esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multimodal Affective Sensing Model for Constructing a Personality-Based Financial Advisor System
## 4 Experiments and Results
### Deep Feature Learning from a Single Modality of an Individual for Personality Prediction
#### 4.1.3 Personality Trait Analysis Based on Text Features  
Text analysis is an effective method for collecting personality information. Most personality trait analysis literature in text-feature learning uses RNN and LSTM models as feature extraction approaches. However, for the long sequence of text message propagation processes, the unidirectional LSTM struggles to convey the message to the finish in the current timestep. As a result, the model for text feature extraction is a two-way LSTM nerve network. We utilized 200-dimensional feature vectors as the input shape and five bidirectional LSTM layers with ReLU activation functions in each layer, as well as 256, 128, 64, 32, and 16 neurons in the neuron settings. We set the number of neurons in the last fully linked layer to one and utilized the linear activation function to predict the personality trait scores. Table 5 presents the evaluation results. We found that the evaluation values of the model are slightly high, probably because the transcribed words in the dataset are too similar, resulting in the model learning effectiveness not being particularly strong. Nevertheless, we can understand from the graph that the loss value of the model maintains a continuously decreasing trend, despite the loss value being high. Figure 7 illustrates the comparison of loss values of text features using Bidirectional LSTM model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **MAE** & **MSE** & **RMSE** & \(\mathbf{R^{2}}\) \\ \hline Openness & 12.707 & 265.683 & 16.299 & 0.642 \\ Conscientiousness & 11.126 & 225.961 & 15.032 & 0.687 \\ Extraversion & 11.498 & 247.152 & 15.721 & 0.676 \\ Agreeableness & 12.454 & 260.860 & 16.151 & 0.656 \\ Neuroticism & 13.932 & 334.969 & 18.302 & 0.627 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluation results for the CNN model based on speech features.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **MAE** & **MSE** & **RMSE** & \(\mathbf{R^{2}}\) \\ \hline Openness & 17.769 & 435.278 & 20.863 & 0.493 \\ Conscientiousness & 8.015 & 78.645 & 8.868 & 0.678 \\ Extraversion & 16.961 & 357.955 & 18.920 & 0.513 \\ Agreeableness & 11.704 & 175.527 & 13.249 & 0.587 \\ Neuroticism & 9.931 & 119.824 & 10.946 & 0.642 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Evaluation results for the Bidirectional LSTM model based on text features.
# Game Theory-Based Smart Mobile-Data Offloading Scheme in 5G Cellular Networks
## 5 Simulation Results  
In this section, we present simulation results and discussions to verify the efficiency of the proposed game scheme. In order to see the domination of the proposed scheme, we compare the performance of the proposed game scheme those of two baseline schemes; the MFI scheme and the no-offload scheme. In the MFI scheme, UEs can also offload data traffic to another UE, and the fairness index of the network is maximized such that link pairs receive a fair payoff. In the no-offload scheme, UEs will transmit data to other UEs through the BS with offload coefficients of zero. We employ performance metrics (average throughput, FI value, sum of payoff value, and EE) in the performance evaluation with various network conditions, such as mean value of harvested energy, and offload coefficient with changing of required minimal transmission data. The FI is defined as a value to determine if link pairs are receiving a fair share of the payoff from the system. In these simulations, we assume a macro BS is located in the center of a typical macro cell with a radius of 180 m, and four UEs are randomly distributed throughout the macro cell. Bandwidth and frequency of the RF signal are set at 1 MHz and 2 GHz, respectively. In addition, we set the path-loss models based on macro-to-UE and A1-type generalized path-loss models developed by the 3 GPP [61] and WINNER II [62], respectively. The minimal payoff to link pairs is set at 0. Algorithm 1 is used to find the optimal offload coefficient for MDO, and the value of other parameters used in the simulation are listed in Table 1.  
```
1:Initialization: Set parameters: \(M\), \(R\), \(A\), \(P_{BS}\), \(P_{m}\), \(N_{m}\), \(BW\), \(K\), \(N_{timeslot}\), \(\alpha\), \(\beta\), \(C\), \(E_{hal}\), \(E_{re}\)\((1)=0\)
2:Input:\(\ell^{\text{{\tiny{ID}}}}\left(t\right),E_{re}\left(t\right)\),\(S=\left(S_{1},\ldots,S_{i},\ldots,S_{I}\right)\), where \(S_{i}=\omega_{mn},\forall m,n\in\mathcal{M},n\
eq m\), and \(0\leq\omega_{mn}\leq 1\)
3:Output: Optimal offload coefficients \(\left(\omega_{mn}^{opt}\right)\), evaluation metrics (FI value, average throughput, EE).
