esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Grain-Flour Classification  
The classification of the spectral data of multiple grain flours was performed using linear and non-linear multivariate analysis methods and preprocessing methods. Table 2 shows the results of the four classification models and six preprocessing methods, including no-preprocessing (NoP) based on SWIR. The accuracy of the proposed classification model over all previous models was 71-97%; the LDA with normalization proved to be the best combination for classifying the eight grain flours with an accuracy of 97.43% and kappa coefficient of 0.97. The LDA showed an overall accuracy of 94% over all the prediction models used in the comparison, whereas PLS-DA showed 87% accuracy. Among the classification models, PLS-DA with D1 showed 96.4% accuracy, SVM with NoP showed 96.28% accuracy, and CART with D1 showed 95.87% accuracy. Tables 3 and 4 present the accuracy of the two multimodal HSI data according to the four classification methods. Each accuracy result presents VNIR (Table 3), and fluorescence (Table 4) data. In the case of VNIR, the use of SVM with D2 achieved the most efficient accuracy and kappa coefficient of 92.37% and 0.91, respectively. In Table 4, LDA with data normalization showed the best accuracy (A = 92.28%, K = 0.91), whereas, PLSDA with normalization showed higher accuracy (A = 93.22%) and lower kappa (K = 0.86) than LDA. Although, PLSDA showed higher accuracy than LDA, as shown Table 3, PLSDA could not classify MF1 (A = 0.09). Note that in the classification result of the eight grain flows, the result of classification between MF1 and MF2, which are the same variety, showed different particle sizes of the rice flours. The VNIR and fluorescence imaging achieved average results of 71.53% and 75.54% based on SVM with D2, respectively. In the case of SWIR, LDA with normalization achieved an average accuracy of 91.73%. Furthermore, discriminant analysis was performed for samples MF1 and MF2, and the use of LDA with normalization achieved an accuracy of 94.18% along with 0.88 kappa coefficient. According to the result of classification using the multivariate analysis method, SWIR (1000-1700 nm) showed the potential to detect and classify different particle sizes for the same flour type compared to other multivariate data-analysis methods [29]. Figure 7 shows the classification result of the three multimode HSI data based on LDA with normalization. VNIR (Figure 7a) shows 87% and 8.7% of the classification potential of LD1 and LD2, respectively, while fluorescence imaging shows the potential of 62% and 25% in 2D space (Figure 7b) and 3D space (Figure 7d), respectively. SWIR using LDA with normalization shows that LD1 and LD2 depict interpretation potentials of 63% and 20%, respectively (Figure 7c). In the figure, on the bottom right, the gray asterisk represents the data of mungbean starch (MS), which makes difficult to separate all rice flour samples (WF \(\bigcirc\), MF1 \(\bigtriangleup\), MF2 +, SF \(\times\)) in the 2D space.
## 3 Design of the Proposed Solution
### Route Path Selection with Link Quality and Critical Switch Aware  
RPSO is responsible for routing a flow from source to desire destination based on two conditions: Arrival of new flows and change in network state. This way, upon arrival of new flows Algorithm 1 is called to return the \(k\) set of candidate paths (Algorithm 3 lines 1-4). Afterward, the set of \(P_{i}\) that made it to the list is forwarded to the critical switch detection phase. For each \(P_{i}\) in P call Algorithm 2 and get the \(P_{i}\) with \(min\) (\(\text{CWF}_{\text{score}}\)) (Algorithm 3 lines 5-7). If \(P_{i}\) CWF\({}_{\text{score}}\) is equal to P\({}_{\text{low}}\), the set of corresponding rules are installed using the command \(\text{F}_{\text{install}}\), and flow routing will commence immediately (Algorithm 3 lines 8-12). However, when there is more than one \(P_{i}\) with equal CWF\({}_{\text{score}}\) the \(P_{i}\) with minimum distance (number of hop count) is considered as the final P. Similarly, the associated rules are installed \(\text{F}_{\text{install}}\) (Algorithm 3 line 13-16). However, when the network operation state changes because of dense flow arrival or timeout. To avoid frequent expiration of flow rules, an advance timeout setting is can be found at [35]. The affected path is obtained, and the set of old rules are removed and replaced with new rules. This way, data forwarding continues as demonstrated in Algorithm 3 (lines 17-22).  
