esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Understanding of TBM Performance
### Current Developments in Performance Prediction  
Precisely forecasting the performance of TBMs within specific geological contexts holds immense significance in the decision-making process for choosing tunnelling methods, establishing construction schedules, and estimating project costs. This is primarily due to the fact that TBMs are notably sensitive to geological variables and necessitate substantial initial investments. During the feasibility stage, it is imperative for the project owner to employ a predictive model for economic evaluation and method selection. As the construction phase commences, the project owner can continue to utilize this prediction model to assess the contractor's progress, while the contractor, in turn, depends on the model to make estimates for bid prices. Furthermore, the builder can conduct a comparative analysis between the predicted progress and the actual construction progress, thereby facilitating the identification of any potential issues. Nonetheless, the construction speed is subject to the influence of multiple factors (as depicted in Figure 5), and the intricate interplay and relationships among these factors make it a complex task to comprehensively establish the correlation between TBM performance and each specific factor from a purely theoretical standpoint. As stated by [PERSON] (2018), \"No task is more challenging than evaluating rock characteristics and applying them to predict TBM performance\". [PERSON] (2018) further notes that \"the current lack of a standardized method within the geotechnical engineering sector for the quantitative assessment of the influence of rock variations on TBM construction performance\". TBM performance prediction encompasses the estimation of various parameters, including the penetration rate (PR), advance rate (AR), utilization (U), and cutter wear (H). In the initial stages, early prediction models primarily concentrated on PR alone. However, in subsequent developments, multi-factor models were introduced, enabling a comprehensive prediction of all facets of TBM performance. Since the 1970s, numerous researchers have developed various types of TBM performance prediction models, evolving from simple to complex. As depicted in Figure 6, these models can be categorized into three main types, theoretical models, empirical models, and artificial intelligence models. Table 7 illustrates the advantages and disadvantages associated with each of these models. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters to Be Considered When Choosing an Excavation Method** \\ Project design factors & Costs budget \\ Ultimate purpose & Overbreak and tunnel profile quality \\ Excellent working environment & Surrounding rock conditions \\ Boring speed & Construction time and tunnel layout \\ Flexibility and acceptable risk & Project contract conditions \\ Geological situation & Protected buildings \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Important evaluation parameters for the selection of excavation methods. \begin{table}
# Sustainable Development of Concrete through Aggregates and Innovative Materials: A Review
## 4 Organic Aggregates
### Bamboo  
Bamboo is a natural resource that grows in tropical zones and can reach its maximum mechanical strength in few years.  
The adhesion between bamboo bars and concrete was studied by [PERSON] et al. [97] in cylinders with a compressive strength of 20 MPa. Bamboo bars of 10 mm square section were embedded in concrete cylinders and subsequently subjected to stress in a universal machine. The tensile strength and elastic modulus were measured according to the guidelines of ASTM D3039. The comparison is made between uncoated and epoxy coated with sand particles, as a result the latter showed better adhesion. On the other hand, the strength of the concrete reinforced with rice husk ash (RHA), sea shell ash (SSA) and bamboo fiber was comparable with that of normal concrete after 28 and 90 days [98]. Sikadur 32-gel can be used to improve the bonding between bamboo and concrete [99]. [PERSON] et al. [100] have shown that treated bamboo reinforcement can be as good as the conventional steel reinforcement in conventional concrete.  