# A Systematic Review of the Trends and Advances in IFC Schema Extensions for BIM Interoperability
## 6 Discussion
### Summary of the Results and Suggestions for Future Work
#### 6.1.2 Trends Identified through Timeline Analyses  
A subsequent timeline analysis revealed changing trends within each domain over the discussed period. The architectural domain, which initially emphasized process representations, showed increases in product representations around 2018, with a significant spike in 2022. This shift aligns with recent endeavors to incorporate insights from innovative technologies and building systems into the IFC schema, building upon the pre-established definitions of the initial IFC schema. Conversely, the infrastructure domain transitioned from an initial focus on product representations to a rising interest in process representations from 2019 onwards. Version 4.0 of the IFC schema seems to have played a pivotal role in this transition.
## 3 Methodology  
### Standard PSO  
The PSO algorithm belongs to a class of swarm intelligence algorithms inspired by the social behavior of bird flocks. It searches for the optimal solution to a problem by modeling the collaborative search of flock members in the solution space. In PSO, a potential solution in the solution space is called a particle. The position of the particle indicates the location of the solution, and the velocity of the particle indicates the direction and speed of the solution moving in the solution space. At the beginning of the algorithm, the particle population is first initialized with random positions and velocities, and the fitness value of each particle is calculated. For each particle, its optimal position and the global optimal position in the whole population are recorded.  
The position and velocity of the \(i\)-th particle in the population are denoted as \(x_{i}(t)\) and \(v_{i}(t)\). Then, the updated formula for the particle is given as follows:  
\[v_{i}(t+1)=\omega v_{i}(t)+c_{1}r_{1}(y_{i}(t)-x_{i}(t))+c_{2}r_{2}(\vec{y}_{i }(t)-x_{i}(t)) \tag{16}\]  
\[x_{i}(t+1)=x_{i}(t)+v_{i}(t+1) \tag{17}\]  
where \(\omega\) is the inertial weight, \(c_{1}\) is the cognitive coefficient, \(c_{2}\) is the social coefficient, \(r_{1}\) and \(r_{2}\) are random sequences sampled from a uniform probability distribution in the range \([0,1]\). \(y_{i}\) represents the historical optimal position of the particle and \(\vec{y}_{i}\) is the global historical optimal position among all particles.  
According to Equations (16) and (17), the movement of a particle is directed by three factors, namely, following its way, moving toward its best position, or moving toward the swarm's best position. The ratio among those factors is determined by the values of \(w\), \(c_{1}\), and \(c_{2}\).
# Personal Picocell Scheme Using Adaptive Control CRE in Heterogeneous Mobile Networks +
## 3 User Throughput in Single-Band HetNet  
### Simulation Setup  
In this study, we evaluate the user throughput of adaptive control CRE using system-level computer simulations under the conditions that the number of macrocells with three sectors per site is 19 and the number of picocells is four per macro-sector. The primary simulation parameters are listed in Table 1. The carrier frequencies used in the macro- and pico-eNBs are 2.0 GHz. The system bandwidth of both the macro- and pico-eNBs are assumed to be 10 MHz. The number of UEs is set to 30 per macro sector. UE is located based on a cluster distribution in which 2/3 of the number of UEs are uniformly deployed nearby the pico-eNBs and the rest (1/3 of the number of UEs) are uniformly deployed within the macrocell. The transmission power in the downlink is fixed at +46 and +30 dBm for the macro- and pico-eNBs, respectively. The path-loss model and other assumptions are obtained from in ref. [25]. In this paper, we focus on the performance evaluation of downlink user throughput.  
As the use of higher-order QAM is expected to increase data rates within a limited bandwidth, the implementation of 1024-QAM and 4096-QAM has been discussed [26; 27; 28] for 5G and beyond. However, in the simulations, we use 15 types of MCS indexes up to 256-QAM in the downlink as listed in Table 2, because the application conditions for these post 256-QAM are currently very limited. This is the same as the channel quality indicator (CQI) indexes specified by 3 GPP release 12. The MCS used in the downlink for UE is determined by the channel quality between the macro-eNB and the UE. The lowest and highest modulation schemes are quadrature phase shift keying (QPSK) and 256-QAM, respectively. MCS index incorporating a higher-order QAM is assigned to UE with good channel quality; however, QPSK is almost assigned to UE with poor channel quality.  
