esa-sceva/satcom-chunk-collection huggingface dataset:
# Key Quality Indicators Prediction for Web Browsing with Embedded Filter Feature Selection
## 1 Introduction  
With the large-scale commercialization of long-term evolution (LTE) networks and the widespread popularity of smartphone, the over-the-top (OTT) services, which are dominated by Internet companies, have developed rapidly and are becoming the major services on the phone. On the one hand, traditional services including voice, SMS, and other value-added services, have been largely replaced. On the other hand, new services have stimulated the convergence of mobile networks and the Internet. The quality of experience (QoE) of OTT services are no longer determined solely by the quality of the mobile network itself.  
For traditional operation and maintenance of mobile networks, engineers have always taken measures after receiving customer complaints. It is difficult to cope with the rapid and ever-changing network and service environments. Instead, in certain circumstance it is better to identify or even predict traffic demands, service quality and potential service degradations [1; 2; 3]. If the service qualitycan be predicted precisely, it will enable engineers to take appropriate measures before the service deterioration leads to complaints, or to recommend the best service to customers. QoE can then be guaranteed.  
With the popularity of smartphones and the application of a large number of sensors, smartphones have gradually replaced drive test equipment, and have become important points to collect users' service perceptions [4]. The data is highly valuable not only for network operators themselves, but also for OTT service providers. Based on massive service perception data and appropriate machine learning methods, one can effectively improve the intelligence of the network operations. Gartner defined this new paradigm as Artificial Intelligence for IT Operations (AIOps) [5].  
Taking the OTT web browsing as an example, here we try to employ the improved feature selection and multi-label learning algorithm to predict its key quality indicators (KQI) for the users in the mobile network, with the perceptional data crowd-sensed at the terminal end.  
The rest of the paper is organized as follows: In Section 2 the related works are introduced, including mobile crowdsensing, KQI of OTT web browsing, multi-label feature selection (FS) and multi-label k-nearest neighbor (ML-kNN). In Section 3 we propose an ML-kNN based algorithm for the prediction of KQI with a modified multi-label feature selection. In Section 4, the proposed method is validated through experiments and compared with the traditional methods. Finally, the work of this paper is concluded in Section 5.
# Machine Reading Comprehension Model Based on Fusion of Mixed Attention
## 2 Problem Formulation and Research Motivation  
This paper tackles critical challenges in existing machine reading comprehension (MRC) models by proposing a solution-oriented approach. We initially identify the following issues:Insufficient contextual semantic fusion: current MRC models encounter limitations in seamlessly integrating semantic information from questions with the context, affecting their capacity to deeply comprehend and interpret the textual context.  
Inadequate utilization of overall information: a prominent challenge in question-answering tasks is the comprehensive utilization of all pertinent information, including not only the direct textual content but also the broader context.  
Limitations in long-text reasoning: existing models exhibit deficiencies in processing and comprehending lengthy texts, particularly when engaging in reasoning through intricate narratives or detailed explanations.  
Ambiguity in answer extraction: during the answer extraction process, models may confront ambiguity among multiple potential answers, necessitating the precise judgment and selection of the most suitable answer based on the context.  
To address these challenges, this study proposes a novel machine reading comprehension model founded on a hybrid attention mechanism. This model combines the pretrained BERT model with a hybrid attention mechanism, with the goal of mitigating issues related to insufficient contextual semantic fusion and inadequate overall information utilization.
###### Abstract  
We analyze the feasibility of providing Wireless Sensor Network-data-based services in an Internet of Things scenario from an economical point of view. The scenario has two competing service providers with their own private sensor networks, a network operator and final users. The scenario is analyzed as two games using game theory. In the first game, sensors decide to subscribe or not to the network operator to upload the collected sensing-data, based on a utility function related to the mean service time and the price charged by the operator. In the second game, users decide to subscribe or not to the sensor-data-based service of the service providers based on a Logit discrete choice model related to the quality of the data collected and the subscription price. The sinks and users subscription stages are analyzed using population games and discrete choice models, while network operator and service providers pricing stages are analyzed using optimization and Nash equilibrium concepts respectively. The model is shown feasible from an economic point of view for all the actors if there are enough interested final users and opens the possibility of developing more efficient models with different types of services.  
