esa-sceva/satcom-chunk-collection huggingface dataset:
# Kolmogorov-Arnold Finance-Informed Neural Network in Option Pricing
## 7 Discussion
### Discussion on American Put Pricing
#### 7.2.2 Convergence  
As illustrated in Figure 6, the KAFIN model demonstrates a comparable performance to that observed in the European call option pricing experiment, exhibiting rapid convergence in the context of American put option pricing. All monitored loss functions exhibit a stable trend, indicating consistent performance throughout the training process. This rapid stabilization underscores the effectiveness of the KAFIN approach in integrating multi-modal data and leveraging advanced training techniques. Such capabilities are crucial in enabling the model to consistently perform well in the inherently complex and dynamic financial environments. Table 6 summarizes the quantitative statistics of the loss trend, in which KAFIN outperforms all other models, particularly in terms of the minimum (MIN) loss, demonstrating its superior ability to achieve optimal solutions. Despite having the highest values in terms of MAX and STD among the models, KAFIN exhibits the lowest MIN total loss of 0.4742, which is significantly better than the next best model, FINN REG, with a MIN total loss of 1321.4523. This stark contrast highlights KAFIN's ability to minimize loss effectively, particularly under favorable conditions, where the model achieves its best performance. The MIN value is critical as it reflects the smallest loss observed during training, serving as an indicator of how well the model performs under ideal scenarios. KAFIN's low MIN suggests that it consistently finds optimal solutions, even in challenging environments. While MAX and STD are larger for KAFIN compared to other models, these metrics do not detract from its overall superior performance. In fact, the large MAX (27,701.9805) and STD (15.9445) values can be interpreted as a reflection of KAFIN's exploratory nature, where the model is capable of covering a wider range of the loss surface. This characteristic is beneficial in complex tasks where the model needs to escape local minima and discover more generalized patterns that other models might overlook. These larger fluctuations are indicative of KAFIN's capacity to explore different regions of the solution space, potentially identifying better overall solutions. Furthermore, the ability of KAFIN to maintain a low MIN value indicates that it has achieved a stable and robust solution during the optimization process, even if it experiences occasional fluctuations in the loss values. The larger MAX and STD values should not overshadow the model's effectiveness in consistently achieving low-loss predictions under normal conditions, which is critical in many applications requiring high precision and accuracy. The performance of KAFIN can be attributed to several key factors. Its architecture is well-suited to capture complex patterns in the data, and its training strategy focuses on minimizing loss across various metrics. Moreover, the flexibility of the model allows it to adapt to diverse data distributions and find optimal solutions that align closely with the underlying data structure.
# Study on Fire Smoke Distribution and Safety Evacuation of Subway Station Based on BIM
## 4 Fire Smoke Analysis of Subway Station
### Fire Model of Subway Station  
#### 4.1.1 Location of Fire Source  
According to the location difference of fire source, subway station fires are generally divided into three types: platform fire, train fire and station hall fire. Through the analysis of the causes of the subway fire accidents, when the luggage and belongings of the passengers catch fire in the carriage, the train will not be affected to continue running, and the passengers can be evacuated at the next station. The evacuation can be completed after all the passengers leave the train, and the evacuation time is relatively short. The station hall floor has a larger area, which is closer to the ground and easier to evacuate. In terms of danger, the loss may be relatively small. However, in the fire condition of platform floor, the direction of smoke flow is consistent with the evacuation direction of passengers, and the evacuation distance is long and the flow density is high, so the risk is greater. Therefore, this study mainly considers the fire on both ends of the platform and the middle of the platform.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Original Scheme** & **Entrance 1** & **Entrance 2** & **Entrance 3** \\ \hline Evacuation facility combination & Double escalators + Stair & T-shaped stair & Double escalators + Stair \\ \hline \hline \end{tabular}
\end{table}
Table 2: Combination form of original evacuation facilities.  
Figure 4: BIM model of the subway station.  
According to the most unfavorable principle, in this calculation, the subway station does not consider other mechanical smoke control measures, and only relies on natural ventilation.
