esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Model Validation
presented a hybrid FE-multibody analysis to investigate the biomechanical properties of the human lumbar spine [29]. Accordingly, the IVR results of these in vitro and in silico studies have been used as a basis for comparison in the case of pure bending load. The employed combined loads were determined by [PERSON] et al. and [PERSON] et al., using follower load and bending moment combination that most closely approximates the average of in vivo measurements reported in the literature [34; 82; 83]. In the case of the combined load, the average of three in vivo measurements [53; 54; 55] was used as a basis for comparison when evaluating the IVR results. In both load cases, the FE predictions of the current study agree with the experimental results in extension, lateral bending, and axial rotation, while the simulations underestimated the experimental results in flexion. The predictions of the current study are comparable to the in silico findings reported by [PERSON] et al. and [PERSON] et al. [2; 29]. The predicted IDP values for combined load were compared to the mean of eight well-accepted in silico results [2]. Moreover, [PERSON] et al. performed an in vivo analysis in a non-degenerative L4-L5 IVD [56]. Their study was designed to provide a validation dataset for in silico or in vitro investigations. Excellent agreement with the in vivo and in silico results indicates that both models can predict the intradiscal pressure values accurately. In the case of combined load, the FJF values were compared only to those mean in silico results reported by [PERSON] et al. due to the lack of relevant experimental results [2]. In general, the validated in silico studies show a significant variance regarding the FJF's magnitude, indicating that this force is sensitive to the FE modelling techniques and the patient-specific anatomy. This finding is also supported by [PERSON] et al., who found that the FJF is highly dependent on the individual geometry of facet joint surfaces [84]. In flexion, no force occurred due to the lack of contact. In extension, the magnitude of the FJF increases caudally, agreeing to the results reported by [PERSON] et al. [29]. In lateral bending, FJF occurred only at the L4-L5 spinal level, as it was reported in several models investigated by [PERSON] et al. [2].
## 5 Real-Time Control Algorithm Implementation and Testing in NI Compact RIO and PXIe  
The compact RIO (cRIO) combines an RT operating system with an embedded floating-point processor. It has a remarkable FPGA performance and hot-swappable analogue and digital input/output modules with hardware flexibility. Each module in cRIO is directly connected to the FPGA, imparting minimum jitter and high-speed input/output signal processing. The FPGA is physically linked to the RT processor via a PCI bus as shown in Figure 16, which represents the internal architecture of the cRIO with an open retrieve to basic hardware resources. The FPGA and RT processor are programmed in LabVIEW, which is a graphical-language-based programming platform. LabVIEW has integrated data-fetching mechanisms to circulate data from the FPGA to the input/output modules and from the FPGA to the RT processor for real-time analysis, data logging, post-processing, and communication with the host computer.  
Figure 16: NI compact reconfigurable input/output (cRIO) architecture.  
Figure 15: Three-phase symmetrical fault.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Item No.** & **System Parameters** & **Values** \\ \hline
1 & Rated power & 10 MVA \\
2 & Vac grid voltage & 4.16 kV \\
3 & \(V_{dc}\) & 8 kV \\
4 & Switching frequency & 300 Hz \\
5 & Rated frequency & 50 Hz \\
6 & SM\_cap (submodule capacitance) & 5000 μF \\
7 & L\_arm (arm inductance) & 5 mH \\
8 & L\_val (line inductance) & 3 mH \\
9 & R\_line (line resistance) & 0.003 \(\Omega\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: System parameters and values.