Keywords:internet of things service provider; network economics; pricing; game theory; population games; discrete choice model; Nash equilibrium; users subscription; wireless sensor networks +
Footnote †: journal: Sensors
# Collaborative Filtering Based on a Variational Gaussian Mixture Model
## 4 Results
### Discussion  
In order to verify the accuracy of the GVAE-CF algorithm, this paper compared the existing methods using the MoviesLens and Netflix datasets. The results of the comparison results are shown in Tables 1-3.  
It can be seen in Table 1 that, in terms of the recommended quality, the GVAE-CF method in this paper was significantly better than the baseline models using the MovieLens-20M dataset, except for RecVAE [16]. Although the recall accuracy of the RecVAE method was slightly higher than that of GVAE-CF, it used noise as input and used noise reduction. The encoder brought more robust embedding learning to be used in combination with the GVAE-CF algorithm. Moreover, the performance of this method on NDCG@100 was better, which shows that our method can determine the relationships between categories and items, because it can push products belonging to the target category to the front of the ranking.  
It can be seen in Tables 2 and 3 that the GVAE-CF method performed better than the other baseline models. For the baseline of WMF matrix factorization recommendation algorithm, recommendation algorithms based on generative models can effectively improve its performance. Compared to previous recommendation algorithms based on the autoencoder model, GVAE-CF was further improved.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Recall@20** & **Recall@50** & **NDCG@100** \\ \hline WMF [12] & 0.314 & 0.466 & 0.341 \\ SLIM [37] & 0.370 & 0.495 & 0.401 \\ CDAE [38] & 0.391 & 0.523 & 0.418 \\ Mult-DAE [14] & 0.387 & 0.524 & 0.419 \\ Mult-VAE [14] & 0.395 & 0.537 & 0.426 \\ RecVAE [16] & **0.414** & **0.521** & 0.420 \\ GVAE-CF & 0.408 \(\pm\) 0.002 & 0.520 \(\pm\) 0.002 & **0.431 \(\pm\) 0.002** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Evaluation scores for GVAE-CF and the baseline models using the MovieLens-20M dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Recall@20** & **Recall@50** & **NDCG@100** \\ \hline WMF & 0.314 & 0.466 & 0.341 \\ SLIM & 0.370 & 0.495 & 0.401 \\ CDAE & 0.391 & 0.523 & 0.418 \\ Mult-DAE & 0.387 & 0.524 & 0.419 \\ Mult-VAE & 0.395 & 0.537 & 0.426 \\ RecVAE & 0.414 & 0.521 & 0.420 \\ GVAE-CF & **0.416 \(\pm\) 0.001** & **0.524 \(\pm\) 0.001** & **0.431 \(\pm\) 0.001** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation scores for GVAE-CF and baseline models on Netflix.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Recall@20** & **Recall@50** & **NDCG@100** \\ \hline WMF & 0.252 & 0.346 & 0.259 \\ SLIM & 0.261 & 0.372 & 0.269 \\ CDAE & 0.264 & 0.396 & 0.275 \\ Mult-DAE & 0.277 & 0.401 & 0.351 \\ Mult-VAE & 0.279 & 0.427 & 0.362 \\ RecVAE & 0.296 & 0.421 & 0.372 \\ GVAE-CF & **0.301 \(\pm\) 0.002** & **0.429 \(\pm\) 0.002** & **0.381 \(\pm\) 0.002** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Evaluation scores for GVAE-CF and baseline models on LASTFM.
