esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Flexural Strength
(Equation (9)) relationships to predict the flexural strength of ambient cured fly-ash based GPC [48]. It was observed that their measured values were higher than the predicted values. However, the estimated values provided by AS 3600 (Equation (8)) were closer to their measured values than the values predicted by the other equations. [PERSON] and [PERSON] (2017) also proposed an empirical relationship (Equation (10)) based on their experimental test data to best fit their results, given by:  
\[f_{ct,f}=0.93\ \sqrt{f_{c}} \tag{10}\]  
In the present study, the flexural strengths of all GPC mixes were estimated by the relationships proposed by [PERSON] et al. (2011) (Equation (9)) and [PERSON] and [PERSON] (2017) (Equation (10)). The estimated values of flexural strength from these equations along with the test values from the present study are presented in Table 7 and Figure 15. Figure 16 shows the comparison of the measured and estimated values of flexural strength of GPC mixes specimens at the age of 28 days. Figure 15: The compressive strength variations of groups A and B mixes with molarity of NaOH solution. As shown in Figure 11, the expressions suggested by [PERSON] et al. (Equation (9)) and [PERSON] and [PERSON] (Equation (10)) have overestimated the flexural strength of ambient cured GPC mixes. The values predicted by [PERSON] et al. (2011) and [PERSON] and [PERSON] (2017) relationships were about 60% and 100% higher respectively than the measured values of this study. The mixing procedure, AAS content, and SS/SH ratio were almost the same in both the above studies. They have used low-calcium fly ash to produce the geopolymer concrete. However, the source material used to produce the geopolymer binder was different than the present study. [PERSON] and [PERSON] (2017) have used the ambient curing conditions; whereas, heat curing was adopted by [PERSON] et al. (2011). Hence, [PERSON] et al. (Equation (9)) and [PERSON] and [PERSON] (Equation (10)) relationships were proposed for heat cured and ambient cured fly ash based geopolymer concrete respectively. For the sake of comparison and to develop a more suitable relationship, these proposed equations were used to predict the flexural strength of ambient cured FS-GPC of the present study. Therefore, the marginal difference in the predicted and the measured values can be attributed to the difference in the source materials and the curing techniques. From the test results of this study, a more appropriate expression to estimate the flexural strength of ambient cured FS-GPC was proposed using regression analysis and given by:  
\[f_{ct,f}=0.25\;(f_{c})^{2/3} \tag{11}\]  
where, \(f_{ct}\) = characteristic flexural strength of concrete (MPa), and \(f_{c}\) = average compressive strength measured on cubes (MPa).
## 5 Case Study and Discussion  
### Experiment Parameters Setup  
According to the combined characteristics of passengers and their luggage, the simulation models of passengers and luggage were established in Pathfinder 2022 software. The model of passengers and their luggage is shown in Figure 5. In [45], the mass of passengers and their luggage was assumed to conform to the normal distribution. The mass of adult passengers ranged from 38 to 85 kg, with a mean of 58 kg and a standard deviation of 7.8 kg. The mass of luggage ranged from 5 to 25 kg, with a mean of 16 kg and a standard deviation of 2.3 kg. Then, we initialized the passenger mass and luggage mass to 58 kg and 16 kg, respectively. As shown in Figure 6, we used a blue circle with a green triangle to represent non-luggage-carrying passengers and their motion direction in a crowd and used a brown circle with a green triangle to represent luggage-laden passengers.  
The related parameters of the simulation and their values are set out in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Parameters** & **Value** \\ \hline \multirow{5}{*}{Non-luggage passenger particle} & Initial density \(\rho_{\text{sp}}\)(ped/m\({}^{2}\)) & Variable \\ \cline{2-3}  & Desire velocity \(v_{sp}^{0}(\rho)\)(m/s) & Variable \\ \cline{2-3}  & Initial velocity \(v_{sp}(\rho)\)(m/s) & 1.2 m/s \\ \cline{2-3}  & Particle radius \(r_{sp}\)(m) & 1.1 m \\ \cline{2-3}  & Particle mass \(m_{\text{sp}}\)(kg) & 64.08 \\ \hline \multirow{3}{*}{Contact} & Fraction coefficient \(\mu\) & 0.3 \\ \cline{2-3}  & Normal stiffness \(k_{n}\) & 1203.90 \\ \cline{1-1} \cline{2-3}  & Initial shear stiffness \(k_{s}\) & 920.65 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter setting in simulation.  