```
1 If new flow f arrived, then
2 Call algorithm 1
3 Get set of k {P}
4 End
5 For each P: in P
6 Call algorithm 2
7 Get \(min\) (Pi CWF\({}_{\text{score}}\))
8 If Pi =P\({}_{\text{low}}\)then
9F\({}_{\text{install}}\)
10 Route \(f\)
11 End
12 End
13 If Pi+T & Pi >1, then
14 Select \(min\) (P\({}_{\text{distance}}\))
15F\({}_{\text{install}}\)
16 Route \(f\)
17 end
18 If network state change, then
19 Get Pi with expire rules
20 For each Pi
21F\({}_{\text{removed}}\)R: R e old rules
22F\({}_{\text{install}}\) new R
23 End
24 end
```  
**Algorithm 3**RPSO
# An Advanced AFWMF Model for Identifying High Random-Valued Impulse Noise for Image Processing
## 4 Experiment Results and Discussions
### Stop Iteration Comparison
Afterwards, it is also necessary to compare the PSRNR algorithm with the original ROR algorithm in different parameters to its noise density in Table 2. In fact, the noise density and the original image are not known when the noise is filtered out, and it is not possible for the iteration number to refer to Table 2 from the original ROR method. Therefore, for the experimental fairness of the testing results, the implementing process was tested under the same ROR architecture and the same noise density as that in Table 2. That is, the noise density is also selected in the same low, medium, and high noise levels (30%, 50%, and 70%) for performance comparison. The experimental results are shown in Tables 12-16 for the five images. It is found that in most cases (about 10/15 = 0.67), the empirical result can be very close to the repair effect found in Table 2, where the proposed PSRNR method is used as the stop condition. Thus, the proposed AFWMF model still conclusively has the best result out of all the filter techniques used in this study. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Noise Density** & **30\%** & **50\%** & **70\%** \\ \hline \(m_{c}=3,m_{f}=1\) & **29.00** & **27.31** & 24.14 \\ \(m_{c}=6,m_{f}=4\) & 28.62 & 27.23 & 23.32 \\ \(m_{c}=9,m_{f}=7\) & 28.56 & 26.98 & **24.31** \\ \(m_{c}=1,m_{f}=1\) & 28.18 & 26.49 & 22.53 \\ \(m_{c}=12,m_{f}=12\) & 26.51 & 26.45 & 24.00 \\ PSRNR (AFWMF) & 28.53 & 27.20 & **24.31** \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The average value of PSNR for setting parameters using the PSRNR formula.
# Study on Rock Damage Mechanism for Lateral Blasting under High In Situ Stresses
The results showed that the direction of crack growth was largely controlled by the hoop tensile stress and biaxial pre-pressure ratio. As a research tool, the numerical modeling method has been widely used to investigate the blast-induced damage characteristics of rock. [PERSON] et al. [12] used the discrete element method (DEM) to study the blast-induced radial fractures under confining pressure and found that the radial fractures induced by blasting tend to grow in the direction of maximum principal stress. [PERSON] et al. [13] investigated the blast-induced damage characteristics under different in situ stresses via a 3D FLAC (Fast Lagrangian Analysis of Continua) analysis. The results indicated that the development of fractures around the borehole is governed by the maximum principal stress and it is more obvious with the increase of the difference between the two principal stresses. [PERSON] [14] used LS-DYNA to study the damage characteristics in cutting blasting under different in situ stresses. The results showed that with the increase of in situ stress, the damage zone becomes smaller. With the increase of the lateral pressure coefficient, the extending direction of the tensile damage zone becomes more obvious, which causes a great challenge to the cutting blasting excavation in deep rock masses. [PERSON], [PERSON], [PERSON] and [PERSON] [15; 16; 17; 18] used LS-DYNA to investigate the influence of in situ stress on the blast-induced cracks. Their results showed that the crack propagation trends towards the direction of maximum compressive pressure. [PERSON], [PERSON] and [PERSON] [19] used a numerical model to study the contour control blasting under different in situ stresses. The result indicated that the in situ stress could affect the crack evolution and direction, and the quality of the contour surface is hard to control in highly stressed rock masses. The studies mentioned above mainly focus on the plane problems of blasting under static load. However, the three-dimension propagation of stress wave induced by explosives, the charge length, and the detonation velocity of explosive cannot be considered in these 2D plane strain models. The above factors can be involved in a 3D model analysis to obtain more realistic results. Additionally, the key factor of the free surface is rarely considered, especially the lateral free surface, which plays an important role in the rock breaking by blasting. In this study, a 3D blasting model of coupling static and dynamic loads is developed in LS-DYNA and the model parameters are calibrated by the physical model test. Subsequently, the calibrated numerical model is used to simulate the blast-induced damage considering the roles of in situ stress and lateral free surface. Based on the damage distribution, the blast-induced damage characteristics and the explosion craters under different static loads and free surface spans are analyzed.