## 2 Related Works
### ENO Based on WPT  
ENO based on WPT has been investigated in some recent work. For example, the feasibility of an energy-neutral Internet of Things (IoT) network was studied in [36] by joint utilization of EH and WPT technologies, and design guidelines for energy-neutral wireless powered networks were presented. In [37], duty cycle operation for a self-powering dual mode SWIPT system in which a sensor node adaptively controls single-tone or multi-tone communication mode was introduced and an adaptive mode switching problem was solved to maximize the achievable rate under ENO to realize a battery-free IoT network. Meanwhile, an energy management scheme for a WPSN was proposed in [38], which maximizes the RF energy transfer efficiency while guaranteeing ENO, and the proposed energy management scheme was implemented in an actual testbed to verify its operation. In [39], an adaptive duty cycle control algorithm was proposed to prevent the energy storage of a sensor node from being depleted and its ENO was validated in a multi-antenna WPSN testbed that dynamically steered a microwave beam towards a sensor node. In addition, the energy neutral Internet of drones was conceptualized in [40] to overcome the energy limitations for continuous operation of drones and to enhance the connectivity between drones. Communication and networking architectures and protocols for drones energized by WPT were presented. Although ENO was considered in SWIPT-enabled networks or WPCNs in these works, to the best of our knowledge, ENO considering both SWIPT and WPCNs has not been investigated yet.
## 4 Results and Discussion
### Results Using TF-IDF Features on the Original Dataset  
For this set of experiments, machine learning models are trained using TF-IDF features from the original dataset. TF-IDF gives weighted features for the learning of models which can be useful for better training of models. The results of machine learning models with TF-IDF features on original data are shown in Table 12. Results show that SVM and SGD outperform all other models with a 0.94 accuracy score each followed by LR with a 0.93 accuracy score. Linear models again perform well on the imbalanced dataset with TF-IDF features, similar to BoW features. Still, KNN, GNB, and AdaBoost are the worst performers on imbalanced data using TF-IDF features, and only 1% improvement in AdaBoost results is observed with TF-IDF on the imbalanced dataset.
# A Hybrid Deep Learning Approach for Cotton Plant Disease Detection Using BERT-ResNet-PSO
## 3 The Proposed Hybrid Deep Learning Approach
### BERT-Based Encoder for Image Segmentation Dataset
#### 3.2.4 Hybridization with PSO for Optimization  
The parameters of the ResNet model have been optimized using Particle Swarm Optimization (PSO) and have improved classification performance. Inspired by the behavior of bird flocking or fish schooling, PSO is a population-based model. The initial position of the ith particle can be defined as:  
\[P_{i}(0)=P_{\text{min}}+r_{i}\times(P_{\text{max}}-P_{\text{min}}) \tag{9}\]  
where \(P_{i}(0)\) is the initial position, \(P_{\text{min}}\) and \(P_{\text{max}}\) are the bounds of the parameter space, and \(r_{i}\) is a random number between 0 and 1.  
The velocity of a particle is updated using its previous velocity, the best-known position for the particle, and the best-known position for the entire swarm. The velocity update is defined as:  
\[v_{i}(t+1)=\omega v_{i}(t)+c_{1}r_{1}(p_{i}(t)-x_{i}(t))+c_{2}r_{2}(g(t)-x_{i}( t)) \tag{10}\]  
where \(v_{i}(t+1)\) represents the updated velocity, \(\omega\) is the inertia weight, \(c_{1}\) and \(c_{2}\) are acceleration coefficients, \(r_{1}\) and \(r_{2}\) are random factors, \(p_{i}(t)\) is the particle's best position, and \(g(t)\) is the global optimal position. The position update is defined as:  
\[x_{i}(t+1)=x_{i}(t)+v_{i}(t+1) \tag{11}\]  
The fitness of each particle is defined as the inverse classification error, where a trade-off between precision, recall, and other relevant metrics is included. The fitness function is expressed as:  
\[\text{Fitness}(P_{i})=\frac{1}{\text{Error}(P_{i})} \tag{12}\]The PSO algorithm iterates until a stopping criterion is met, such as a predefined number of iterations or when improvements fall below a threshold. This convergence criterion can be written as:  
\[\Delta\text{Fitness}<\epsilon \tag{13}\]  
where \(\epsilon\) is a small positive number representing the convergence threshold.