# Enhanced Multicast Repair Fast Reroute Mechanism for Smart Sensors IoT and Network Infrastructure
## 6 Evaluation of the EM-REP Proposal  
The functionalities of the enhanced version of the M-REP algorithm (EM-REP) proposed in Sections 5.1 and 5.2 have been verified by simulations. The implementation of the algorithm itself together and its extensions, as well as the creation of testing scenarios, were performed in the OMNeT++ discrete event simulator. The implementation is based on modification of the Automated Network Simulation and Analysis (ANSA) [69] and INET Framework Objective Modular Network Testbed in C++ (OMNeT++) libraries [70]. The ANSA library implements the multicast technology and the INET provides OSPF routing functionalities.  
The correct behavior of the enhanced M-REP algorithm functions proposed in the paper has been successfully tested using several scenarios. The scenarios simulate various types of failures for various topologies. In these scenarios, we focus on the correctness of the partial activities of the algorithm, as well as on the investigation of the correct delivery of packets belonging to the protected flow to its destination. In this section, we introduce one of the comprehensive testing scenarios. The topology  
Figure 12: Manual configuration of Router D.  
used in the scenario is shown in Figure 13. As a unicast routing protocol, the OSPFv2 protocol in a multiarea deployment model has been used. The routing domain consists of five OSPF areas, 23 routers and three hosts. For testing purposes, we generate data flow originated from host H11 to the H42 receiver. This data represents a protected stream of user datagrams, the delivery of which is ensured by our algorithm. In the case of a stable and error-free network condition, the delivery of packets is following the shortest path selected by OSPF (in Figure 13 represented by red arrows). In this scenario, we simulate the occurrence of several (three) independent network failures (the improvement introduced in Section 5.1), that occurs inside of different OSPF areas. The purpose of the simulation is to observe how the algorithm will protect user data in the event of multiple network failures within three separated areas.  
Figure 13: The OMNeT++ simulation topology.  
The description of scenario is as follows. At the beginning of the simulation we wait 200 ms to complete the process of network convergence (i.e., the convergence of OSPF unicast routing), then at the time of 200 sims (simulation seconds) the H11 host starts generating data flow. The source is the H11 with IPv4 address 192.168.11.2, the destination is the host H42 with IPv4 address 192.168.66.2. At the time of 210 sims we simulate the first failure, and the R14 router is shut down. At 212 sims we simulate the second failure as a permanent connection failure between routers R13 and R16. Finally, at 215 sims we simulate another failure of router R05 (Table 4).
# Person Re-Identification across Data Distributions Based on General Purpose DNN Object Detector
## 3 Theory Overview
### YOLO Model
#### 3.3.1 Darknet Architecture  
Darknet neural network has multiple convolutional layers. Figure 4 presents the architecture of this network. The last two layers are fully connected. While the first layers extract the input image features, the last two layers predict the probabilities and output coordinates. The weight and the height of the predicted bounding boxes are normalized to the input image's size. Therefore, the output values will be in the range [0,1].  
The YOLO model is trained in order to minimize the sum-squared error. Two scaling parameters are also introduced to control the loss function. This function is computed while using the equation:  
\[L=\lambda_{coord}\sum_{i=0}^{s^{2}}\sum_{j=0}^{B}1_{ij}^{obj}[(x_ {i}-\hat{x}_{i})^{2}+(y_{i}-\hat{y}_{i})^{2}+(w_{i}-\hat{w}_{i})^{2}+(h_{i}- \hat{h}_{i})^{2}]\\ +\sum_{i=0}^{s^{2}}\sum_{j=0}^{B}1_{ij}^{obj}(C_{i}-\hat{C}_{i})^ {2}+\lambda_{noobj}\sum_{i=0}^{s^{2}}\sum_{j=0}^{B}1_{ij}^{obj}(C_{i}-\hat{C}_ {i})^{2}+\sum_{i=0}^{s^{2}}1_{i}^{obj}\sum_{eclease}[p_{i}(c)-\hat{p}_{i}(c)]^{2} \tag{7}\]  
where:  
* \(1_{i}^{obj}\) indicates if there is an object in cell \(i\);
* \(1_{ij}^{obj}\) refers to the bounding box \(j\) in cell \(i\), which is responsible for the prediction. This is the bounding box with the highest IOU relatively to the real bounding box (described in Figure 5).
* \(C_{i}\) represents the confidence score of the responsible predictor, from cell \(i\);
* \(\hat{C}_{i}\) represents the predicted confidence score;
* \(P_{i}(c)\) defines the conditional probability that the cell \(i\) contains an object from class \(c\); and,  
Figure 4: Darknet architecture.  