Figure 5: Passenger and luggage model in a simulation (Pathfinder 2022 software).  
Figure 6: Schematic illustration of particles in the simulation (Pathfinder 2022 software).
## 3 Results  
### Characteristics of UV-Vis Spectra  
Figure 2A-C shows the UV-Vis spectra of the olive oil dataset. Specifically, Figure 2A represents EVOO spectra according to origin; Figure 2B represents spectra according to different levels of adulteration; Figure 2C represents spectra according to different adulteration types. All spectra at the same level to each factor were averaged for presentation purposes. The olive oil samples have multiple absorption peaks in the visible light region. The absorption observed in this spectral region may be dominated by the oil pigments [23]. Specifically, there were three obvious absorption peaks in the 420-480 nm region that correspond to the absorption of blue light by olive oil, which may be mainly related to the carotenoids and chlorophyll contained in olive oil [24]. The peak appearing around 670 nm was also consistent with the absorption of chlorophyll [24]. Therefore, it is interesting to discover whether the pigment compositions can affect the UV-Vis fingerprints by influences of various factors.  
A preliminary study was carried out first by directly observing the original spectra to explore the influence of the three factors of EVOO origin, adulteration level, and adulterated type on the spectra. The spectra were plotted according to these three factors, with different colors designated in each class. Figure 2A shows the UV-Vis average spectrum drawn according to different EVOO origins. There are great differences between different EVOO originations and the biggest difference between A1 (Australia) and other origins. Figure 2B shows an average spectrum according to different adulteration levels. The color in the figure from lighter to darker indicates the increasing degree of adulteration. As the degree of adulteration increases, the absorption peaks of the average spectrum obtained gradually decrease accordingly. Figure 2C shows an average spectrum drawn according to different adulteration types. The difference between pure EVOO and adulterated EVOO is significant, while the differences between different types of adulteration are small. Since  
Figure 2: Mean UV-Vis spectral profile (**A–C**) and conventional PCA score plots (**D–F**) of adulterated olive oil according to different factors. (**A,D**) EVOO Origin; (**B,E**) adulteration Level; (**C,F**) adulteration Type. EVOO Origin included Spain, S1–S4; Italy, II–I3; Greece, G1; Portugal, P1; and Australia, A1. In (**A–C**), all spectra at the same level were averaged for presentation purposes.  
the preliminary observation implied the influences of all factors, it is difficult to obtain individual influences directly. The subsequent chemometric methods will be used to further analyze the three factors.
# A Comprehensive Review of In-Body Biomedical Antennas: Design, Challenges and Applications
## 4 Challenges That Influence the Design of In-Body Antennas
### Effective Isotropic Radiated Power (EIRP)  
A remarkable level of EIRP of the in-body antenna can be harmful to the biological tissues, and it can create interference with the nearby radio devices. The standardized limit of EIRP for an in-body antenna functioning in the MedRadio band is \(-16\) dBm [88] and \(-36\) dBm at 915 MHz for the ISM band [89]. In case the in-body antenna is used for data telemetry, the input power must be limited to alleviate damage to the tissues. If the in-body antenna is operating as a receiver, the external source of power must follow the aforementioned standards.
## 2 Materials and Methods
### Model Development and Implementation
#### 2.2.2 Particle Swarm Optimization  
Particle swarm optimization (PSO) is a computational technique that optimizes a stochastic or non-linear problem by iteratively improving a candidate solution relative to a defined fitness function. It was first presented by [PERSON] and [PERSON] in 1995 and simulates social behavior like the movement of organisms in a bird flock or fish school [30]. The approach helps solve a numerical problem by generating candidate solutions called particles and moving them around in the search space until the optimal solution is attained. The movement of each particle is guided by its knowledge of its personal best or local best  
Figure 3: Typical ANFIS model architecture. Each colour represents different stages in the ANFIS layers. Adapted from [29].  
position and the global best position of the swarm or population. In this way, the swarm moves towards the best solution. The flowchart of the method is shown in Figure 4.  