# Memory-Based Learning and Fusion Attention for Few-Shot Food Image Generation Method
## 5 Experimental Results and Analysis  
### Comparison with the State-of-the-Art  
In this section, to validate the efficiency of MLA-Diff, numerous experiments on food image generation tasks are conducted on the Mini-food dataset compared with the state-of-the-art methods. As shown in Table 1, the MLA-Diff shows superior performance compared to the state-of-the-art methods. Specifically, the FID and LPIPS metrics indicate an average decrease of 6.448 and 0.842, respectively, while IS shows an average increase of 6.373 for the MLA-Diff. Although there is a slight decrease of 0.007 in IS compared to the XMC_GAN, the MLA-Diff still significantly outperforms other state-of-the-art methods. Furthermore, XMC_GAN also has better performance than other mainstream methods due to the better FID, LPIPS, and IS scores of 6.448, 0.842, and 6.373, respectively. Overall, our MLA-Diff succeeds in exhibiting richer texture and finer details, more closely consistent with human visual perception.  
To visualize the performance of MLA-Diff in food image generation, Figure 6 displays some generated images from varying degrees of food ingredients. From Figure 6, compared to other methods, the MLA-Diff indistinctly succeeds in distinctly describing each food ingredient in the generated images without blurry boundaries. Specifically, in the first set of Figure 6, the MLA-Diff not only reproduces the most exact shape of the food similar to the original but also a more comprehensive array of food ingredients. In the third set of Figure 6, the generated food images closely resemble the original in terms of shape, color, and texture. Consequently, the experimental generates images with semantic consistency and sufficient visual reality, indicating that the MLA-Diff exhibits superior performance in food image generation compared to existing methods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network** & **FID** & **LPIPS** & **IS** \\ \hline ChefGAN [13] & 11.537 & 0.837 & 1.955 \\ CookGAN [10] & 10.854 & 0.904 & 2.274 \\ MoCA [27] & 9.650 & 0.747 & 5.480 \\ DreamArtist [25] & 8.677 & 0.735 & 2.882 \\ PGTI [26] & 7.092 & 0.746 & 4.530 \\ XMC\_GAN [32] & 6.448 & 0.842 & **6.373** \\
**MLA-Diff** & **4.285** & **0.733** & 6.366 \\ \hline \hline \end{tabular} Bold text in the table represents the optimal results.  
\end{table}
Table 1: Comparison of food image generation performance.
## 2 Experiment Design
### Equipment and Measurement  
Figure 6 shows a drawing and photograph of the equipment and measurement setup. Two vertical jacks and one horizontal jack are used to impose the vertical and horizontal loads, respectively, while force sensors are used to measure the loading at the ends of the jacks. The sensor for the horizontal displacement is set at the end of the upper foundation beam. Dial indicators are installed at the end of the lower foundation beam to detect slippage in the horizontal direction.  
Figure 5: The making of the specimens: (**a**) stem making; (**b**) wall making; (**c**) completed specimen and its maintenance.  
Figure 6: Equipment and measurement: (**a**) loading devices and indicator arrangement; (**b**) test scene; (�⃝- reaction wall;⃝- loading frame;⃝-50t jack;⃝-50t sensor;⃝- loading beam;⃝- displacement sensor for loading beam;⃝- wall;⃝- marble-graphite isolator;⃝- anchor block;⃝- upper foundation beam;⃝- - lower foundation beam;⃝- displacement sensor for upper foundation beam;⃝- - dial indicator).  
