esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Methodology
### Design of Vector Product Fusion Multiplication Technique  
The resultant outcome of the T5 and RoBERTa models was taken into consideration as a probability vector, and a fusion multiplication technique was developed. Fusion approaches are more commonly used for output concatenation for internal models. Maximums, minimums, means, averages, sums, differences, and products are some of these measures. A tweet's probability vector is refined using the RoBERTa and T5 models. A multiplicative fusion approach is used to combine the two probability vectors (the array of the last layer). The resulting vector is used to forecast the tweet label.  
\[A=\begin{bmatrix}x_{1}&y_{1}\\ x_{2}&y_{2}\\ \vdots&\vdots\\ x_{n}&y_{n}\end{bmatrix} \tag{12}\]  
\[B=\begin{bmatrix}u_{1}&v_{1}\\ u_{2}&v_{2}\\ \vdots&\vdots\\ u_{n}&v_{n}\end{bmatrix} \tag{13}\]  
where \(x_{i}+y_{i}\) and \(u_{i}+v_{i}=1\).  
Here \(x_{i}\) and \(y_{i}\) represent the likelihoods of fake and true news according to the RoBERTa model, respectively, whereas \(u_{i}\) and \(v_{i}\) represent the T5 model's probabilities of fake news versus legitimate news. Algorithm 1 shows a detailed illustration of the proposed multiplicative vector fusion technique.  
```
Initialization: 1.1: Real News 0: Fake News 0: Fake News 0: It represents the probability vector value of the T5 model. 0: It represents the probability vector value of the RoBERTa model. 0: It represents the size of the validation dataset. 0: Input: N, A, and B 0: Begin  for j = 1 to N \(Final\_classified/predicted\_value=MVF\left(A_{j},B_{j}\right)\)  end 09 Output: Final_classified/predicted_value(\(1-\text{Real},0-\text{Fake}\))
```  
**Algorithm 1** for the proposed Multiplicative Vector Fusion (MVF) technique  
To illustrate, supposing that A and B represent the RoBERTa and T5 probability matrices, respectively, then \(MVF(A,B)=AB\) is the multiplicative fusion vector of A and B in a single vector.  
\[MVF(AB)=\begin{bmatrix}x_{1}*u_{1}&y_{1}*v_{1}\\ x_{2}*u_{2}&y_{2}*v_{2}\\ \vdots&\vdots\\ x_{n}*u_{n}&y_{n}*v_{n}\end{bmatrix} \tag{14}\]  
\[FBEDL_{\text{T5}\left(news_{i}\right)}=\begin{cases}Fakex_{i}*u_{i}>y_{i}*v_{ i}\\ Realx_{i}*u_{i}<y_{i}*v_{i}\\ \omit\span\omit\span\omit\span\omit\span\omit\span\omit\span\omit\span \omit\span\omit\span\omit\span\omit\span\omit\span\omit\span\omit\span \omit\span\omit\span\omit\span\omit\span\omit\span\omit\span\omit\span \omit\span\omit\span\omit\span\omit\span\omit\span\omit\span\omit\span\omit \span\omit  
where \(x_{i}\)_and_\(u_{i}\) is the 1 st column's ith element where A and B are related. Similarly, \(y_{i}\)_and_\(v_{i}\) is the 2 nd column's ith element where A and B are related. Equation (15) depicts these possible outcomes.  
1. The news item will be predicted as Fake if \(x_{i}*u_{i}>y_{i}*v_{i}\) is true.
2. The news item will be predicted as Real if \(x_{i}*u_{i}<y_{i}*v_{i}\) is true.
3. The neutral condition rarely occurs because the proposed technique is trained and tested on the dataset as a binary classification.