Bamboo used as reinforcement in walls can enhance the shear capacity compared to unreinforced masonry and achieves a behavior similar to that of reinforced with steel [101]. The use of bamboo as a replacement of steel reinforcement has been evaluated in beams, slabs and columns. Results provide enough strength as the members reinforced with steel. For instance, [PERSON] and [PERSON] [102] tested different slabs in where steel reinforcement was completely replaced by bamboo bars as indicated in Figure 10. They demonstrated that using bamboo as mainly flexural reinforcement in slab was possible had its better performance compared with the same slab containing steel reinforcement [102].  
\begin{table}
\begin{tabular}{c c c} \hline
**Type of Treatment** & **Properties** & **Reference** \\ \hline \multirow{6}{*}{**Substitution of aggregate**} & Optimal results are obtained by replacing 20\% in aggregates. Decreases resistance to compression, flexion and tension. & \\  & The density of the concrete increases. It presents an increase in water absorption, abrasion and permeability. & \\  & The workability of concrete increases with increasing replacement of stone material. & \\  & It can be used in all engineering works applications. In proportions of 20\% to 40\%, it can be used in nonstructural elements such as pavements. & \\ \hline \multirow{2}{*}{**Corrosion resistance**} & Using the 5.0\% substitution increases corrosion cracking. & \\  & There are no changes in the chemical properties of the mixture. & \\  & The optimum percentage of substitution is 10\% for positive effects of tensile stress. & \\ \hline \multirow{2}{*}{**Mineral powders (basal, quartz, feldspar)**} & There is an increase or decrease in abrasion resistance. Depending on the percentage of substitution and the type of mineral. & \\  & The addition of 20\% quartz increases resistance to abrasion. & \\ \hline \end{tabular}
\end{table}
Table 7: _Cont._
# Compact Sub 6 GHz Dual Band Twelve-Element MIMO Antenna for 5G Metal-Rimmed Smartphone Applications
The proposed antenna system has an envelope correlation coefficient (ECC) of less than 0.38 and an isolation greater than 10 dB without any isolation components, and its efficiency is greater than 36%. This paper is structured in the following manner. We first present the design details of the MIMO antenna system in the following section. The design mechanism and parametric analyses are discussed in Section 3. Afterward, Section 4 presents a discussion of the radiation performance metrics of the antenna, such as its reflection coefficients, transmission coefficients, ECC, efficiency, and radiation pattern. The MIMO channel capacity are discussed in Section 5, and Section 6 presents an analysis of the characteristics of the MIMO antenna system. In Section 7, we present the conclusion.
# Advancements and Prospects of Electronic Nose in Various Applications: A Comprehensive Review
## 2 E-Nose System
### The Electronic Nose's Brain: Algorithms and Visualization
#### 2.4.2 Linear Discriminant Analysis  
LDA, a frequently employed discriminant technique, optimizes the variance ratio between classes and within classes by transforming original variables through linear combinations, ensuring effective separation of classes. Its primary goal is to project datasets onto lower dimensional spaces that enhance class distinguishability, mitigating overfitting concerns and reducing computational complexity. Serving as a method of linear transformation for dimensionality reduction, LDA shares similarities with PCA [84; 85].  
Going a step further, LDA presents itself as a robust tool with parallels to PCA in dimensionality reduction, visualization, and as a dependable classification approach. It undertakes a linear transformation from an N-dimensional space to a lower dimensional space (M \(<\) N), aiming to retain essential information and minimize noise. Unlike PCA, LDA specifically enhances inter-class variance and maximizes inter-class separation, optimizing distinction. This property facilitates dividing space into regions associated with specific classes, such as gases in our context.  
Distinguishing itself from PCA, LDA operates as a supervised method, employing a training dataset to construct a model that evaluates new data against the established model [86]. This enables LDA to serve as a classifier, comparing incoming data to the trained model and labeling the data based on proximity to certain groups (in our case, different gases). This method yields a direct classification label as output, eliminatingthe need for subjective interpretation and ensuring true classification. As with other supervised methods, it is essential to test models on independent datasets, distinct from the training data.