* \(\lambda_{coord}\) represents the scaling parameter that leads to the increasing of the loss function according to the coordinates' predictions of bounding boxes. YOLO model uses the parameter \[\lambda_{coord}=5\] (8)
* \(\lambda_{noobj}\) represents the scaling parameter that leads to the decreasing of the loss function, according to the predictions for the confidence score of the bounding boxes that do not contain objects. This system uses the scaling parameter \[\lambda_{noobj}=0.5\] (9)  
According to Equation (7), the classification errors only appear when there is an object in cell \(i\), while, otherwise, \(1_{i}^{obj}=0\). Furthermore, the coordinates error only intervenes when it comes to the responsible predictor. For all the others predictors, \(1_{ij}^{obj}=0\).  
Because to the improvements, the YOLO model has several advantages when compared to other classification-based systems. First of all, it knows the image's global context, because it looks at the whole image, not only in the image regions.  
Furthermore, it makes predictions after a single network evaluation, unlike the R-CNN network, which requires thousands of evaluations for a single image. This also leads to the second advantage. The YOLO model is high-speed. It is 1000 times faster than than the R-CNN network, and it is 100 times faster than the Fast R-CNN network [8].
# An Adaptive INS-Aided PLL Tracking Method for GNSS Receivers in Harsh Environments
## 3 Tracking Error Analysis of Delta Doppler Aided PLL
### Thermal Noise Error  
The variance of the steady-state phase tracking error \(\Psi_{ss}^{T}\) due to the input thermal noise \(n_{\theta}^{T}\) is given as [20]:  
\[\mathrm{var}[\Psi_{ss}^{T}]=2B_{n}T(1+\frac{64\zeta^{4}+16\zeta^{2}+16\zeta^{2 }B_{n}T}{(16\zeta^{4}+8\zeta^{2}+1)(4\zeta^{2}+1)}B_{n}T)\mathrm{var}[n_{\theta }^{T}] \tag{26}\]  
where \(\mathrm{var}[\cdot]\) is the variance function. The variance of thermal noise is related to \(C/N_{0}\) and integration time \(T\), which can be expressed as:  
\[\mathrm{var}[n_{\theta}^{T}]=\frac{1}{2 TC/N_{0}}\left(1+\frac{1}{2 TC/N_{0}}\right) \tag{27}\]  
Define:  
\[c_{2}=2B_{n}T(1+\frac{64\zeta^{4}+16\zeta^{2}+16\zeta^{2}B_{n}T}{(16\zeta^{4}+ 8\zeta^{2}+1)(4\zeta^{2}+1)}B_{n}T) \tag{28}\]  
Then, \(\mathrm{var}[\Psi_{ss}^{T}]\) can be expressed as:  
\[\mathrm{var}[\Psi_{ss}^{T}]=c_{2}\mathrm{var}[n_{\theta}^{T}] \tag{29}\]
## 4 Validation and Discussion of Land Subsidence Results
### Typical Land Subsidence Areas Discussion
#### 4.3.2 Land Subsidence along the Subway Analysis  
The reason for the subway deformation mainly includes an increase in construction, a change in the groundwater system, and geological conditions. These factors interact with each other, resulting in the complexity and inhomogeneity of surface settlement. Therefore, it is necessary to take scientific and reasonable measures to process and prevent surface  
Figure 12: The accumulative displacement of characteristic points (the position of points is shown in Figure 7).  
subsidence in the process of subway construction and operation. The experimental results are consistent with the experimental results of rail transit routes in the 2021 Shanghai Geological Environment Bulletin [55]. Based on the degree of time series deformation during 2016-2022, the approximate construction time of the subway, the maximum annual average deformation rate of the railway, and the clarity of the display of the subway deformation information, we divide the subway into two parts for analysis.  