## 3 Multivariable Polynomial Signature Schemes
### LUOV Scheme  
As mentioned above, LUOV is based on UOV and basically differs from it in the key generation algorithm and in the way a preimage of \(y\) is computed under the central map \(\mathcal{F}\). More specifically, its key generation algorithm generates the linear map \(\mathcal{T}\) and a large part of the public key from the output of a pseudo-random generator on input a 256-bit seed. Additionally, the public key \(\mathcal{P}\) is 'lifted' to an extension field \(\mathbb{F}_{2}\subset\mathbb{F}_{2^{\prime}}\), \(r\in\mathbb{N}\) by considering the map \(\mathcal{P}:\mathbb{F}_{2}^{n}\rightarrow\mathbb{F}_{2}^{o}\) as a map \(\mathcal{P}:\mathbb{F}_{2^{\prime}}^{n}\rightarrow\mathbb{F}_{2^{\prime}}^{o}\) (hence its name). Note that this field lifting makes the public key remain small (since the coefficients of the public key are 0 or 1), as well as makes solving the system \(\mathcal{P}(x)=y\) for some \(y\) in \(\mathbb{F}_{2^{\prime}}\) more difficult compared to the case when \(y\) is in \(\mathbb{F}_{2}\)[2]. Finally, the matrix \(\mathbb{T}\) that represents the linear map \(\mathcal{T}\) is chosen of the form  
\[\begin{pmatrix}I_{o}&-T\\ O&I_{o}\end{pmatrix} \tag{2}\]  
where \(T\) is a \(v\)-by-\(o\) matrix generated from a private seed. Note that \(\mathbb{T}\) is determined by \(T\) and its inverse matrix is itself. This choice is made for optimisation, i.e., to make both the key generation algorithm and the signing algorithm run much more quickly, although it does not affect the security of the scheme [1]. We next expand more on the LUOV scheme, the reference implementation and libpqcrypto implementation.
## 4 An Indoor Positioning Algorithm Combining IMU and LBP-FFT-CNNs
### Inertial-Based Positioning Algorithm Using IMU
#### 4.1.5 Sensor Data Dynamic Fusion Based on the EKF Algorithm  
The integration of two systems is demonstrated using the EKF algorithm as follows:  
\[\overset{\rightarrow}{p}_{t}=\overset{\rightarrow}{p}_{IMU,t}+K_{t}\left( \overset{\rightarrow}{p}_{rision,t}-\overset{\rightarrow}{p}_{IMU,t}\right) \tag{23}\]  
where \(K_{t}\) is the Kalman filter. The LBP-FFT-CNNs model performs obstacle detection and classification by analyzing visual texture and frequency information. Integrating it with IMU data addresses the limitations of each system, resulting in a more robust positioning and obstacle detection framework. First, the LBP-FFT-CNNs model provides absolute position updates or corrections using visual feature matching, mitigating drift. Second, IMU data offer high-frequency updates, complementing the relatively slower processingspeed of vision system to ensure real-time tracking and positioning. The third aspect lies in adaptability to diverse environments, enabling efficient performance in both feature-rich and feature-scarce scenarios.
## 6 Strengths, Limitations, and Future Research Directions
### Limitations
#### 6.2.5 Explainability and Interpretability  
Despite well-established methods for model interpretation, CNNs still lack full interpretability because the complex interactions between layers and neurons can make it difficult to fully understand the model's decision-making process, particularly in deeper layers of the network.  
While ViTs produce attention maps for interpretability, the complex interactions between self-attention heads can still present challenges in accurately interpreting the model's behaviour. ViTs can have multiple heads attending to different regions, which can make it difficult to interpret the interactions between different attention heads and to understand the reasoning behind model predictions.
###### Abstract  
The archipelagic Romblon province frequently experiences typhoons and heavy rains that causes extreme flooding, this produces particular concern about the severity of damage in the Municipality of Odiongan. Hence, this study aimed to assess the spatial flood risk of Odiongan using the analytical hierarchy process (AHP), considering disaster risk factors with data collected from various government agencies. The study employed the geographic information system (GIS) to illustrate the spatial distribution of flooding in the municipality. Sendai Framework was the basis of risk analysis in this study. The hazard parameters considered were average annual rainfall, elevation, slope, soil type, and flood depth. Population density, land use, and household number were considered parameters for the exposure assessment. Vulnerability assessments considered gender ratio, mean age, average income, number of persons with disabilities, educational attainment, water usage, emergency preparedness, type of structures, and distance to evacuation area as physical, social, and economic factors. Each parameter was compared to one another by pairwise comparison to identify the weights based on experts' judgment. These weights were then integrated into the flood risk assessment computation. The results led to a flood risk map which recorded nine banezays (small local government units) at high risk of flooding, notably the Poblacion Area. The results of this study will guide local government units in developing prompt flood management programs, appropriate mitigation measures, preparedness, and response and recovery strategies to reduce flood risk and vulnerability to the population of Odiongan.  