# Hollow-Core Optical Fibers for Telecommunications and Data Transmission
## 9 Manufacturing of Hollow-Core Fibers
### Extrusion of Preform  
Most soft glasses can be extruded at temperatures below 400 \({}^{\circ}\)C. Certain pairs of soft glass and heat-resistant polymers, like PEI or fluorinated ethylene-propylene (FEP), have similar melt temperatures and can be co-extruded to make a preform, later drawn into a fiber with protective coating in a single operation. Similar materials were used in the past to make Bragg fibers--see Section 3. ARF fibers from soft tellurite glass (70%ToO\({}_{2}\)-13%ZnO-10%BaO-7%K\({}_{2}\)O) for power delivery at mid-infrared wavelengths, exhibiting a loss of 4.8 dB/m at 5.5 \(\upmu\)m, were reported [96]. In this case, the preform with FEP coating was extruded at 340 \({}^{\circ}\)C.  
The extrusion of a fused silica preform would require a temperature in the order of 1500 \({}^{\circ}\)C. Basalt fibers, used in tensile strength members of fiber optic cables, are successfully fabricated via the extrusion of molten basalt rock through platinum-rhodium bushings at similar temperatures: 1400-1600 \({}^{\circ}\)C [97]; therefore, the adoption of this method for making preforms of NANF fibers appears to be possible. Dopants decreasing fused silica viscosity, like titania (TiO\({}_{2}\)), can reduce the required extrusion temperature.  
The extrusion process for honeycomb-structured ceramic and glass products was developed and patented by Corning Glass in 1974 [98], but it is used in the production of ceramic inserts for automobile catalytic converters, not optical fibers.
## 3 Background
### Intrusion Detection System
#### 3.1.2 Cloud-Based IDS Methods  
IDS in the cloud can be classified into two main methods, with a third possibility available, as follows:  
1. Signature-based detection: This approach is indicated as misuse- or rule-based. It is based on documented signatures of previous attacks and aims to identify applicable intrusions by analyzing data in opposition to these predefined guidelines. Data compliance with the predefined guidelines could be identified as attacks and invoke an alarm. This system is not capable of detecting unknown attacks [17].
2. Anomaly-based detection: This system requires modern network users to be compared with a baseline of historically learned ordinary community behavior. The baseline is built using observations of the community and hosts for a prolonged period of profile creation. Any enormous perversion from this baseline is identified as an anomaly and invokes an alert. This system is capable of detecting unknown attacks [17].
3. Specification-based detection: This system is a hybrid of signature and anomaly, using the signature approach to detect known attacks and the anomaly approach to detect unknown attacks [18].
# Temperature Distribution Characteristics of Concrete during Fire Occurrence in a Tunnel
## 2 Characteristics of Fire in a Tunnel  
### Material Characteristics of Concrete Exposed to High Temperature  
Exposure to high temperatures results in spalling or destruction of the coating on members due to the water vapor pressure created inside the concrete [2; 6; 7; 8]. At 100-400 \({}^{\circ}\)C, Al\({}_{2}\)O\({}_{3}\)-, Fe\({}_{2}\)O\({}_{3}\)- and tobermorite-based hydrates are dehydrated, resulting in the collapse of gel and cement hydrates. In moderate-strength concrete that has been exposed to high temperatures, voids are generated as the vapor inside the concrete evaporates at approximately 200 \({}^{\circ}\)C [2; 6; 7; 8]. The deformation recovery ability is drastically lowered in this temperature range, and the elastic modulus decreases significantly at temperatures of over 600 \({}^{\circ}\)C [2; 6; 7; 8].  