Figure 13a shows the deformation state of Lines 2, 3, 5, 6, 10, 14, 15, 17, 18, and 19, among which Lines 14, 15, 17, and 18 were built in recent years. Line 14 is scheduled to be put into operation by the end of 2021. It will pass through the most densely populated area of Shanghai and become the rescue line of Line 2, which is the line with the highest passenger flow in Shanghai. Subway line 15, with a total length of 42.3 km, began operation on 23 January 2021. Line 15 is a north-south traffic line from the BS District to the MH District. Metro Line 17 began operation on 30 December 2017 and is an east-west fast transportation line between Honggiao Railway Station and Dongfangdi. Subway line 18 is planned to pass through 26 stations, with a total length of approximately 36.1 km, going through the Yangtze River to Hangtou; the most obvious settlement occurred at Fudan University Station, with a maximum settlement rate of \(-\)18.2 mm/a, connecting the No. 12 subway line and the No. 19 maglev train. Part of the reason for the settlement of these subways is the large amount of earthwork excavation and underground structure construction in the process of city development. Meanwhile, these works affect the surrounding soil and groundwater systems, finally resulting in surface settlement. Figure 13b shows the relatively stable subways in Shanghai. One reason is that the construction time is long, all the conditions are basically stable, and the settlement is within a controllable range.
## 2 Materials and Methods
### Photocatalysis Experiments: Degradation of Ciprofloxacinacin  
In a typical experiment, the photocatalyst film was positioned in the centre of a borosilicate glass beaker and immersed in an aqueous solution (50 mL) containing CIP (C\({}_{0}\) = 10 ppm) on. The beaker was covered with aluminium foil and kept in the dark for 30 min while stirring (150 rpm) to achieve adsorption-desorption equilibrium. After 30 min of initial dark, the foil was removed and the beaker was irradiated for 180 min. When required, H\({}_{2}\)O\({}_{2}\) was added to the starting solution to reach a (5 \(\times\) 10\({}^{-4}\) M) concentration. Aliquots (1 mL) of irradiated solution were taken at fixed time intervals and UV-Vis spectra were recorded to evaluate the extent of CIP disappearance. The absorbance values at \(\lambda_{\text{max}}\) = 272 nm were collected and the degradation of CIP was reported as:  
\[\text{Degradation efficiency (\%)}=\left(\frac{\text{C}_{\text{o}}-\text{C}_{ \text{t}}}{\text{C}_{\text{o}}}\right)\times 100=\left(\frac{\text{A}_{\text{o}}- \text{A}_{\text{t}}}{\text{A}_{\text{o}}}\right)\times 100 \tag{3}\]  
where C\({}_{\text{o}}\) and A\({}_{\text{o}}\) are the starting molar concentration and absorbance of CIP while C\({}_{\text{t}}\) and A\({}_{\text{t}}\) are the molar concentration and absorbance of CIP at time t. Control experiments were carried out by irradiating the starting solution of CIP in the absence of any photocatalyst.  
The irradiation was provided by three halogen lamps (Osram (Berlin, Germany) HALOPAR 30, 75W, 30\({}^{\circ}\) aperture angle, luminous flux of 350 lumens) positioned in a vertical support at 40 cm height from the reactor, and they were slightly inclined to direct the focus towards the centre of the beaker containing the catalyst and the CIP solution. The spectral irradiance of the light source is reported in Figure S1. Cooling was provided by adding a constant supply of ice around the reactor with a monitored temperature (35-20 \({}^{\circ}\)C). UV-Visible absorption spectra were recorded using a VARIIAN Cary 5000 UV-Vis-NIR spectrophotometer. When working in photo-Fenton mode, a 5 \(\times\) 10\({}^{-4}\) M of H\({}_{2}\)O\({}_{2}\) is employed, which is high enough to ensure a large excess (ca. 20 times with respect to the 2.59 \(\times\) 10\({}^{-5}\) M CIP) and low enough for the absorbance of H\({}_{2}\)O\({}_{2}\) not to interfere with the spectrophotometric determination of CIP.