Before the horizontal loading is initiated, the two vertical jacks each impose a 50-kN (or 100-kN total) force, which is held constant throughout the experiment. The vertical loading of 100 kN is chosen as it is believed to be the general case for such a section in a large force bearing. For example, for a 9 m \(\times\) 9 m three-storey house (81 m\({}^{2}\) in storey area), the total is 243 m\({}^{2}\) in area. If the loading imposed on each square meter is assumed to be 15 kN (dead weight considered), the house totals 3645 kN in weight (243 m\({}^{2}\)\(\times\) 15 kN/m\({}^{2}\)). Given that the wall totals 54 m in length (9 m \(\times\) 6), the loading borne at the bottom averages at 67.5 kN/m (3645 kN/54 m). The wall section applied for the experiment registers 1.45 m in length. Therefore, upon its application, the vertical loading imposed upon should be approximately 100 kN (67.5 kN/m \(\times\) 1.45 m = 98 kN).  
The loading control in the horizontal direction is applied as follows. Before isolator slippage, the testing involves a loading-controlled approach. As the specimen enters the stage of isolator slippage, the testing then changes to a displacement-controlled approach. As the slippage reaches the upper limit, the stage of seismic resistance is initiated, and a loading-controlled approach is applied. The loading approach is listed in Figure 7 (one single cycle requires 10-15 min, which is then followed by a 5-10-min break to view the results). Upon the initiation of horizontal loading, such loading is imposed gradually in 5 kN increments (fourincrements to reach the initiating load) with the peak values at each loading cycle being 5, 10, 15 kN and the initiating load. Upon the slippage of the specimens, cyclic loading is imposed to reach the maximum slippage in 5-mm increments (5, 10, 15 mm, etc.). At the stage of seismic resistance, a 20 kN interval is adopted before the specimens reach the peak value.
# Cardiac Electrophysiology Meshfree Modeling through the Mixed Collocation Method
## 2 Materials and Methods
### Mixed Collocation Form of the Monodomain Equation
#### 2.1.1 Deriving the Mixed Collocation Form  
To derive the mixed collocation form of the decoupled cardiac monodomain model, only the diffusion term is considered, since the cellular reaction term is solved independently. We consider the discretization of the domain into a cloud of \(N\) arbitrarily distributed field nodes. We write the diffusion term from Equation (4) for each field node \(I\) as  
\[\frac{\partial V_{I}}{\partial t}=\mathbf{\
abla}\cdot\mathbf{q}_{I}\text{ in }\Omega,\quad I\in N \tag{5}\]  
where  
\[\mathbf{q}_{I}=\mathbf{D}_{I}\mathbf{\
abla}V_{I} \tag{6}\]  
denotes the transmembrane potential flux at the field node \(I\). Interpolating the transmembrane potential and the transmembrane potential flux, we obtain  
\[V_{I}=\sum_{i=1}^{n}\phi_{I}^{i}V_{I}^{i} \tag{7}\]  
\[\mathbf{q}_{I}=\sum_{i=1}^{n}\phi_{I}^{i}\mathbf{q}_{I}^{i} \tag{8}\]  
where \(n\) is the number of field nodes in the local support domain of the node \(I\). \(\phi_{I}^{i}\) is the \(i^{th}\) component of \(\mathbf{\phi}_{I}\). The vector \(\mathbf{\phi}_{I}\) contains the evaluation of the meshfree basis function of \(I\) at the \(n\) points of its local support domain, with \(I\) included as part of these points. \(V_{I}^{i}\) is the transmembrane potential at node \(i\) and \(\mathbf{q}_{l}^{i}\) the corresponding transmembrane potential flux vector.  