# Transfer Learning-Based Fault Diagnosis under Data Deficiency
###### Abstract
They have validated their method by both the bearing and gearbox datasets. [PERSON] et al. [13] proposed domain adaptation combined with a deep convolutional generative adversarial network (DA-DCGAN) for the DC series arc fault diagnosis in a photovoltaic (PV) system. They used normal and fault data of the source domain (PV emulator in the laboratory) and only normal data of the target domain (PV system in the field) for an adversarial training process. By generating fault data of the target domain artificially, high detection accuracy was achieved. [PERSON] et al. [14] proposed a deep convolutional transfer learning network (DCTLN) for intelligent fault diagnosis of machines with unlabeled data. They optimized their network models in the direction of minimizing the maximum mean discrepancy (MMD) between the source and target domain datasets for learning domain-invariant features from raw signals. The above-mentioned papers have shown several improvements in the fault diagnosis by using TL, but the imbalance issue in the sample size between the normal and fault data was not studied, which may affect the performance of TL greatly. Even though a few papers [11; 12] have addressed a similar issue, it was about the sample imbalance between the source and target domain, in which the size of fault data is the same or greater than the normal. Recalling that the fault does not occur often in the real operation conditions because of the periodic maintenance or the costly effort to make by intention, the imbalance issue between the normal and fault data is of great importance for practical use. For this reason, the effect of transfer learning in the fault diagnosis of imbalanced data is investigated in this study.
## 4 Stability Analysis  
It should be noted that the control scheme in this section is based on the assumption that all states are measurable and feedback is available.  
Define the desired surge and sway speeds.  
\[\begin{bmatrix}u_{d}\\ v_{d}\end{bmatrix}=\overline{\Gamma_{h}}\begin{bmatrix}x_{d}+l_{x}\text{tanh} \begin{pmatrix}-\frac{k_{x}}{l_{x}}x_{e}\\ y_{d}+l_{y}\text{tanh}\begin{pmatrix}-\frac{k_{y}}{l_{y}}y_{e}\end{pmatrix} \end{bmatrix} \tag{27}\]  
where  
\[\overline{\Gamma_{h}}=\begin{bmatrix}cos\psi&sin\psi\\ -sin\psi&cos\psi\end{bmatrix}\]  
\(k_{x},k_{y},l_{x},l_{y}\) are constants, and \(k_{x},k_{y}>0,l_{x},l_{y}\
eq 0\).  
Bring in Formulas (13) and (27), and the result is as follows:  
\[\begin{bmatrix}e_{u}\\ e_{v}\end{bmatrix}=\overline{\Gamma_{h}}\begin{bmatrix}\dot{x}_{e}-l_{x}\text{ tanh}\begin{pmatrix}-\frac{k_{x}}{l_{x}}x_{e}\\ \dot{y}_{e}+l_{y}\text{tanh}\begin{pmatrix}-\frac{k_{y}}{l_{y}}y_{e}\end{pmatrix} \end{bmatrix} \tag{28}\]\(\left|\overline{\Gamma_{h}}\right|=1\), the matrix is non-singular. When the speed error \(e_{u}\), \(e_{v}\) converges to 0, it can be guaranteed.  
\[\begin{split}\dot{x}_{e}&=l_{x}\text{tanh}\Big{(} -\frac{k_{x}}{l_{x}}x_{e}\Big{)}\\ \dot{y}_{e}&=l_{y}\text{tanh}\Big{(}-\frac{k_{x}}{l_ {y}}y_{e}\Big{)}\end{split} \tag{29}\]  
Based on the Lyapunov stability theory, the adaptive controller is referenced according to the AUV motion Equation (6) and the RBF-based model (Figure 2). The AUV trajectory tracking error and velocity error are asymptotically stable. Let V represent the Lyapunov function. If the following conditions are met, the actual curve tracks the upper expected curve \((\chi=\chi_{m})\), that is, the actual system output is equal to the reference model system output. Then, it shows that the trajectory tracking error is asymptotically stable. Furthermore, the equilibrium point of the closed-loop system at the origin is globally asymptotically stable according to the probability.  
The system output error predicted by the neural network, that is, the output of the neural network, is:  
\[y=w_{f}h_{f} \tag{30}\]  
The actual error encountered by the AUV during operation is:  
\[y=w_{f}h_{f}+\delta_{e} \tag{31}\]  
Let us define a Lyapunov function:  
\[V=\tfrac{1}{2}(x-x_{m})^{2}+(y-y_{m})^{2} \tag{32}\]  
Taking the derivative of the Lyapunov function and substituting (29) into it gives:  
\[\dot{V}=x_{e}\dot{x}_{e}+y_{e}\dot{y}_{e}=-x_{e}l_{x}\text{tanh}\bigg{(}\frac {k_{x}}{l_{x}}x_{e}\bigg{)}-l_{y}y_{e}\text{tanh}\bigg{(}\frac{k_{y}}{l_{y}}y_ {e}\bigg{)} \tag{33}\]  
It can be concluded from the above formula that when \((x_{e},y_{e})\
eq 0\), \(k_{x},k_{y}>0\), and \(l_{x}\), \(l_{y}\
eq 0\), \(\dot{V_{1}}<0\), it can be explained that \((x_{e},y_{e})\) in this case gradually converge to \((0,0)\).