# VANET Secure Reputation Evaluation & Management Model Based on Double Layer Blockchain
## 2 Related Work
### Reputation Evaluation and Management
Malicious nodes can not only post fake trust ratings, but also falsify safety-critical information to deceive trusted vehicle trust. In ([PERSON] et al., 2018), [PERSON] et al. proposed to evaluate node trust and data trust to ensure the reliability of data exchange between entities and the authenticity of data transmitted by these entities. The final trust calculation was based on the weighted trust score of the vehicles' cooperation with partners. In ([PERSON] et al., 2018), [PERSON] et al. evaluated the authenticity and rationality of the local danger warnings based on the reputation of the vehicles. The proposed scheme designed a complete blockchain-based implementation scheme, with consensus using PoA (proof of authority) and provided simulation of the blockchain overhead. While meeting the requirements of effectiveness and latency, the proposed scheme can resist false injection attacks. This scheme uses an optimistic strategy, in which the nodes are initially considered to be trustworthy, so there are almost no defensive measures before committing any inappropriate behaviors. Therefore, the security of this scheme still needs to be considered in practical applications. In ([PERSON] et al., 2018), [PERSON] et al. designed a vehicle reputation evaluation system with reward and punishment mechanisms and conventional tax mechanisms, and implemented the system using Hyperledger Fabric. In this scheme, all tasks of updating reputation values are abstracted into three types of transactions, which have improved the performance in terms of effectiveness and throughput compared to existing solutions. At the same time, it effectively addressed the issues of on-off attacks and rational selfish behaviors. However, the above two schemes only evaluate the reputation of vehicle nodes through the trust computation and fusion between nodes, and evaluate the vehicle status based on preset thresholds, without considering the impact of historical reputation and social factors. The above related work is summarized in Table 1. We can see from the above summaries and Table 1 that there is not yet a complete secure reputation model covering trust calculation, reputation update, and management. In addition, as for reputation evaluation schemes, there is no solution that fully considers all reputation characteristics, such as historical reputation, interactive trust, and social factors. The DBREMM, proposed in this paper, implements a comprehensive evaluation and secure storage of event credibility and node credibility in trust calculation and reputation update and management. At the same time, improvements and innovations are made on the threshold and weight quantization selection, anti-attack capability, and other aspects of the existing trust computing schemes.
## 4 Local Transmission Control: Redundancy-Based Transmission  
The proposed local transmission control establishes the paths between hub nodes selected by the large-scale transmission control. The proposed local control uses synchronized redundancy as route-selection criteria.  
In Figure 1(a), node \(n_{i}\) and node \(n_{j}\) are mobility-synchronized if the following condition is met:  
\[|v_{j}-v_{i}|\leq\gamma\quad\text{and}\quad\arg(v_{j}-v_{i})\leq\theta, \tag{17}\]  
which means that \(n_{j}\) moves similarly to \(n_{i}\). In Figure 1(b), if link \(l(i,j,k)\) has a lifespan greater than \(l(i,k)\), link \(l(i,j,k)\) is defined to be link-synchronized to the link \(l(i,k)\):  
\[\min\{L(i,j),L(j,k)\}\geq L(i,k), \tag{18}\]  
where \(L(\cdot)\) means the lifespan of link \(l(\cdot)\).  
Figure 2: Synchronization.
## 4 Software-Bus Components
### Data Points  
Data is the crucial aspect of any IoT environment. Embedded devices are controlled to perform certain actions, depending on conditions, or sense their surroundings and provide this information. Collecting and transferring data is the basis for any activity. Examples are the turning off of a heater if a sensor discovers an open window or the collection of temperature and humidity. In the context of the SBT, data points contain a piece of data in connection with meta-information, such as unit, type and upper and lower bound. Data points are created by the SBT components and transmitted via communication protocols. This approach provides syntactic level interoperability, as any given data can be defined and integrated into devices.