The velocity and the position of a particle are updated as follows [31]:  
\[\mathrm{v_{i}(t+1)=v_{i}(t)+C_{1}r_{1}(t)[x_{\mathrm{Pbest,i}}(t)-x_{i}(t)]+C_{2}r _{2}(t)[x_{\mathrm{Gbest,i}}(t)-x_{i}(t)] \tag{8}\]  
\[x_{i}(t+1)=x_{i}(t)+v_{i}(t+1) \tag{9}\]  
where \(x_{i}(t)\) and \(x_{i}(t+1)\) are the particles' positions in the search space at time step \(t\), and \(t+1\). \(v_{i}(t)\) and \(v_{i}(t+1)\) are the velocity vectors of particle \(i\) at time step \(t\) and \(t+1\). \(x_{\mathrm{Pbest,i}}(t)\) and \(x_{\mathrm{Gbest,i}}(t)\) are the personal (local) and global best positions of particle \(i\). \(C_{1}\) and \(C_{2}\) are the acceleration factors. \(r_{1}(t)\) and \(r_{2}(t)\) are random numbers between 0 and 1.
# Synchronization, Control and Data Assimilation of the Lorenz System
## 2 Master-Slave Synchronization
### Intermittent Synchronization  
In real applications, it is generally impossible to obtain a signal from one system and inject it into the replica in a time-continuous way. Pecora and Carrol were able to achieve it using electronic circuits Pecora and Carrol (2014), but if one needs to pass through a measurement system, it is expected that this device has a finite bandwidth, i.e., a finite measurement time.  
So, the question becomes: is it possible to synchronize two replicas by measuring one quantity only at time intervals \(\tau\)?  
Numerically, if the equations are integrated using a constant time step \(\Delta t\), this means that the replacement or mixing of components is applied every \(k\) time steps, so that \(\tau=k\Delta t\).  
For homogeneous coupling (\(\mathbf{C}=[1,1,1]\)), the linear analysis simply tells us that (Equation (7))  
\[p_{c}=1-\exp(-\lambda_{\text{MAX}}\Delta t\cdot k)=1-\exp(-\lambda_{\text{MAX} }\tau), \tag{20}\]  
i.e., intermittent synchronization is equivalent to standard synchronization for a system with a larger Lyapunov exponent.  
Then, for a small \(\tau\), we have  
\[p_{c}=\lambda_{\text{MAX}}\tau. \tag{21}\]  
This relationship also holds numerically for other coupling directions, as shown in Figure 3. In the figure, we also plot the linear fit obtained using the first 20 points.  
The estimated values obtained from the linear fit (\(\lambda_{\text{fit}}\)), compared with the values (\(\lambda_{\text{lin}}\)) computed using Equation (21), with \(p_{c}\) estimated numerically (Figure 2), are summarized in Table 1.  
Figure 3: The dependence of the synchronization threshold on the intermittent parameter \(k\) such that \(\tau=k\Delta t\) for different coupling directions and its linear fit obtained using the first 20 time steps. The other parameters of the simulation are: \(dt=10^{-3}\) and \(k_{max}=50\) for \(\mathbf{C}=[1,0,0]\), \(k_{max}=100\) for the others.
## 4 VRFormer
### Training and Validation Procedures
#### 4.7.2 Hyperparameter Configuration and Training Augmentation  
The VRFormer model's training within VRDeepSafety was rigorously configured with specific hyperparameters and data augmentation techniques to achieve optimal performance, efficiency, and robustness.  
Hyperparameter Optimization  
In this step, the model was trained using a batch size of 32 across 50 epochs. These values were carefully selected to balance training time and the model's convergence capabilities. Larger batch sizes were found to lead to memory issues, while smaller batch sizes resulted in prolonged training times without any significant performance gains. The Adam optimizer, known for its efficiency in handling sparse gradients commonly encountered in deep learning models with numerous parameters, was chosen for this model. Its parameters were set as follows: \(\beta_{1}=0.9,\beta_{2}=0.999,e=10^{-8}\), with a learning rate of 0.001. These specific settings were determined to be the most effective in achieving efficient optimization of the network weights, ensuring efficient training convergence.  