AHP; digital elevation model; flood; GIS; risk assessment Article  
Flood Risk Assessment Using GIS-Based Analytical Hierarchy Process in the Municipality of Odiongan, Romblon, Philippines
## 6 Conclusions  
An undersampling method that can be implemented with low complexity hardware in an OFDM environment was presented in this paper. It can reduce the samples needed at the receiver by up to 1/4 reducing the power consumption, the size of the memory needed to store the ADC samples and the complexity of the FFT. The proposed method can recover the original sparse data with zero or very low error assisted by the employed FEC scheme without the use of time consuming iterative processes.  
Future work will focus on the investigation of the efficiency of different QAM modulations, FFT sizes and implementations, FEC methods, such as LDPC and Turbo Codes, interleaving schemes, _etc_. Different channel noise models will also be tested. Moreover, we will look for special applications where the input data obey to more sophisticated relations between them in order to allow the substitution of a larger number of samples with low BER.
# IoT and Blockchain Integration: Applications, Opportunities, and Challenges
## 4 Blockchain Technology
### Types of Blockchain Networks  
Depending upon who can join a network, blockchain networks are of mainly four categories [30]: (i) public, (ii) private, (iii) consortium, and (iv) hybrid BCN.  
In public, open, or global blockchain network, any node can join, leave, and perform tasks independently following the network protocol. For instance, Bitcoin and Ethereum networks are popular public blockchain networks. However, in private BCN, also called permissioned BCN, only the permitted nodes can join the network. The network manager or owner performs the node authentication and access control operations. The identity of permitted nodes is also managed as part of the network operation. Consortium BCN is a particular type of private BCN where every participant in the network is a member of the same company or organization or a group of collaborating companies or organizations. A hybrid BCN combines the feature of both public and private BCN. They provide data privacy by allowing only permitted nodes to join the network while using the public node's consensus to validate transactions that do not contain private data. Hybrid BCN is suitable for IoT systems such as supply chain management for data protection and access control [16].  
In general, a blockchain network consists of two types of computing nodes, viz., verifier and normal nodes. Verifier nodes keep a copy of the complete blockchain structure and validates the transactions. These nodes are also known as full nodes. They have higher computing and storage power. They contribute to data integrity, execute smart contracts, maintain network security, and participate in network consensus mechanisms. They communicate transactions and messages using various communication protocols such as Gossip and Kademlia [16]. Normal nodes are lighter nodes that do not require high storage and computing power. They do not store a copy of the blockchain ledger; however, they obtain partial information about the blockchain status from the full nodes. Table 1 summarizes key features of blockchain networks.  
Figure 3: (**Left**) Structure of a blockchain (ledger) as a sequence of data blocks Block 0, Block 1, and so forth. Each block is linked to the previous block through a reference known as block hash, which is an output of a cryptographic function of the content of the previous block. (**Right**) A simple blockchain network with three nodes \(N_{1}\), \(N_{2}\), and \(N_{3}\), each keeping a copy of a blockchain. As a transaction (\(T_{j}\)) is received by a node (\(N_{2}\)), it is broadcast to other nodes for execution.
## 3 Results
### Signal Amplitude Value Statistical Analysis  
The sum of absolute amplitude values was computed from the GPR signals on one of the longitudinal profiles that intercepted the compacted soil zones and then plotted based on the trace number (Figure 4). The figures show the area plot, which allows for delineating trends and deviations in the plotted data. The amplitude values examined on the profile are those within the uppermost horizon (time: 0-5 ns) and on the profile distance from 5.5 m to 40 m (inset Figure 4, yellow rectangle line). The zones of altered soil structure because of the normal stress induced by the tractor passes are easily discernible, showing significant contrasting values (less than 150,000, inverted arrows in Figure 4). The decline in the values at these zones may be attributed to change caused by the loss of the soil structure, which hosts the fluid contents that influence the dielectric permittivity of the media that controls the reflectivity, as revealed in the signal's amplitude.  