Concrete exhibits a tendency toward decreasing compressive strength and elastic modulus when exposed to high temperatures. Concrete exposed to high temperatures causes cross-sectional defects due to surface peeling or scattering [2; 6; 7; 8]. This phenomenon is called spalling. The main cause of this phenomenon is the water vapor pressure that is generated when the water inside the concrete expands in response to high heat [2; 6; 7; 8].  
It is known that when a concrete structure is exposed to temperatures of approximately 650 \({}^{\circ}\)C or higher, it loses 50% of its original strength. When it is exposed to temperatures of approximately 850 \({}^{\circ}\)C or higher, it loses its structural performance [2; 6; 7; 8].
## 3 The Use of Agro-Wastes in Concrete Production
### Durability and Long-Term Performance of Agro-Wastes in Concrete  
The durability and long-term performance of AWs in concrete remain critical factors for their widespread applicability in structural systems. While short-term studies have demonstrated improvements in mechanical properties, water absorption resistance, and compressive strength, limited data exist on their performance under real-world environmental conditions, including moisture exposure, freeze-thaw cycles, and chemical attacks.  
RHA and PO fuel Ash have shown notable contributions to durability as partial replacements for cement. For instance, studies indicate the following:  
* RHA at an optimal replacement level of 20% significantly enhances compressive strength and durability, reducing the carbon footprint and industrial waste [47; 53; 54; 55; 56].
* PO fuel Ash improves the mechanical properties and durability when heat-treated, with positive effects on resistance to aggressive environmental conditions [50; 54; 55; 56; 57].  
However, for other AW materials such as CSs, coconut fibers, WS, and other AWs, studies focusing on long-term durability and performance remain scarce.
* CSs and fibers: Although these materials improve mechanical properties, tensile strength, and ductility, their behavior under long-term exposure to moisture, freeze-thaw cycles, and chemical attacks is not well understood. Further studies are required to assess their degradation rates and overall stability in structural applications.
* WS and Ash: Research has highlighted their contributions to thermal insulation and mechanical properties. However, the long-term durability of WS-based composites, particularly their resistance to biodegradation and thermal stress, requires further investigation.
* Other AWs: Materials such as PO clinker and other lightweight agro-based aggregates have shown promise in enhancing mechanical properties, but their performance in varying environmental conditions has not been comprehensively studied.  
To address these gaps, future studies should focus on the following key areas:  
* Conducting lifecycle analyses (LCA) to evaluate the full environmental impact of AWs in concrete, including production, usage, and end-of-life stages.
* Investigating the degradation rates of AW-based concrete under diverse environmental conditions, such as humidity, freeze-thaw cycles, chemical exposure, and thermal variations.
* Developing standardized durability testing protocols to ensure the consistent evaluation of AW materials in structural applications.  
These efforts will provide a comprehensive understanding of the viability of AWs in real-world applications and contribute to their acceptance as sustainable alternatives in the construction industry.
# Three-Stage Deep Learning Framework for Video Surveillance
## 3 Proposed Method
### Proposed Overall Architecture
#### 3.2.2 Second Stage  
The bidirectional LSTM (BiLSTM) emerges as a significant improvement in the development of the long short-term memory (LSTM) networks mentioned in Section 2. BiLSTM augments the fundamental LSTM architecture by incorporating bidirectional processing capabilities. This modification allows for the simultaneous analysis of sequence data in both the forward and backward directions, enabling the consideration of past and future information at each time step. Consequently, BiLSTM surpasses unidirectional LSTM in capturing sequence-dependent characteristics, particularly excelling in extracting more accurate insights from temporally correlated data by considering both past and future contexts.  