System-level simulations are worth evaluating user throughput for mobile network, and will contribute significantly to the requirements at early stages of the development and hardware implementation.
# Seasonal Net Carbon Exchange in Rotation Crops in the Temperate Climate of Central Lithuania
###### Abstract  
Intelligent agricultural solutions require data on the environmental impacts of agriculture. In order for operationalize decision-making for sustainable agriculture, one needs to establish the corresponding datasets and protocols. Increasing anthropogenic CO\({}_{2}\) emissions into the atmosphere force the choice of growing crops aimed at mitigating climate change. For this reason, investigations of seasonal carbon exchange were carried out in 2013-2016 at the Training Farm of the Vtyatutas Magnus University (former Aleksandras Stulginskis University), Lithuania. This paper compares the carbon exchange rate for different crops, _viz._, maize, leg, winter wheat, spring rapeseed and barley under conventional farming. This study focuses on the carbon exchange rate. We measure the emitted and absorbed CO\({}_{2}\) fluxes by applying the closed chamber method. The biomass measurement and leaf area index (LAI) calculations at different plant growth stages are used to evaluate carbon exchange in different agroecosystems. The differences in photosynthetically assimilated CO\({}_{2}\) rates were significantly impacted by the leaf area index (\(p\) = 0.04) during the plant vegetation period. The significantly (\(p\) = 0.02-0.05) strong correlation (\(r\) = 0.6-0.7) exists between soil respiration and LAI. Soil respiration composed only 21% of the agroecosystem carbon exchange. Plant respiration ranged between 0.034 and 3.613 umol m\({}^{-2}\) s\({}^{-1}\) during the vegetation period composed of a negligible ratio (mean 16%) of carbon exchange. Generally, respiration emissions were obviously recovered by the gross primary production (_GPP_) of crops. Therefore, the ecosystems were acting as an atmospheric CO\({}_{2}\) sink. Barley accumulated the lowest mean _GPP_ 12.77 umol m\({}^{-2}\) s\({}^{-1}\). The highest mean _GPP_ was determined for leg (14.28 umol m\({}^{-2}\) s\({}^{-1}\)) and maize (15.68 umol m\({}^{-2}\) s\({}^{-1}\)) due to the biggest LAI and particular bio-characteristics. Due to the highest _NEP_, the leg (12.66 umol m\({}^{-2}\) s\({}^{-1}\)) and maize (12.76 umol m\({}^{-2}\) s\({}^{-1}\)) agroecosystems sank the highest C from the atmosphere and, thus, they might be considered the most sustainable items between crops. Consequently, the appropriate choice of crops and their area in crop rotations may reduce CO\({}_{2}\) emissions and their impact on the environment and climate change.  
Keywords:CO\({}_{2}\) fluxes; bio-parameters; environment; crops +
Footnote †: journal: _Nustainability_
## 3 Results and Discussion
### Effects of \(\Delta\Omega\)  
Based on the above analysis, \(\Delta\Omega\) might have a significant effect on the extreme load prediction in this model. In this section, we want to test the results when \(\Delta\Omega\) is equal to 0, that is without delay in the safety control system. During the calculation, Equations (20) to (33) were still used. However, the upper value of rotor speed was change to 2.45 rad/s in Equation (26) and 2.17 rad/s in Equation (33) respectively. Figures 14-17 show the comparison of results from PSO-ELPM with and without considering \(\Delta\Omega\) (expressed as \"no delta omega\" in these figures).  
Figure 11: Comparison of extreme edgewise bending moment along the blade (1.5 MW).  
Figure 10: Comparison of extreme flapwise bending moment along the blade (1.5 MW).  
Figure 14: Comparison of extreme flapwise bending moment along the blade (1.5 MW).  
Figure 12: Comparison of extreme flapwise bending moment along the blade (2.0 MW).  
Figure 13: Comparison of extreme edgewise bending moment along the blade (2.0 MW).  
Figure 16: Comparison of extreme flapwise bending moment along the blade (2.0 MW).  
Figure 17: Comparison of extreme edgewise bending moment along the blade (2.0 MW).  
Figure 15: Comparison of extreme edgewise bending moment along the blade (1.5 MW).  
From Figures 14 and 16, we can see the flapwise bending moments are still very close. It means \(\Delta\Omega\) had little influence on them. This is also can certified by Table 2. In Table 2, the extreme flapwise bending moment did not happen at the maximum rotor speed. Hence, the extreme value was not changed though the maximum rotor speed reduced. But the edgewise bending moments was very different, and noticeably changed without \(\Delta\Omega\), especially near the blade root. Therefore, it needs further improvement. The \(\Delta\Omega\) should be fixed to make this model more accurate.