Hyperparameter tuning is crucial for achieving peak model performance. Within VRDeepSafety, this process is conducted using a combination of manual and automated techniques. Initial hyperparameter values, such as batch size, the number of epochs, optimizer learning rate, and loss function weights, are selected based on preliminary empirical tests. Following this, an automated grid-search methodology is applied to explore specific ranges around these initial values, with the goal of optimizing key parameters, such as the loss function weights and batch size. Furthermore, we employed a Bayesian optimization methodology to fine-tune the most impactful hyperparameters, including the learning rate, dropout rates, and layer sizes. Throughout the tuning process, the validation set is used to evaluate the model after each epoch, and the final hyperparameters are chosen based on the best performance observed on the validation set. Using this approach, we determined the optimal values to be a batch size of approximately 32, a training duration of approximately 50 epochs, and a learning rate of 0.001.  
The loss function employed in the model training was a weighted combination of several component losses, each playing a key role in the comprehensive learning of the model. Cross-entropy loss, used for classification tasks, was assigned a weight of 1.0; smooth L1 loss, applied for bounding box regression, was set to 0.5; mean squared error (MSE), used for trajectory prediction, was assigned a weight of 0.3; and binary cross-entropy (BCE), used for collision risk assessment, had a weight of 0.2. These specific weights were determined empirically, through a detailed set of experiments using the validation set, enabling the model to effectively learn across the diverse training objectives, as each loss function had a role in model performance.
# Efficient Mobile Vehicle Data Sharing Scheme Based on Consortium Blockchain
## 2 System Model  
### System Model  
The data sharing system model of IoV based on the consortium blockchain is shown in Figure 1, which includes trusted authority (TA), moving vehicle (i.e., data seller), RSU, and data demand entity (i.e., data buyer) [12]. TA is mainly responsible for the registration and certification of entities, and a special government department is responsible for TA authority and security. Any IoV entity that wants to be part of the sharing system (such as mobile vehicles, traffic dispatching centers, autonomous driving companies) should first register and obtain the TA's access permission to join the network through issuance of a digital certificate.  
Normally, RSU performs as data agent to respond to requests for data purchases and sales in the IoV. As shown in Figure 1, RSU is one of the consensus nodes of the federated blockchain; the data sharing smart contract (DSSC) is used to control data sharing. The communication between RSUs is referred to as R2R, which is mainly cable connection. The data buyer to RSU communication is referred to as B2R, and it can also be connected by wired connection. The mobile vehicle to RSU communication is referred to as V2R, which is completed by satellite, wireless communication network, 4G/5G or other technologies [13; 14]. Moving vehicles covered within a network area are called data sellers. The data seller registers an account with TA, and this account can be a non-real name, so as to protect the privacy of the vehicle owner. After account registration is completed, the wallet function needs to be activated to store resource coins. The mobile vehicle communicates with the RSU through the onboard unit (OBU). Moreover, the data demand entity can be an enterprise or an institution, such as an autonomous driving company, a traffic control center, etc. It plays the data buyer role in the entire framework. Data originating from moving vehicles are obtained using the scheduling of sart contracts deployed on the RSU. In addition, these data are used for a fee, paid through resource coins.
# A Review on Data-Driven Condition Monitoring of Industrial Equipment
## 2 Data-Driven Condition Monitoring of Industrial Equipment
### Fault Diagnosis and Prediction
#### 2.2.1 Method Based on Shallow Machine Learning
Multiple wavelet auto-encoders were utilized to create a depth wavelet autoencoder that efficiently captures the vibration signal's features. The recorded features are then fed into an ELM model to identify various bearing faults. The experimental results demonstrated that the suggested technique achieves over 95% accuracy, which is much greater than classic ELM, wavelet neural network, BP neural network, and SVM. The relevant references for the ELM methods discussed in this section are summarized in Table 16.
