FRLF\) is shown in Figure 14. These are defined as:  
* The value of false lobe fissure (\(FLF\)): the TR(\(LF_{ACO}\)) is not overlapped on the trustful region of TR(\(LF_{P}\));
* The value of true lobe fissure (\(TLF\)): the TR(\(LF_{ACO}\)) is overlapped on the trustful region of TR(\(LF_{P}\));
* The value of false real lobe fissure (\(FRLF\)): the TR(\(LF_{P}\)) is not overlapped on the trustful region of TR(\(LF_{ACO}\));  
According to the analyzed results (as listed in Tables 2 and 3), the mean values of precision, recall and F-measure that can be achieved are 78.26%, 83.89% and 80.9% on all left-lung cases, and 79.68%, 86.34% and 82.84% on all right-lung cases, respectively. We can observe that the value of recall is almost always larger than precision, because the cyclic paths would be happened when the ants move in some unclear lung CT cases. We also obtain that the metric values in right lung are larger than in the left lung. This is because the heartbeat may cause the scanning error of lung CT. However, the quantitative assessment shows that the proposed ACO method achieved the average F-measures of 80.9% and 82.84% for left and right lung cases, respectively. The higher F-measure means that the proposed ACO method for lobe fissur  
Figure 14: The relationship of parameters: false lobe fissure (\(FLF\)), true lobe fissure (\(TLF\)), and false real lobe fissure (\(FRLF\)).
## 7 3D Model Analysis for Structural Monitoring of the Aqueduct  
An appropriate 3D FE model that can be highly representative of the static and dynamic responses of the aqueduct is very important to its long-term structural monitoring. The model can give reasonable predictions for the deformation and stress states of the structure under different load conditions, some of which may not be frequently encountered during operation, thus supplementing the structural monitoring database. Through the comparative study of the field-monitored data and model analysis results, it is possible to identify potential hazards and provide a rational evaluation for the structural safety of the aqueduct.  
A 3D structural model of the entire aqueduct was created by SAP2000 V23.3.0. The middle part of the model is shown in Figure 23, which includes the target monitoring span--span #8. From top to bottom, the model comprises the reinforced concrete U-shaped trough segments (including 3 cm-wide contraction joints), the composite tied-arch (including post-tensioned tendons inside tension rods), transverse bent frames erected from the tied-arch, moment frame supports holding up the composite tied-arch, and pile foundations. The rubber bearings of the composite tied-arch, fixed basin-type at upstream ends, and sliding basin-type at downstream ends are incorporated in the model using link elements with shear and rotational stiffness properly defined. The non-conventional bearings made of two layers of thin asphalatic felt for prefabricated U-shaped concrete trough segments are also  
Figure 21: Correlation between the temperature-effect-excluded \(\epsilon_{dav}\) of the left rod and \(W_{dav}\).  
Figure 22: Correlation between the temperature-effect-excluded \(\epsilon_{dav}\) of the right rod and \(W_{dav}\).  
simulated. The structural model was verified by a comparative analysis of in situ dynamic tests and modal analysis prior to this study [19], which also provided an approximation of elastic moduli for the concrete of main load-bearing members, including the composite tie arch, transverse bent frames, and moment frame supports.  
This 3D FE model is used to obtain and compare the analytical data with their monitored counterparts from the real structure under different load conditions. The vertical and longitudinal deformations of the composite tie arch are calculated using displacements along vertical and horizontal directions at specified nodes, and the strains of the main arch ring and tension rod are evaluated by the compressive or tensile stresses of corresponding frame elements in the model as shown in Figure 24.
## 3 Non-Thermal Technologies in Food Processing
### UV Radiation
#### 3.2.3 Application in Cereal Product  
UV light irradiation has been tested on cake batters by [PERSON] et al. for different times (0, 1, 2, and 4 h) and subsequently backed applying typical methods or microwaves or a combination of them. The parameters evaluated are chemical composition, water activity, specific volume, crumb and crust color, textural parameters such as hardness, cohesiveness, springiness, and resilience, and sensory characteristics. An increase in UV radiation and time of irradiation leads to an enhancement in browning reactions on the cake. In addition, a rise in specific volume has been observed after 1 or 2 h of UV treatment. Consumers have noticed an unappealing taste and fragrance as the irradiation time increases; on the other hand, the browning reaction on the cake surface, backed only with conventional methods, has made cases prepared with UV-light more preferable [128].  
UV radiation effects on food products containing gluten are documented by [PERSON] et al., who focused their research on wheat (_Tritcium aestivum_) flour and its protein modification after UV-C radiation. They observed a decrease in gluten content as well as a rise in total volatile basic nitrogen content and photo-induced thiol-disulfide bridge exchange [129].