esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of the Development and Future Challenges of Case-Based Reasoning
## 4 Application Fields of CBR
### Recommendation System  
E-commerce is similar to any other form of business. Users have needs, but often the needs can only be met to a certain extent, and users can only specify the needs incompletely or inaccurately. For users, it is difficult to analyze massive volumes of information and filter out the information they need; therefore, CBR plays a key role in the development of content-based or case-based recommendation systems, such as [119; 120]. It includes the user's personal preferences and needs, as well as product-related information. Therefore, CBR can provide products and information that users are interested in or need according to user needs or preferences.  
[PERSON] et al. [58] proposed a dynamic review recommender 'DiversityBite' based on the CBR framework, which combines reviews and diversity to help users obtain diversified recipes and meal plan recommendations. [PERSON] and [PERSON] [121] proposed a personalized recommendation method using product reviews as recommendation knowledge. It extracts cluster features from reviews to form cases through triplet features and feature clustering steps. In the recommendation process, it calculates the recommendation score through the linear combination of emotional features and similarity, which is used for query-based and user-based recommendation scenarios. [PERSON] et al. [122] argued that, since users only interact with items of interest in the recommendation system, that system must retain very large amounts of personalized information and item sparsity, which seriously affects the performance of the recommendation system; therefore, they proposed a CBR-recommender method to reduce data sparsity through data classification and dynamic clustering, which makes the system run faster in large-scale recommendation research that dynamically calculates user preferences.
## 1 Introduction  
### Liner's Role in the Pressure Storage of Hydrogen  
The acquisition of electric energy from fuel cells combined with hydrogen vessels is an alternative solution to the use of lithium-ion batteries and, simultaneously, one of the key stages in the technological development of energy supply for equipment, mobile devices, and stationary objects. Hydrogen, being an energy carrier used in the power supply to fuel cells, can be used in three states of matter: gas, liquid, or a chemically bonded phase in solids and liquids. Its storage form depends on the state of matter, the most frequently used solution being the storage of compressed gas.  
In pressure storage systems (Figure 1), from 35 to 70 MPa, the most technologically advanced and safest solutions are applied, thus they have a well-deserved high reputation resulting from their use in transport, mainly over land and water, in stationary systems used for energy generation and hydrogen storage, including systems cooperating with renewable energy sources (RES). This is because high-pressure systems, and especially composite vessels, are highly efficient, i.e., the values of parameters describing the amount of stored hydrogen or energy in relation to the mass or volume of a storage system (expressed as system gravimetric capacity or system volumetric capacity, respectively). Moreover, despite the use of high working pressures, they are characterized by a high exploitation safety owing to the fact that they meet the strict requirements specified in binding regulations. In comparison with other hydrogen storage methods, e.g., various chemical absorption methods and liquid hydrogen at cryogenic temperatures, they are also competitively priced.  
Composite vessels used for compressed gas storage are composed of two layers (Figure 1). The internal layer (the so-called liner) is responsible for gas accumulation and shaping the whole construction. It must form a barrier that is sufficiently strong to prevent gas particles from permeating outside the container. The liner can be made of metal (suitable steel type or aluminum alloys) or plastics (polymers). Currently, the latter solution tends to dominate. This results mainly from the following requirements, which modern liners must meet:  
* The smallest possible mass of a hydrogen storage system, which can only be ensured by using completely composite containers (type 4) in which a liner is made of a polymer and a load-carrying shell is made of woven carbon fiber;
* The smallest hydrogen permeability, thanks to the use of high-density materials and additional sealing with, e.g., sol-gel type of materials [1];
* Ease of formation and geometry modification, due to the use of polymer technology solutions, which have been known for many years, such as rotomolding, blow molding, or extrusion;
* Low reactivity to hydrogen, i.e., small influence of stored gas on material properties (e.g., elimination of the hydrogen corrosion problem that occurs in the case of metal liners).