## 2 System Model
### Computation Model
#### 2.4.2 Edge Execution  
We assume that, due to the limited resources, the SUE \(k\) is not able to accomplish the computing task within the time frame of \(t_{k}^{max}\), whereas the GUE needs to offload the tasks for computation. In both cases, the agent \(l\) rigorously analyzes the task and takes the initiative to execute the task at the edge (\(s_{k}^{edge}=1\)) aiming to achieve extremely low latency. Besides the mode selection, the agent \(l\) is also accountable for choosing the optimal E-AP based on the UE demand. In the case of offloading the task to the edge, the total delay specifically involves the computation delay and uplink transmission delay [1]. The cumulative latency experienced at the edge is expressed as follows:  
\[D_{k}^{edge}=\frac{M_{k}}{R_{k,l}}+\frac{C_{k}}{f_{l,k}^{edge}}, \tag{9}\]  
where \(f_{l,k}^{edge}\) is the amount of computation resources that the E-AP \(l\) assigns to UE \(k\) and \(R_{k,l}\) stands for the attainable transmission rate of UE \(k\) for accessing the E-AP \(l\). The corresponding energy consumption due to transmission is expressed as:  
\[E_{k}^{edge}=p_{k,l}\frac{M_{k}}{R_{k,l}} \tag{10}\]
# Renewable Energy-Based Energy-Efficient Off-Grid Base Stations for Heterogeneous Network
###### Abstract  
The heterogeneous network (HetNet) is a specified cellular platform to tackle the rapidly growing anticipated data traffic. From a communications perspective, data loads can be mapped to energy loads that are generally placed on the operator networks. Meanwhile, renewable energy-aided networks offer to curtailed fossil fuel consumption, so to reduce the environmental pollution. This paper proposes a renewable energy based power supply architecture for the off-grid HetNet using a novel energy sharing model. Solar photovoltaics (PV) along with sufficient energy storage devices are used for each macro, micro, pico, or femto base station (BS). Additionally, a biomass generator (BG) is used for macro and micro BSs. The collocated macro and micro BSs are connected through end-to-end resistive lines. A novel-weighted proportional-fair resource-scheduling algorithm with sleep mechanisms is proposed for non-real time (NRT) applications by trading-off the power consumption and communication delays. Furthermore, the proposed algorithm with an extended discontinuous reception (eDRX) and power saving mode (PSM) for narrowband internet of things (IoT) applications extends the battery lifetime for IoT devices. HOMER optimization software is used to perform optimal system architecture, economic, and carbon footprint analyses while the Monte-Carlo simulation tool is used for evaluating the throughput and energy efficiency performances. The proposed algorithms are validated through the practical data of the rural areas of Bangladesh from which it is evident that the proposed power supply architecture is energy-efficient, cost-effective, reliable, and eco-friendly.  
heterogeneous network; renewable energy; internet of things; optimal power systems +
Footnote †: journal: _Energies_  
0000-0001-9900-0001-9900-0000]M. [PERSON]
### Contribution and Paper Organization  
By proposing the use of RF transceivers as replacements for DC PLC modems in short wired networks, such as those in Li-ion battery mobile platforms, we can benefit from the wide availability of various signal modulations and data rates on robust high sensitivity systems, instead of dealing with the limitations of modem technology. Furthermore, since the wired network can function as an antenna-leaky cable, physical damage to connections may be overlooked as hybrid wired/wireless functionality, which could lead to greater opportunities for analysis and diagnostics rather than investigating the causes of the loss of connectivity. Since measurement redundancy ensures safety and prevents failure or drift over time, the research presented in this paper delivers an important contribution to BMS research by illustrating the possibility of using the RF transceivers for wired PLC, whereas they are typically associated with wireless communication. With this presented application, future smart cells equipped with RF transceivers may be able to provide hybrid BMS data services as well as diagnose fault connections within cell networks.  
The work presented in this article is organized in four main sections. As the Introduction explains the background and motivation for the study, the second section covers the methodology employed in the investigation; the experimental testing and pre-sets are prefaced by a simulation, which delivers the complementary data regarding the network equivalent circuit as a system reacting to tailored input resulting from priory measurements. A discussion is provided about the potential implications of data variation in section three, based on simulations and empirical results. The final section summarizes the findings and highlights the potential benefits that may result from pursuing the avenues newly opened by this study.