## 5 Results and Discussion
### Discussion of Results
#### 5.3.4 Advanced Flexible Services  
Some of the services and capabilities implemented in the proposed architecture are not exclusive to AIoT networks. Conventional aeronautical test control systems have functions similar to those described above for the wireless system. However, AIoT technology provides certain advantages and test capabilities that conventional systems do not offer or offer in a limited way.  
Among the advanced services offered by AIoT architectures is the direct connection to the computing center through mobile applications, which allow requesting test information on demand and from any location, as well as sending alerts and warnings automatically. Connecting new MQTT clients to the network, which could correspond to sensors on the edge or mobile clients, is quick and easy to perform. A large amount of resources is available for the application of ML techniques to process the recorded data and make estimates of future values to anticipate possible execution errors and reduce the risk of failure.  
Additionally, AIoT architectures can benefit from cloud services to store large amounts of recorded data, apply deep analytics without impacting the computational performance of processes in the edge and fog layers, and provide remote access to all this information from anywhere and on any device.
## 1 Introduction  
In various rock engineering projects, most of the rock destabilization is due to the change in the natural stress field of the rock mass during the construction process, such as the excavation of underground caverns, the drilling of anchor holes during the support process, the change in pore water pressure after water injection, etc. The internal cracks of the rock are disturbed by the construction process, resulting in the initiation, expansion, and penetration, forming a macroscopic fracture zone, which eventually leads to the collapse and failure of the rock mass.  
Therefore, several scholars have conducted research on the deformation and failure characteristics of rock masses containing multiple cracks and compound defective cracks. [PERSON] [1] and [PERSON], et al. [2] prefabricated cracks inside granite, and observed the propagation and fracture mechanism of cracks under uniaxial compression. The authors [3; 4; 5; 6; 7; 8; 9] have conducted numerous studies and summaries on the expansion pattern of prefabricated fractures in cracked rock. [PERSON], et al. [10; 11; 12] summarized the multi-fracture interaction, penetration, and linkage mechanisms, and analyzed the influence law of the parameters and geometric distribution of fractures on the deformation and damage characteristics of marble. The authors [13; 14; 15; 16; 17] carried out experimental studies of rock damage with combined hole-crack defects, hole-hole, and different hole shapes under compression.  
[PERSON] [18] analyzed the effects of the coplanar angle and ligament angle on the mechanical parameters and fracture process of red sandstone containing prefabricated elliptical holes. [PERSON] et al. [19] proposed that the longer and wider the crack, the lower the strength of the specimen, and that the damage to the specimen is mainly caused by shear cracks. [PERSON] et al. [20] conducted biaxial compression tests on single-hole sandstone to simulate the damage of brittle rock during underground excavation.  
In real rock materials, there are many defects, such as joints and cracks, with varying shapes and disorderly distribution. It is also difficult to arrange built-in cracks, so it is difficult to summarize the crack propagation law and its impact on the mechanical properties of materials using rock for testing. This study formulated a brittle mortar material. Through physical and mechanical parameter testing of the mortar material, the results compared with sandstone showed that the mortar material is a heterogeneous material with good brittleness, and its physical and mechanical parameters are all within the range of sandstone. As a substitute for sandstone, testing can effectively simulate its stress deformation characteristics. On this basis, biaxial compression experiments are conducted on the samples to investigate the generation, development, and association of secondary cracks in the cracked samples under different hole depths and compression loads, to study the mechanism of crack propagation, and to obtain damage modes under different excavation depths.