Figure 4: _Cont._  
In the same vein, computed sum absolute amplitude values on two adjacent GPR profiles, with one profile each from both distorted and undistorted zones, were plotted. The plotted curves in the two different scenarios were curve-fitted using the polynomial method, and the results gave an insight into the GPR signal responses. The sixth-degree polynomial fitted curve predicted higher amplitude values on the undistorted profile (Figure 5a) than the profile where normal stress was induced on the soil (Figure 5b). It can be deduced that signal reflectivity declined from ~200,000 to 150,000 in the two scenarios, respectively could be as a result of the alteration in the soil structure.  
Figure 4: GPR signal sum of absolute amplitude value plots along a longitudinal profile (from part of the unaltered soil zone to part of the altered zone). The inset represents the plotted zone marked within the yellow rectangle.  
Figure 5: GPR signal sum of absolute amplitude value plots on two adjacent profiles: (**a**) for a profile on the unaltered zone and (**b**) for a profile on the altered zone.  
The higher signal amplitude values at the undistorted zone, as expected, may be due to the contrasting dielectric permittivity with the altered zone induced by the influence of the soil structure alteration.
# Long-Term Creep Mechanical and Acoustic Emission Characteristics of Water-Immersed Coal Pillar Dam
## 2 Methodology  
### Selection of Long-Term Loading Stress  
As the primary underground load-bearing structure within CMURs, coal pillar dams feature sections perpetually submerged below water level, as shown in Figure 1. The reservoir water level fluctuates between 3 and 4 m through operational adjustments based on intake and discharge volumes rather than maintaining a constant height. Without specialized waterproof treatment, one side of these coal pillar dams remains under sustained water immersion. To evaluate their long-term stability, laboratory and field tests must be carried out to establish the relationship between the in situ stress states of coal pillar dams and their long-term strength and determine appropriate stress levels for designing long-term creep mechanical tests so as to investigate time-dependent mechanical response patterns during the sustained loading of coal pillar dams.  
According to field borehole tests conducted by previous research among CMURs in the Shendong Mining Area [19], the in situ stress states of coal pillar dams in the regional CMURs range from 0 to 12 MPa. The testing results obtained by previous research [20] (Figure 2) on samples from the Daliuta CMUR showed that the uniaxial compressive strength (UCS) of Daliuta coal specimens under non-immersed conditions is approximately 15.5 MPa, while the UCS exhibits a distinct declining trend with increasing immersion duration.  
Figure 1: Schematic diagram of the CMUR and structure.  
Based on the mechanical characteristic curves under uniaxial conditions [20] and representative characteristic curves [21] for coal at Daliuta CMUR, it is acknowledged that the long-term strength of Daliuta coal pillar dams at failure is approximately 60% of its short-term strength (\(\approx\)9 MPa). Considering the actual in situ stress conditions and detrimental factors affecting dam stability, the experimental load was set at 10 MPa (65% of short-term strength). Acoustic emission (AE) monitoring was implemented to capture and analyze time-dependent mechanical responses during prolonged creep deformation and the microcrack propagation process within the coal.
## 3 Simulation Tests and Analysis
### Reconstruction Result  
After setting up the sample dataset with the CFD simulation results, the POD basis vectors were extracted from it. Test cases were used to show the reconstruction result.  
For reconstruction, 25 pieces of data were randomly extracted from the CFD results, and Gaussian noise was added to these data and the signal-to-noise ratio (SNR) was 40 dB. The results were used as the measurement data. In this special test case, the measurement data remained constant. Figure 2 shows the temperature contours with the simulation results and reconstruction results in the plane at 0.08 m.  