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Business Model** & **Traditional Bank** & **Digital Model** & **Banking 5.0 Model** \\ \hline Philosophy & Providing services & Digital Banking & Embedded banking, personalization, humanization \\ \hline \multirow{4}{*}{Suggested value} & Limited services of the branch & \multirow{2}{*}{Unlimited, can go beyond the geographical location of the banking institution and have access 24 h a day} & \multirow{2}{*}{Instant Services, FinTech, Stability, + Services} \\  & network and staff, performed at specific times, services & & \\  & depend on the qualifications & & \\  & and experience of the & & \\  & bank employee. & & \\ \hline Proximity & It takes time and money & Fast service, communication & \\  & & via SMS and email newsletter & \\ \hline Segmentation & Segmentation & Online, in the branch & A customer department \\ \hline \multirow{4}{*}{Location} & However, flexibility is limited to a small number of service channels & Flexible and performed through any channel suitable for the customer & \\  & & & \\ \hline Platform, context, platform & Old systems & Digital and automatic & Multi-service platforms, artificial intelligence, API \\ \hline Processes & Bureaucratic & Bureaucratic & Flexible and pure \\ \hline \multirow{4}{*}{Persons} & The operator’s duties are & The operator’s tasks are & \\  & performed by a & performed by the bank & \\  & bank employee & customer or automatically & \\ \hline Partnership & Limited to bank insurance & Unity and participation & Ecosystem, FinTech, Big Tech \\ \hline \multirow{4}{*}{Pricing} & Top, taking into account bank & \multirow{2}{*}{Low service, often free} & \multirow{2}{*}{Pricing risk, machine learning} \\  & costs for personnel and maintenance departments & & \\ \hline \multirow{4}{*}{Payments} & Key models are about staff & Articles are key to buying and & \\  & and department maintenance & maintaining servers & Pay as you go \\ \cline{1-1}  & & and bundles & \\ \hline Protection & physical protection & Password and PIN & cybersecurity \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Banking 5.0 compared to the past [31]. been conducted to examine how human beings become proficient in this ever-changing dynamic environment, which of course is only one of the problems. Figure 1 provides a comparison between the concepts of the fourth and fifth industrial revolution Therefore, given the difficulties of the path for banks to enter the fifth industrial revolution, it is very important to carefully examine issues such as the problems, obstacles, and solutions to move banks as quickly as possible to transform the processes. Therefore, the following questions have been formed in the minds of researchers and this article has been written to answer them:  
* What are the indicators of industrial revolution 5.0 for successful entry into Banking 5? * What connection can be found between the identified indicators? * Which indicators should be focused on for successful word Banking 5.0?
# MM-iTransformer: A Multimodal Approach to Economic Time Series Forecasting with Textual Data
## 2 Related Work
### Text-Enhanced Economic Time Series Forecasting  
Multimodal learning, which incorporates both time series and textual corpora as input data sources, has generated significant interest for economic time series forecasting. Mostly, the focus has been primarily on sentiment analysis from financial news and social media to predict stock market trends [33]. In [34], a method that estimates social attention to stocks by sentiment analysis and influence modeling is proposed to predict the movement of the financial market. These methods treat the prediction as a classification problem, where the goal is to determine the direction of market movements based on sentiment polarity.  
Moreover, when the objective shifts to forecasting specific future values of a time series, the task becomes a regression problem, which is inherently more challenging than simple directional predictions. Significant efforts have been made to enhance the accuracy of such forecasts. For instance, a study enhances stock forecasting by integrating external sentiment indicators derived from online text with traditional stock index series, showing improved accuracy on the Shanghai Stock Exchange Composite Index [35].  
The Modality-aware Transformer, introduced in Ref. [36], is a novel multimodal transformer-based model designed to utilize both textual and numerical data to enhance time series forecasting. The model employs specialized attention mechanisms to focus on the most relevant features across different modalities, resulting in improved prediction accuracy demonstrated through extensive experiments on financial datasets. However, this study primarily relies on sentiment-based text processing, limiting the ability to fully leverage the nuanced information present in unstructured text.  
While existing approaches have demonstrated the value of incorporating textual data, they often simplify textual inputs to sentiment polarity or other low-dimensional representations, potentially overlooking critical information. This paper addresses these limitations by proposing a novel framework that directly integrates textual and time  
Figure 2: Comparison of temporal and variate token embedding approaches in vanilla transformer and iTransformer.  
series data using language models to extract high-dimensional features from text. Unlike sentiment-based methods, our approach comprehensively models the interactions between text and time series, capturing deeper insights into the factors influencing predictions.
## 1 Introduction
### Integrated Navigation Method for Interference Monitoring  
In 2011 [PERSON] and [PERSON] proposed an interference monitoring method based on GNSS and Inertial Navigation System (INS) [8], where GNSS is usually integrated with INS using an extended Kalman filter. The least squares estimated values of the potential variance jump are calculated by studying the impact on the covariance of the Kalman filter outputs induced by the increase of the GNSS noise. With these estimated values as the inputs of continuous Bayesian testing experiments, the presence of interference in the received GNSS signals can be determined according to the experimental results. This method requires the GNSS and INS to work collaboratively, and superior stability of the system as well. Nevertheless, the existence of interferences will inevitably affect the stability of receivers.