By introducing Equation (7) into Equation (6), we can express the transmembrane potential flux in terms of the nodal transmembrane potential as follows:  
\[\mathbf{q}_{l}=\mathbf{D}_{l}\sum_{i=1}^{n}\mathbf{\
abla}\phi_{l}^{i}V_{l}^{i},\quad I=1,2, \ldots,N \tag{9}\]  
or given in matrix form as  
\[\mathbf{q}_{l}=\mathbf{K}_{a}^{l}\mathbf{V}_{l},\quad I=1,2,\ldots,N. \tag{10}\]  
where \(\mathbf{K}_{a}^{l}\) is built based on the evaluation of the gradient of the meshfree basis function at the nodes in the local support domain of \(l\) scaled by \(\mathbf{D}_{l}\). Assembling the matrices \(\mathbf{K}_{a}^{l}\) for the \(N\) field nodes, \(I=1,2,\ldots,N\), the following equation in matrix form is obtained:  
\[\mathbf{q}=\mathbf{K}_{a}\mathbf{V}. \tag{11}\]  
Finally, introducing Equations (7)-(9) into Equation (5), the mixed collocation formulation of the monodomain model's diffusion term is obtained in terms of the transmembrane potential:  
\[\sum_{i=1}^{n}\phi_{l}^{i}\frac{\partial V_{l}^{i}}{\partial t}-\sum_{i=1}^{n} \mathbf{\
abla}\mathbf{\cdot}(\mathbf{D}_{l}\mathbf{\
abla}\phi_{l}^{i}V_{l}^{i})=0,\quad I \in N. \tag{12}\]  
Assembling the contribution of all field nodes \(N\), as above, we write Equation (12) in the equivalent matrix form:  
\[\mathbf{M}\hat{\mathbf{V}}+\mathbf{K}\mathbf{V}=\mathbf{0},\quad\mathbf{K}=\mathbf{K}_{a}\mathbf{K}_{a} \tag{13}\]  
where \(\mathbf{M}\) is the sparse matrix collecting the basis functions, \(\mathbf{K}_{a}\) is a sparse matrix collecting the evaluation of the divergence of the meshfree basis functions, and \(\mathbf{K}\) denotes the stiffness matrix.
## 2 Geographical and Geological Settings  
The study area is located in the northern part of Wanquan district, Zhangjiakou city, Hebei province, China (Figure 2a), with an area of about 1080 km\({}^{2}\) ranging from E 114\({}^{\circ}\)16\({}^{\prime}\)33\({}^{\prime\prime}\) to 114\({}^{\circ}\)38\({}^{\prime}\)36\({}^{\prime\prime}\) and N 40\({}^{\circ}\)44\({}^{\prime}\)14\({}^{\prime\prime}\) to 41\({}^{\circ}\)04\({}^{\prime}\)07\({}^{\prime\prime}\) (Figure 2b) and is located in the transition zone between the North China Plain and the Inner Mongolia Plateau, with high topography in the northwest and low topography in the southeast, undulating hills, and a mainly shallow-cut landform type. The landform type is mainly shallowly cut stripped-erosion low hills, with altitude between 1000 and 1300 m. The northern part is adjacent to the Zhangbei Dam Plateau. The area has a temperate semi-humid continental monsoon climate, with an average precipitation of about 400-500 mm. The soil type is mainly chestnut brown soil and brown soil [16], and the mountainous land is covered by larch, white birch, and mountain poplar, etc. [17], while bulk crops and vegetables grow on the plains.  
The strata in the study area belong to Archean, Sinian, Jurassic, Cretaceous, Neogene, and Quaternary periods, respectively, of which petrological descriptions are illustrated briefly in Figure 2b as notes [18; 19]. The spatial distribution has the following characteristics: the northern region is dominated by basaltic rocks of Hannuoba Formation (N\({}_{3}\)), the central and southeast region is dominated by Jurassic, Cretaceous, and Quaternary rocks or sediments, and the southwest region is dominated by Archean and Sinian rocks. Faults mainly trend northwest and northeast. Intrusions in the study area are less developed, except diabase stock in the northwest (Figure 2b).