As Figure 2 shows, the contours in Figure 2b are similar to those in Figure 2a, and the contours in Figure 2c are quite different from those in Figure 2a. When comes to Figure 2d, it is obvious that using eight POD basis vectors could result in a much better solution than using 20 POD basis vectors. Above all, Figure 2 indicates that the proposed method could be used in temperature field  
Figure 2: Comparisons of the reconstruction results. (**a**) Simulation results in the special test case, (**b**) reconstruction results in the special test case (eight proper orthogonal decomposition (POD) basis vectors), (**c**) reconstruction result in special test case (20 POD basis vectors), and (**d**) reconstruction errors in different test cases.  
reconstruction, it also reveals that there are some important factors that can affect the reconstruction result and further study is needed.  
Moreover, the boundary condition of the special test case is different from those used to set up the sample dataset. This result shows that the proposed method has a wide range of applications and a certain robustness.
# Benchmarking Deep Learning Methods for Aspect Level Sentiment Classification
## 3 Deep Learning Methods for Aspect Level Sentiment Classification
### Data Modelling Procedure for Deep Learning-Based ALSC
In Figure 5, the dependency relations are the labels on the arc as: cc, det, compound, nsubj, and acomp. **Location of aspect**\(\rm LOC_{\rm Aspect}\) is the starting and ending index of the aspect location in the sentence. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**ID**} & \multirow{2}{*}{**Deep Learning Method**} & \multirow{2}{*}{**Description**} & \multirow{2}{*}{**Input to the Method**} & \multicolumn{2}{c}{**Neural**} & \multirow{2}{*}{**Attention Used**} & \multirow{2}{*}{**Syntactical Information**} \\  & & & & & & **Architecture** & \\ \hline \multirow{3}{*}{DL31} & \multirow{3}{*}{CapsNet [31]} & \multicolumn{3}{c}{Capsule Network utilizes BiGRU encoding layer and capsule-guided routing along with BERT for ALSC.} & \multirow{3}{*}{S, A, \(\rm C_{l}\), \(\rm C_{r}\)} & BiGRU, \(\rm Transformer\) & Yes & No \\ \cline{3-8}  & & & & & & \\ \hline \multirow{3}{*}{DL32} & \multirow{3}{*}{RGAT [36]} & \multicolumn{3}{c}{Relational Graph} & \multirow{3}{*}{A, S, \(\rm POS_{Tag}\), \(\rm POS_{Tag}\), \(\rm POS_{R}\), \(\rm POS_{R}\), \(\rm POS_{R}\), \(\rm POS_{R}\), \(\rm POS_{R}\), \(\rm POS_{R}\), \(\rm POS_{R}\), \(\rm POS_{R}\), \(\rm POS_{R}\), \(\rm LOC_{\rm Aspect}\)} & \multirow{3}{*}{Yes} & \multirow{3}{*}{Yes} \\  & & dependency tree from an ordinary dependency tree. & & & & \\ \hline \multirow{3}{*}{DL33} & \multirow{3}{*}{BERT-SPC [40]} & \multicolumn{3}{c}{BERT-SPC is a simple pre-trained BERT model designed for ALSC task.} & \multirow{3}{*}{C, A} & \multirow{3}{*}{BERT} & \multirow{3}{*}{Yes} & \multirow{3}{*}{No} \\ \hline \multirow{3}{*}{DL34} & \multirow{3}{*}{GAT-Glove [37]} & GAT leverages dependency relation by adopting a Relation graph attention network for exchanging the words based on the dependency tree. & \multirow{3}{*}{S, A, \(\rm D_{T}\), \(\rm D_{R}\)} & BiLSTM, Graph Attention Network & & \\ \hline \multirow{3}{*}{DL35} & \multirow{3}{*}{GAT-BERT [37]} & Variant of GAT(DL34) leveraging contextual BERT embeddings. & \multirow{3}{*}{S, A, \(\rm D_{T}\), \(\rm D_{R}\)} & BiLSTM, Graph Attention Network & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
Figure 5: Dependency Tree for the sample sentence. The inputs to non-BERT based methods are converted to vectors using GloVe embeddings [45]. However, BERT embeddings are used for converting input to vector in the BERT-based method. In addition, BERT based method require [CLS] and [SEP] for starting and separating the input, respectively.