## 2 Characterization of Marine and Continental Shale Reservoirs  
### Analysis of Shale Mineral Fractions  
In this study, the marine shale was sourced from outcrops of the Lower Silurian Long-maxi Formation in Sichuan Basin, China, with an average burial depth of 3000 m. It exhibits a dark coloration, high organic matter content, and a dense structure, contributing to its significant brittleness. On the other hand, the continental shale originated from outcrops of the Dongyue Temple Formation of the Middle Jurassic in the Sichuan Basin, China, with an average burial depth of 2840 m. Formed in a continental lake basin environment, it appears as deep gray to black-gray, featuring relatively lower organic matter content and a comparatively loose structure (Figure 2). The shale is distinguishable by its nail-indentation capability and exhibits clear bedding structures. Considering the sedimentary and diagenetic environments, the marine shale experiences relatively weaker hydrodynamic conditions, resulting in a higher strength of mineral particle bonding. In contrast, continental shale, influenced by stronger hydrodynamic conditions, primarily undergoes mechanical compaction, leading to a noticeable decrease in the strength of mineral particle bonding compared with marine shale.  
Mineral fractions of shale have an influence on its mechanical properties, seepage characteristics, and microscopic pore structure; our experiment used an Empyrean-type multifunctional high-resolution X-ray diffractometer to analyze the mineral fractions. The experimental results are shown below.  
Figure 1: Calculation of fractal dimension of fracture surface based on optical scanning technique.  
Through the analysis of the XRD test results of the marine shale rocks and the XRD test results of the continental shale rocks, it can be seen (as shown in Figure 3) that the mineral fractions of the marine and continental shale rocks have obvious differences, which are mainly related to the depositional environment. The quartz content of Longmaxi Formation marine shale is 59.55%, calcite content is 4.9%, dolomite content is 64.04%, pyrite content is 0.89%, and the total content of clay minerals is 5.87%. The sample of continental shale from Dongyuemiao Formation has 32.56% quartz, 2.12% sodium feldspar, 13.12% calcite, 4.50% pyrite, 4.50% clay, and a total clay content of 47.70%.  
Overall, the clay mineral content of continental shale is higher and the brittle mineral content is relatively low, while the brittle minerals such as quartz are absolutely dominant in marine shale.
## 2 Materials and Methods
### Collection and Determination of Volatile Compounds  
The analysis of volatile compounds was conducted employing HS-SPME-GC/MS, adhering to the method described by [PERSON] et al. [22] with slight modifications.  
Volatile compounds in crabapple cultivar samples were extracted using headspace solid-phase microextraction (HS-SPME) with a divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) fiber assembly (Agilent, Santa Clara, CA, USA), followed by analysis with GC-MS. The analyses were carried out using an Agilent 7890B gas chromatograph equipped with a 5977A mass selective detector, a CTC PAL autosampler, and an HP-Innowax column (30 m \(\times\) 0.250 mm I.D., 0.5 \(\upmu\)m film thickness). The oven temperature program was set as follows: 50 \({}^{\circ}\)C for 2 min, then increased at 3 \({}^{\circ}\)C/min to 220 \({}^{\circ}\)C, followed by 8 \({}^{\circ}\)C/min to a final temperature of 235 \({}^{\circ}\)C. Helium was used as the carrier gas at a flow rate of 1 mL/min.  
Sample preparation was performed as follows. A total of 0.5 g of fresh flower sample was placed into a 20 mL headspace vial, followed by the addition of 2 g of sodium chloride and 5 mL of 10% (\(v/v\)) anhydrous ethanol. Then, 5 \(\upmu\)L of 2000 ppm 4-methyl-2-pentanol solution was added as the internal standard, resulting in a final concentration of 2 mg/L in the vial. In this study, 4-methyl-2-pentanol was selected as the internal standard for GC-MS analysis. This compound exhibits good chemical and thermal stability, moderate volatility, and stable mass spectrometric response. Preliminary tests confirmed that it was not detected in the volatile components of crabapple flowers, and its retention time did not overlap with the main aroma compounds. Therefore, it was considered suitable as an internal standard for this analysis.  