## 3 Results and Discussion
### In-Sample Test  
Table 4 illustrates the in-sample test performance scores of different models/classifiers on various datasets. Overall, the performance of the proposed deep neural network was found to be the best of all the considered datasets. The performance of random forest was observed to be better than SVM and LR, and inferior to the proposed deep neural network model. It is interesting to note that logistic regression performed better than support vector machine. Figure 3 represents the in-sample testing accuracy of all approaches on targeted datasets. The accuracy of the proposed DNN model was 7%, 14%, and 9.8% better than the average accuracy of the other benchmark algorithms on the Hindukush, Chilean, and Californian datasets, respectively. In terms of precision, the average improvement of the proposed DNN model was 20%, whereas the corresponding figures for recall and F1-score were 29% and 36%, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Hindukush**} & \multicolumn{6}{c}{**Chile**} & \multicolumn{6}{c}{**California**} \\ \hline
**Approach** & **DNN** & **RF** & **SVM** & **LR** & **DNN** & **RF** & **SVM** & **LR** & **DNN** & **RF** & **SVM** & **LR** \\ \hline Accuracy & 95.8 & 95.13 & 77.9 & 92.9 & 98.28 & 97.2 & 73.8 & 85.7 & 99.29 & 98.7 & 84.8 & 87.7 \\ Precision & 93.15 & 86.03 & 88.09 & 95.86 & 98.69 & 86.05 & 74.4 & 82.15 & 99.26 & 97.52 & 92.39 & 75.01 \\ Recall & 85.56 & 80.9 & 66.35 & 92.46 & 98.68 & 78.34 & 50.46 & 79.47 & 95.87 & 88.24 & 50.07 & 66.95 \\ F1-Score & 99.22 & 82.78 & 67.89 & 90.28 & 98.68 & 81.06 & 43.47 & 80.64 & 97.62 & 92.14 & 46.4 & 69.75 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Overall In-Sample Results of All Approaches from the Considered Datasets.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Formula** \\ \hline Accuracy & \(\frac{TP+TN}{TP+TN+FP+FN}\) \\ Recall & \(\frac{TP}{TP+FN}\) \\ Precision & \(\frac{TP}{TP+FP}\) \\ F1-Score & 2 \(\frac{Precision-Recall}{Precision+Recall}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance metrics.  
Overall, it can be argued that the proposed technique is more capable of identifying the patterns in the time series data for seismic activity detection than the benchmark techniques. This can be attributed to the complex structure of the proposed deep neural network, and to the working mechanism of the deep neural network. Furthermore, the addition of drop-out was potentially helpful, by reducing the over-fitting.
## 4 Corrosion Protection Measures
### Anticrovise Coating  
Reducing anti-corrosion by coating is one of the main anti-corrosion measures. From ancient animal and plant oils and asphalt to various metals and metal compounds, resin materials, and various other anti-corrosion materials, corrosion-resistant coatings can provide a physical barrier to protect the pipeline itself. Additionally, some coatings contain inhibitors that are released when corrosive media penetrate, helping to reduce corrosion of the pipeline material. Furthermore, certain coatings protect the pipeline material through cathodic protection via sacrificial anodes. Corresponding coating methods are also emerging, including spraying, flow coating, electroplating, and electrophoresis. The coating can also be melted on the protected surface by explosive methods. As shown in Table 2, there are three types of anticorrosive coatings: organic anticorrosive coatings, inorganic anticorrosive coatings, and metal anticorrosive coatings.
## 5 Conclusions  
In this paper, we presented an online deep learning framework, MFACNet, for multi-object tracking with video satellites. The framework enhances the feature representation of the current frame by inheriting multi-frame deep features. It also constructs inter-frame feature mapping by calculating the correlation volume between the current frame and the preceding one, enabling the prediction of object displacements. Finally, a novel matching algorithm based on object motion and appearance features was employed to complete the trajectory construction. Qualitative and quantitative experiments demonstrated that our JDE algorithm, MFACNet, effectively reduces false positives (FPs) in detection, while efficiently utilizing motion and appearance information to construct and manage object trajectories, leading to a great reduction in identity switches (IDSWs). Improvements were observed in both the detection and tracking performance. Moreover, comparative experiments showed that our tracking framework achieved superior performance compared to popular multi-object tracking models.  
For the MFACNet, our improvement came at the cost of slightly increased FNs, indicating the requirement to explore more effective ways of utilizing features. Additionally, during the training of the MFACNet, we observed a certain degree of overfitting in the tracking vehicle targets, where only vehicles with prominent features obtained satisfactory tracking results. This necessitates a reevaluation of our training strategy and training data. Lastly, current MOT methods are tailored for finely processed video sequence products and have not been formally deployed on video satellites. Normally, video sequences require the excessive occupation of satellite communication bandwidth and complex post-processing procedures. To provide real-time information for efficient MOT, we are still investigating the feasibility of deploying the MFACNet on video satellite platforms.  
Conceptualization, H.Z., Z.W. and Q.Z.; Methodology, H.Z., Y.S. and Q.Z.; Dataset Construction, H.Z. and Y.S.; Validation, H.Z.; Writing the original draft, H.Z. and Q.Z.; Formal analysis, Q.Z.; Writing, review, editing, and supervision H.Z., Z.W. and Q.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (No. 2022 YFE0209300) and the Shenzhen Science and Technology Program (No. ZDSYS20210623091808026).  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 3 Results and Discussion  
### Analysis of Temporal and Spatial Variation and Correlation of Tourism Carbon Emissions  
#### 3.1.1 Temporal and Spatial Variation Analysis of Tourism Carbon Emissions  
By estimating the carbon emissions of the tourism industry in 31 provinces of China from 2019 to 2020 (Figure 1), it was observed that the tourism industry's carbon emissions decreased by 207.04 million tons in 2020, a reduction of 74.71% due to the impact of the COVID-19 pandemic. In terms of transportation, accommodation, and destinations, carbon emissions in 2020 decreased by 46.03%, 35.18%, and 94.95%, respectively, compared to 2019. Among them, the carbon emissions from tourism destinations experienced the most significant decline. Reduced desire for travel and limited travel range and duration resulted from the impact of the COVID-19 pandemic.  