In the second stage, the BiLSTM and multi-head attention techniques are integrated to effectively model the temporal characteristics of sequential data. BiLSTM integrates a bidirectional processing capability into LSTM, allowing it to simultaneously analyze sequence data in both the forward and backward directions, thus understanding the overall temporal flow of the sequence. On the other hand, multi-head attention processes and integrates information from various points within the sequence in parallel. This combination proves particularly beneficial in handling complex, temporally evolving information, such as video data.  
The integration of bidirectional LSTM (BiLSTM) with multi-head attention layers is known for its enhanced capability in handling long-term dependencies in sequence data. However, it still encounters limitations in maintaining and transmitting information from the distant past when dealing with exceedingly long sequences, as the length of the sequence increases. To overcome these limitations, a third stage is introduced, where the data processed through the second stage are further processed by Transformer encoder layers. This additional step aids in addressing the difficulties associated with lengthy sequence data, enhancing the model's ability to effectively retain and utilize historical information.
## 4 Data Analysis in Maritime IoT Applications
### Role of Data Analysis in Enhancing Maritime Operations
#### 4.2.3 Enhance Decision-Making  
Data-driven decision-making is critical in today's complex and dynamic maritime environment. By transforming raw data into actionable insights, data analysis aids decision-makers in making more informed and strategic decisions.  
From route planning and weather forecasting to maintenance scheduling and resource allocation, every aspect of maritime operations can benefit from data-driven insights. This not only leads to improved outcomes and reduced costs but also contributes to the resilience and adaptability of maritime organizations [81].
# Damage Detection in Beam Bridges Using Quasi-static Displacement Influence Lines
## 3 Numerical Example
### Comparison of the Performance Between the Proposed Method and the Traditional Approach
#### 3.3.1 Damage Detection Without the Effect of Measurement Noise  
In this situation, the damage is modelled through the 1/10L cross-section and the damage extent is set as 5%. Under the same quasi-static loading conditions before and after damage, 50 cases are simulated without any measurement noise, that is, 25 cases for a healthy bridge and 25 cases for a damaged bridge. For each case, 9 displacement ILs are obtained; all displacement ILs through different cross-sections are drawn in Figure 5. As shown in Figure 6a, it is difficult to directly detect the damage with the displacement ILs because the extent of damage is relatively small. With the traditional method, the difference in the displacement ILs before and after damage is applied to detect the damage, as shown in Figure 6b; obviously, the damage can be identified directly. Using the proposed method, the damage is also detected easily, as shown in Figure 6c. Therefore, both the proposed method and the traditional approach are effective at detecting damage without the effect of measurement noise under the same loading conditions before and after the bridge suffers damage.  
Figure 5: Displacement influence lines (ILs) obtained by 9 sensors for a healthy bridge.
# Laser Transmission Characteristics of Seawater for Underwater Wireless Optical Communication
## 3 Results and Discussion
### Average AOA
#### 3.1.2 Average AOA at Different Offsets on the Receiving Plane
Furthermore, when the attenuation length exceeds 25, the AOA distributions in coastal and turbid harbor waters tend to remain constant. When the attenuation length is 35, the peak angles in coastal water and turbid harbor water are 16.6\({}^{\circ}\) and 27\({}^{\circ}\), respectively, which is within a 1\({}^{\circ}\) increase relative to the peak angles at an attenuation length of 25. The sharp-peaked original laser beam is scattered after a certain attenuation length, and the final angular distribution only depends on the inherent optical characteristics (IOPs) of the water body following asymptotic radiative theory [4; 43; 44; 45; 46]. The analysis above highlights the crucial role of the AOA distribution in determining the FOV of the receiver and its impact on receiving gain. As shown in Figure 4, when the attenuation length is small, a high receiving gain could be obtained at the center of the receiving plane using a small FOV. However, the receiving gain rapidly decreases at offsets  
Figure 5: AOA distributions for the different attenuation lengths in (**a**) clear water, (**b**) coastal water, and (**c**) turbid harbor water. away from the center, so a large FOV is suitable. In addition, in coastal and turbid harbor waters, it is necessary to magnify the receiving field of view to obtain a high receiving gain at long attenuation lengths. These findings highlight the necessity of considering factors such as water type, transmission distance, and the position on the receiving plane when determining the FOV of the receiver.