## 2 Related Work on Bridging Between Sensors and Applications
### Centralized Sensor Web Portals  
Emerging centralized web portals for sensors can be seen as a new class of systems to enable access to sensor resources on the application level. Such Sensor Web portals enable users to upload and share sensor data. The support of data formats depends on the portal and may range from numeric data (e.g., temperature measurements) to audio and video data (e.g., from Web cameras). Uploaded data can then be queried and displayed by end users for example as time series charts or video feeds. Instances of such systems are SensorMap with its underlying SenseWeb infrastructure [37], SensorBase [38], Pachube (_[[http://www.pachube.com_](http://www.pachube.com_)]([http://www.pachube.com_](http://www.pachube.com_))), as well as Sensorpedia [39]. Specific subtypes of such Sensor Web portals are platforms which are specialized for certain sensor types or domains. Examples are Weather Underground (_[[http://www.wunderground.com_](http://www.wunderground.com_)]([http://www.wunderground.com_](http://www.wunderground.com_))) allowing people to register their home weather station and contribute their measured data to weather forecast computations, or EarthCam (_[[http://www.earthcam.com_](http://www.earthcam.com_)]([http://www.earthcam.com_](http://www.earthcam.com_))) which links the video feeds from thousands of Web cameras.  
Such Sensor Web portals offer APIs to the public for registering sensors, uploading sensor data, as well as querying inserted data. Once registered, the discovery of sensors is also supported. However, a controlling or tasking of sensors, as provided by OGC's SPS service, is usually not possible. Except for Sensorpedia, which supports the SOS as a data source [40], none of the portals leverage SWE standards.  
The centralized approach of the portals is the main difference to the decentralized approaches of Sensor Web infrastructures described in Section 2.2. Metadata of registered sensors as well as uploaded sensor data are hosted by the centralized portal instead of separate service components within enterprise architectures. This may be unsuitable for use cases with needs for full control over deployment and administration set up or strict data privacy regulations.
# A Hybrid Route Selection Scheme for 5G Network Scenarios: An Experimental Approach
## 2 Related Work
### Routing in 5G
#### 2.1.2 Traffic Splitting among Available Routes  
Traffic splitting allows traffic to be split and distributed among different routes in order to improve load balancing and reduce traffic congestion at BSs serving large traffic streams. Traffic splitting is possible in 5G networks as the CC is aware of source nodes containing a particular traffic stream, as well as the available routes towards a destination.  
In [21; 31], a centralized route selection scheme is proposed to split and distribute large traffic streams among available routes in order to achieve load balancing. The scheme addresses the dynamicity of channel availability. The CC: (a) gathers network-wide information (i.e., network topology comprised of nodes and links, resources comprised of channel availability, and traffic patterns); (b) identifies source nodes that possess a particular traffic stream requested by a destination node; (c) estimates the channel capacity and end-to-end delay of routes established between a pair of source and destination nodes; and (d) dynamically segregates a traffic stream into different fractions and sizes contributed by different source nodes based on the channel capacity and the amount of traffic of the candidate routes. Based on the routing decisions made by the CC, a source node sends parts of a traffic stream in fractions to a destination node. Intermediate nodes that receive the fractions from different source nodes aggregate them, and subsequently split them among available outgoing links towards a destination based on the shortest route, in terms of the number of links and the current traffic amount of each route. The destination node aggregates the fractions to obtain the original traffic stream. The proposed scheme is shown to reduce end-to-end delay. A similar traffic splitting scheme has been presented in [33], although the intermediate nodes that receive the fractions do not aggregate and split them. In [36], intermediate nodes receive updated routing tables to address the split traffic to the right destination. The split traffic scheme is shown to improve throughput and reduce handover and end-to-end delay.
## 3 Results and Discussion
Moreover, the differences in relative values observed across various months--established through both prior data analysis and laboratory-scale experiments--were effectively modeled by these approaches. As a result, the inclusion of the \"month\" variable enabled the selected methods to better capture monthly seasonality, improving the overall accuracy and interpretability of the time series forecasts. Figure 7 illustrates the graph depicting the distribution of actual and predicted values of the target variable. It is evident that the prediction accuracy is predominantly high. To further assess the model's performance, a residual analysis was conducted. The graphical representation of this analysis is shown in Figure 8.  