## 4 Results and Discussion
### Effect of CAWA on the Geotechnical Properties
#### 4.2.2 Ultrasonic Pulse Velocity (UPV)  
After 7 days of curing, UPV tests were conducted on four separate samples (i.e., 0, 5, 10, 15, and 20% of CACW). The CACW had a substantial influence on the improvement in ultrasonic pulse velocities, as seen in Figure 6. It can be shown that samples with  
Figure 5: Impact of adding CAWA on the optimum moisture content and maximum dry density.  
10% CACW had greater UPV values than those with other CACW fractions; however, specimens with 15% and 20% CACW had a slight decrease in UPV values. The sample with 10% CACW in the soil mixture performed better than the other mixes in terms of UPV. This finding is consistent with the research's earlier findings. The maximum UPV values were found at 10% of CACW due to the highest maximum dry density at the same percentage as indicated in the previous section. The improvement in UPV values is seen as a respectable indication of good densification in the soil-CACW mixture, particularly with 10% CACW and the soil-5% CACW-2% cement mixture as well. Greater UPV values are another excellent sign that the specimen's structure has been homogenized, as this enables the applied waves to pass through the specimens more quickly and leads to greater UPV values. CACW, a finely graded granular material, has demonstrated its capability to operate as a good filler material by producing well-homogeneous mixtures and increasing particle bonding. Figure 6 shows that specimens with CACW greater than 10% had lower UPV values than those with 10% WMD. This demonstrated that 10% CACW is the ideal proportion for filling the pore space in the specimen and maintaining the continuity of the grain-to-grain connections.  
As stated before, with a 10% CACW replacement, the specimens' maximum dry density was increased, and the ultrasonic pulse velocity of the mixtures was increased. This was owing to the mixtures' more homogeneous and uniform structures, which hastened the passage of ultrasonic waves in the mixtures, resulting in higher UPV values. The extra amount of CACW in the combination caused a more porous structure, which resulted in a reduction in the UPV of the specimens with a further increase in CACW replacement (Figure 6). The same trend is observed with a higher value of ultrasonic pulse velocity, as seen in Figure 6 when 2% cement is added to the mix.
# Performance and Challenges of Service-Oriented Architecture for Wireless Sensor Networks
## 2 Background and Concept  
### Middleware Architectures for WSNs  
The middleware architectures for WSNs have the ability to distribute sensor nodes, sink nodes, and high level applications [13], as shown in Figure 1. This middleware does not support SOM architecture that is integrated with WSNs [4]. SOM architecture is a designed middleware similar to WSN middleware with a new layer called the advanced services layer [4]. The architecture consists of three layers, which are the cluster services layer, resource management services layer, and advanced services layer. The advanced services layer provides services for security, QoS, and reliability applications [4]. Each layer provides services for the above layers as shown in Figure 2.  
Middleware provides many advantages when applied to WSN applications. These advantages range from hiding the complexity of the network communication, dealing with the heterogeneity of applications or devices, and managing system resources. The components of the middleware architectures are used to integrate WSNs with user applications while the complexity and heterogeneities of the hardware and software are hidden [14].  
The literature discusses a new and emerging architecture called SOA, where each component acts as a service. It enables the software services to interact with each other to execute and complete numerous tasks. The SOA services communicate through different standard languages such as Extensible Markup Language (XML) and Simple Object Access Protocol (SOAP). The challenges mentioned above can be addressed using the SOM architecture. The SOA can be applied with or without middleware that allows different applications to interact with various networks. SOA is a framework design that enables various applications to be developed by using loose coupling and interoperable services. The SOA consists of different components including the service provider, service registry, service customer, and message-based interaction protocol as shown in Figure 3[15].  
Figure 1: Middleware Architecture for WSN [13].  
Figure 2: The SOM Architecture Layers for WSNs [4].  
Moreover, SOA enables different services for Heterogeneous Cyber-Physical-Systems that can be selected and shared among various applications as proposed in [16].
## 3 System Design
### Overview  
The methodology adopted in this research demonstrates the practical viability of an ABAC system integrated with blockchain technology for the privacy-preserving tokenization of mineral reserves.  