Volatile compounds were identified by matching mass spectra with the NIST14.0 library and comparing retention indices (RI). Semiquantitative analysis was conducted using 4-methyl-2-pentanol as the internal standard.  
To eliminate potential background VOCs originating from solvents or instrumentation, a blank control was included in the analysis. The blank consisted of 2 g of sodium chloride and 5 mL of 10% (\(v/v\)) anhydrous ethanol, with 5 \(\upmu\)L of 2000 ppm 4-methyl-2-pentanol added as internal standard. The same HS-SPME-GC-MS procedure was applied to the blank to subtract any interfering signals from the final dataset.  
The odor descriptions of the volatile compounds were based on references [23; 24]. The odor thresholds in water primarily referenced Van Gemert and the online database ([[http://www.vcf-online.nl/VcfHome.cfm](http://www.vcf-online.nl/VcfHome.cfm)]([http://www.vcf-online.nl/VcfHome.cfm](http://www.vcf-online.nl/VcfHome.cfm)), accessed on 20 August 2024). OAVs were calculated as follows: OAVs = concentration of each compound (mg/L) divided by the odor threshold for that compound in water (mg/L).
# An Information-Theoretic Approach for Detecting Community Structure Based on Network Representation
## 4 Experiments
### Benchmarks  
To verify the accuracy of the algorithm, we considered both the number of communities (CN) and normalized mutual information (NMI).  
NMI evaluates the difference between two partitions on the basis of information theory; therefore, it can be used as an index aiming at evaluating the accuracy of community detection, which is introduced in [35].  
The formula of NMI is shown as follows:  
\[\text{NMI}= \frac{-2\sum_{i=1}^{C_{A}}\sum_{j=1}^{C_{B}}C_{ij}\cdot\log\left( \frac{C_{ij}\cdot N}{C_{i}\cdot C_{j}}\right)}{\sum_{i=1}^{C_{A}}C_{i}\cdot\log \left(\frac{C_{ij}}{N}\right)+\sum_{j=1}^{C_{B}}C_{j}\cdot\log\left(\frac{C_{j }}{N}\right)} \tag{8}\]  
where \(C_{A}\) and \(C_{B}\) are the number of communities of partitions \(A\) and \(B\), respectively. \(C\) is a confusion matrix that indicates the number of nodes that belong to a pair of communities at the same time. Specifically, \(C_{ij}\) denotes the number of nodes simultaneously belonging to community \(i\) in partition \(A\) and community \(j\) in partition \(B\). \(C_{i\cdot}\) and \(C_{j}\) denote the sum of all elements of a row and a column, respectively, in matrix \(C\). \(C_{A}\) and \(C_{B}\) denote the number of communities of partitions \(A\) and \(B\), respectively. NMI measures the similarity of two community partitions; it is equal to 1 if the two partitions are identical, and it is close to 0 if they are quite dissimilar.
# Network Allocation Vector (NAV) Optimization for Underwater Handshaking-Based Protocols
## 4 Performance Analysis  
In this section, we evaluate the proposed NAV optimization scheme, comparing it to NAV setting methods described in [24; 25] in terms of total NAV time, throughput, latency, and fairness. As mentioned earlier, both [24; 25] are based on the CSMA/CA protocol with four-way handshaking mechanism and BEB (binary exponential back-off) algorithm [26], but differ in the method of NAV setting.
# Travel Time Prediction on Long-Distance Road Segments in Thailand
## 3 Materials and Methods
### Our Approach  
In the first experiment explained in Section 3.4, the LSTM showed the best performance. The MAE was 27.12 min per 100 km, as shown in Section 4.1. This meant that we could use the original LSTM to predict travel time with an error of about 27.12 min for every 100 km. Thus, the LSTM was selected as a baseline, and our challenge was to improve the LSTM, increasing the performance by minimizing the MAE. After obtaining the raw data, we used a filter to process the data in order for it to be an input of the LSTM with attention, as shown in Figure 5.