Since the median of the residuals is shifted away from zero, this may indicate the presence of systematic error--this model slightly overestimates values, as suggested by the slightly elongated left tail. Additionally, the linear relationship describing the residual scatter has a low coefficient of determination, which can be interpreted as a sign of the absence of dependency. Neither overfitting nor underfitting of this model is clearly indicated. However, it is evident that the model's accuracy can be improved. As previously mentioned and discussed in the literature, the most effective approach to achieving this is to introduce new significant predictors into this model. Future studies will focus on selecting predictor sets for both time series modeling and regression tasks, utilizing SHapley Additive exPlanations (SHAP) analysis [44]. Figure 7: Actual vs. Predicted Values (BOD\({}_{5}\)) on Test Set (ARIMA–LSTM). Based on the predicted BOD\({}_{5}\) concentrations, adjustments to the activated sludge dosing were carried out by modifying the ratio of return sludge to excess sludge in the physical model. Since the characteristics of the substrate used imply a nonlinear relationship between COD and BOD\({}_{5}\), direct measurement of BOD\({}_{5}\) was not feasible. This limitation made it difficult to promptly adjust the specific organic loading rates, as such adjustments had previously been performed five days after wastewater introduction--corresponding to the conventional five-day BOD\({}_{5}\) incubation period required for this analysis. To overcome these challenges, machine learning approaches were employed to refine wastewater characteristics in advance. By enabling preventive control of key operational parameters, these predictive methods enhanced overall system stability. As a result, no emergency conditions arose during subsequent model operations. In particular, the system exhibited no filamentous bulking of the activated sludge, a problem commonly associated with unstable treatment systems subjected to rapid fluctuations in incoming wastewater composition.
## 2 Materials and Methods
### Data Analysis and Statistics  
Proteins were identified by Proteome Discoverer (vs 2.2, Thermo Fisher Scientific Inc.) and corresponding abundances were extracted for each sample. Proteins with valid values for each sample in at least 2 out of 4 replicates were processed, while the rest were filtered out. Data were normalized and log2 transformed using variance stabilizing normalization with the vsn package in R [60,61]. Missing values were imputed under a Missing Not At Random (MNAR) assumption by random draws from a left-shifted distribution using the impute function of the DEP package [62]. Differential expression of proteins across experimental conditions was revealed by tests based on protein-wise linear models and empirical Bayes statistics incorporated in the DEP package. Differentially Expressed Proteins (DEPs) with \(p\)-value less than 0.05, that exhibited 2-fold absolute change at least in pairwise treatment comparisons, were selected and further processed.
# Research of Park and Ride Parking Spaces Tiered Pricing Methodology Based on Subway Ride Distance
###### Abstract  
Park and Ride (P&R) as a demand management tool has the effect of reducing traffic congestion in urban centers, saving energy and reducing pollutant emissions. Since 2000, many cities in China have been constructing P&R facilities, which have partially alleviated urban traffic congestion and provided a time-reliable mode of travel for commuters heading to urban centers. However, in recent years, due to the pricing policy of the P&R facility, there has been an insufficient supply of P&R facilities in many places. In fact, the P&R system prefers to welcome travelers who make long-distance subway rides and does not want those who make short-distance subway rides to occupy more parking spaces. To address this, this paper proposes a tiered pricing strategy that considers charging parking fees based on the distance traveled by commuters after switching to public transportation, to improve the utilization of P&R. That is, charge less for parking for long-distance subway riders and more for short-distance subway riders. Firstly, based on questionnaire data from SP surveys, a fixed pricing mixed logit model (FP model) and a tiered pricing mixed logit model (TP model) for P&R facilities are constructed. Utilizing two models, we explored the mechanisms underpinning traveler's mode choice influenced by daily habits and travel considerations through the comparison of the two models to validate the effectiveness of the tiered pricing for P&R facilities. The study found that the implementation of a tiered pricing method for P&R facilities increases its attractiveness to long-distance subway ride travelers, resulting in a higher proportion of long-distance subway riders among P&R commuters. In the study's last section, a marginal effect analysis was conducted on the per-kilometer cost (Pkm) within the P&R model. This analysis determined the optimal Pkm for three subway travel distances within the P&R model. Consequently, it calculated the corresponding P&R parking fees for these three subway travel distances. Additionally, we have predicted the implementation effects of the tiered pricing scheme.  
park and ride; tiered pricing strategy; insufficient parking spaces; mixed logit; marginal effects +
Footnote †: journal: Applied
## 3 DST-LEO Satellite-Traffic Prediction Model
### Model Architecture  
The main architecture of the DST-LEO satellite-traffic prediction model consists of a data separation module, a spatial dependency extraction model, a temporal dependency extraction model, a data fusion module, an attention mechanism, and an output layer. Due to the complexity of the dynamic topology, the model utilizes a graph convolutional network (GCN), the data separation and fusion module, and the spatio-temporal attention mechanism. These components complement each other, enabling the model to accurately capture the changing patterns of traffic.