The primary objective is to move beyond traditional role-based systems and validate a dynamic, attribute-driven model where access permissions are governed based on real-world conditions such as mining licenses, ownership percentages, and regulatory statuses [24]. This direction aligns with recent advances in lightweight ABAC models that leverage blockchain frameworks, such as Hyperledger Fabric, to enforce fine-grained, auditable, and decentralized access control for sensitive resources in constrained environments [25].  
The overall approach can be divided into three main phases:  
1. Blockchain and Smart Contract Design: A permissioned blockchain environment is configured (using Hyperledger Fabric) to deploy smart contracts capable of managing user attributes, resource access policies, and evaluation mechanisms securely and immutably [8].
2. Synthetic Data Generation: A simulated dataset is generated to mirror realistic operational conditions. Each user receives multi-dimensional attributes relevant to the mining sector.
3. Simulation and Scalability Testing: Automated scripts register users, create policies, and evaluate access in a large-scale manner, resulting in thousands of transactions that assess system correctness, performance, and scalability [26].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Criteria** & **Conceptual Research** & \begin{tabular}{c} **Prototype** \\ **Implementations** \\ \end{tabular} & **This Work** \\ \hline ABAC Integration with & & & \\ Blockchain & Explored theoretically & Partially implemented & Fully implemented \\ \hline Permissioned & & & \\ Blockchain Use & Proposed & \begin{tabular}{c} Used with limited \\ policies \\ \end{tabular} & \begin{tabular}{c} Used with \\ domain-specific policies \\ \end{tabular} \\ \hline Tokenization of Mineral & & & \\ Assets & Not addressed & Generic token examples & \
\begin{tabular}{c} Applied to mining \\ licenses \\ \end{tabular} \\ \hline Policy Dynamics & & & \\ (Revocation, Context) & Static or assumed & Limited automation &
\begin{tabular}{c} Fully dynamic and \\ contextual \\ \end{tabular} \\ \hline Auditability and & Not considered & Basic or missing & Fully supported \\ Structured Logging & & & \\ \hline Domain-Specific Design & & & \\ (Mining) & & & \\ \hline Enterprise Evaluation & & & \\ Metrics (e.g., latency, & & & \\ scalability) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative Summary of Blockchain-Based ABAC Approaches.
## 3 Intersection Delay Fairness Analysis
### Delay Model Determination  
Currently, the models used to calculate delay at signalized intersections mainly include the Webster delay model [3], the ARRB model [4], the HCM1985 model [5], and the HCM2000 model [6]. In estimating delay at low-saturation signalized intersections, these models are generally consistent because the latter three are derived from the Webster delay model and are more widely applicable. However, as saturation increases, the trend of consistency gradually weakens. When saturation is below 0.8, the relative error percentage of the average delay per vehicle obtained from the Webster model compared to simulation models falls within the range of 0 to 30% for over 95% of cases, significantly better than the other three delay models. Since this study focuses on low-saturation conditions during off-peak periods, the Webster model is chosen as the delay calculation model. The phase-average delay in the Webster model consists of three parts: uniform delay, random delay, and delay correction, as shown in Equation (13):  
\[d_{i}=\frac{C(1-\lambda_{i})^{2}}{2(1-\lambda_{i}x_{i})}+\frac{{x_{i}}^{2}}{2 q_{i}(1-x_{i})}-0.65(\frac{C}{q_{i}^{2}})^{\frac{1}{3}}{x_{i}}^{(2+5\lambda_{i})} \tag{13}\]  
In the equation, \(d_{i}\) represents the phase \(i\) average delay; \(\lambda_{i}\) represents the phase \(i\) green time ratio; \(C\) represents the signal cycle length; and \(q_{i}\) represents the flow of the phase \(i\). In the equation of \(\lambda_{i}=\frac{x_{i}}{x}\), \(x_{i}\) represents the phase \(i\) saturation and \(x\) represents the total intersection saturation.  
Since the last two terms in Equation (13) are much smaller compared to the first term, they can usually be ignored in analysis. Therefore, Equation (13) can be simplified to Equation (14):  
\[d_{i}=\frac{C(1-\lambda_{i})^{2}}{2(1-\lambda_{i}x_{i})} \tag{14}\]  
Then substituting \(\lambda_{i}=\frac{x_{i}}{x}\) into the above equation gives, as expressed in Equation (15):  
\[d_{i}=\frac{C(1-\frac{x_{i}}{x})^{2}}{2(1-\frac{x_{i}^{2}}{x})} \tag{15}\]  
Figure 4: Phase-average delay coefficient of four saturation rates.