In terms of the total carbon emissions of each province and municipality, in 2020, Beijing, Shanghai, Guangdong, and Sichuan had tourism carbon emissions exceeding 5 million tons. Among them, Guangdong had the highest tourism carbon emissions in 2020, reaching 11.53 million tons. The considerable population mobility and abundant tourist attractions in Beijing, Shanghai, and Guangzhou placed them at the forefront of tourism carbon emissions during the pandemic. Heilongjiang, Jiangsu, Hunan, Hainan, Chongqing, and Fujian, six provinces and municipalities, had tourism carbon emissions exceeding 2 million tons in 2020. These provinces have relatively developed tourism industries, especially Hainan and Chongqing, which are renowned tourist destinations. Compared to more developed provinces, they experienced a milder impact from the COVID-19 pandemic, and their tourism industries were relatively well established compared to cities with lighter outbreaks.  
The tourism carbon emissions of the remaining provinces in 2020 were all below 2 million tons. Due to variations in the severity of the pandemic, differences in tourism industry development, geographic location, economic development level, and epidemic prevention and control policies were observed. Among them, Shanxi was the most affected by the pandemic, with the most significant reduction in tourism carbon emissions. Shanghai was the least affected, with the smallest decrease in tourism carbon emissions.  
Figure 1: Carbon emissions of tourism in 31 provinces (cities) of China from 2019 to 2020.
## 2 Theoretical Background
### Smart Gates
#### 2.1.2 Information Systems Applied in Smart Gates  
There are at least two information systems referenced in the literature that aim to improve gate operations: the gate appointment system and the automated gate system.  
Gate operational management can be improved by a gate appointment system (GAS) (or vehicle booking system, or terminal appointment system), which is an information system (IS) that can be used both to facilitate the operations of the terminal operator and the drayage company operators. The goal of this IS (on the drayage company operator's side) is to ease the schedule of the arrival of truckers at the terminal and smooth the flow of cargo, which consequently avoids peak hours, reduces waiting times for the trucks, and reduces vehicle emissions. On the terminal operator's side, it can facilitate the management of the terminal operations according to the appointments during each time interval. It should also be pointed out that the information on the arrival of the trucks should be updated in real-time to improve the efficiency of the operations, for example, in cases of delays due to unforeseeable external factors. Therefore, this system must allow information sharing and communication between both parties [13; 21; 22; 42; 46; 47].  
The automated gate system (AGS) is a technological solution that automates and optimizes gate operations and represents another IS for terminal operations. Marine terminal gates manage inbound and outbound cargo flows, and accurate recording and verification of container and vehicle movements is crucial for efficient operations. Gate information systems integrated with the terminal's TOS (terminal operating system) play a vital role in ensuring accurate data management. The procedures involve checking containers for damage, cargo classifications, and truck driver authorizations. Technologies such as OCR and RFID are used for the automatic identification and verification of vehicle, driver, and container data [13; 22; 48].  
These technologies work together to create an efficient and secure smart gate, streamlining entry and exit processes at seaports and improving the management of traffic flow and vehicle and cargo records [13; 22; 39]. [PERSON] & Vo8 [22] offer a detailed analysis of the information systems used in a smart port, including the TOS and the PCS (port community system), among others.
## 2 GPS SDR Architecture for CRPA Processing and Computational Challenges
The 150-channel, 5 Msps GPS SDR in [34] can simultaneously run roughly less than 20 GPS L1 SDRs with 12 channels each, but it is not enough for the CRPA processing which requires the computational cost of about 70 GPS L1 SDRs [Equation (5)]. However, these GPUs have compute capability 1.x (Table 1) and these SDR performances could be enhanced by using new generation GPUs of compute capability 2.x. Although backward compatibility is guaranteed, performance enhancement by a new generation GPU is limited if unmodified source codes designed for a previous generation GPU is used, because the new generation GPUs of compute capability 2.x have a very different architecture called Fermi architecture [40]. In order to take full advantage of Fermi architecture, we specifically designed our GPU-based GPS SDR for CRPA in a way to maximize the utility of new resources from the new GPU architecture. The GPU of compute capability 2.0 used in this work is NVIDIA GeForce GTX 480 which was released in March 2010. Although a GPU can provide more computational throughput than a CPU, certain tasks may not be readily parallelizable for GPU processing. More important, it is not desirable to idle a CPU while a GPU performs its tasks. Thus, a wise use of CPU in parallel with GPU can always improve the SDR performance. In our design, the computational burden is shared by multi-threading on a quad-core CPU. In this way, a multi-core CPU provides thread-level parallelism in addition to the hardware parallelism as a different hardware from GPU. The novel GPS SDR implementation by parallel processing on CPU and GPU is detailed in the next section.
## 4 Results
### Results and Comparison  
We mainly validate the performance of our network with other different road extraction methods through the three challenging datasets mentioned above. These datasets are optical remote sensing imagery datasets. To demonstrate the advantages of the DPBFN, we compare our method with some classic methods selected from CNN-based, transformer-based, and hybrid based. To facilitate visual interpretation and the analysis of extraction outcomes yielded by diverse algorithms, accurately classified road segments are represented in white, while accurately classified non-road pixels are depicted in black. In cases of erroneous extractions and misclassified pixels, these specific segments will be emphasized by using red and blue shades, as illustrated in Figure 5.
# Minimization of Material Waste Through Maintenance Interval Optimization in Transport Systems
## 4 Results
### Comparison of the Effectiveness of the Presented Algorithm with Standard Servicing  
Overall, optimizing the maintenance process brings numerous economic and operational benefits for the operator, improving their market competitiveness and customer satisfaction.  
For the normal DSU process, the total maintenance time was 1975 days, of which:  
* P2 service: 366 days (18.5%);
* P3 service: 49 days (2.5%);
* P4 service: 1200 days (60.8%);
* P5 service: 360 days (18.2%).  
After optimization, the total maintenance time was reduced to 1650 days:  
* P2 service: 131 days (7.9%);
* P3 service: 11 days (0.7%);
* P4 service: 1158 days (70.2%);
* P5 service: 350 days (21.2%).  
The optimization resulted in a reduction in total maintenance time by 16.4%, which can significantly improve vehicle availability.  
Based on the analysis of the presented chart (Figure 10), there are clear benefits from optimizing the servicing process for the rail vehicle. Compared to the normal DSU process, optimization reduced servicing time by 325 days, representing a 16.4% reduction.  
Significant time savings were seen in P2, P3, and P4 services. Specifically, P2 was reduced from 366 to 131 days (a 64% reduction), and P3 from 49 to 11 days (a 77.6% reduction). Although P4 remains the dominant element of the total servicing time (around 70.2%), its duration was also successfully reduced.  
The benefits resulting from optimizing the servicing process can be highly significant for a railway operator. Shortening the servicing time translates into:  
* Increased fleet availability--shorter downtime periods mean more days when the vehicle can be used for passenger or cargo transport, directly improving efficiency and revenue;
* Reduced operational costs--shortened maintenance periods reduce direct and indirect costs, such as hiring replacement vehicles or lost revenue from downtime;
* Work schedule optimization--shorter service time allows for better planning and resource use, including maintenance personnel and parts availability
* Improved service quality--increased fleet availability facilitates better scheduling and reduced delays due to vehicle unavailability, enhancing passenger satisfaction.  
Figure 10: Comparison of results.
## 2 System Overview
### Communication Interface  
In this paper, the authors used the RS485 Modbus RTU protocol for communication interface using RJ-116p4c cable. RS485 to USB converter was used to convert data from the sensor using RS485 Modbus RTU protocol to USB for the computer to read and process the data from the sensor. The RS485 protocol data rate can reach 35 Mbit/s over a 10 m connection and 100 Kbit/s over a 1200 m line [14]. The RS485 Modbus RTU protocol has a number of benefits, including reliable communication, interoperability across devicesfrom different manufacturers, and ease of installation and configuration, making it ideal for edge computing [15].  
The general architecture used in this study contained hardware, an IoT platform, and an artificial intelligence (AI) platform, as shown in Figure 1. The industrial-grade sensor collected temperature, humidity, PM0.3, PM0.5, PM1, PM2.5, PM5, and PM10 during a three-day period from 24 September to 26 September 2021. The Mobius, an open IoT platform, acts as the gateway, server, and database. This open IoT platform works on an oneM2M technology standard, which is elaborated in the next section.  
Jupyter Notebook, based on python programming, was used as the AI platform. The Jupyter Notebook contains a set of open standards for collaborative computing. These open standards can be used by third-party developers to construct bespoke applications with embedded interactive computing based on HTML and CSS on cloud computing. Jupyter Notebook spans through visualization, multimedia, and more with its modular design. In addition to running the code, it saves the code and output as well as markdown notes in an editable document called a notebook. When users save a page in their browser, it is transferred to their notebook server, which saves it as a JSON file with the a.ipynb extension on the disk [16].